\r\n\tb) how a concentrated attention focus on screens (i.e., tablets and smartphones) could result in a total activity absorption and a flow experience; \r\n\tc) teens' preference for media social interaction appears to be closely associated with impaired modes of mood regulation; \r\n\td) the web activities as factors of externalized and/or internalized risks; \r\n\te) the implementation of health promotion interventions by Internet Apps; finally, \r\n\tf) the cross-cultural differences and similarities about teen approaches to the web around the world.
\r\n
\r\n\tThis book intends to provide the reader with an overview of studies with a research topic that is crucial today: the need to integrate teens' use of the web into the processes contributing to determine adolescents' developmental trajectories and Quality of Life.
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1. Introduction
“Leadership is about character and substance” [1].
The decades of the twenty-first century is tinted with a long list of horrendous scams in the field of corporate, politics, spiritual or otherwise that could be traced back to the judgment made by ambitious people in positions of authority. Such cases raise questions on the very intent and content of leaders and victims wonder whether the failures were intentional or the upshot of incompetent big-headed and reckless leaders. Toxicity in leadership has been running in nerves of the organizations and in societies from their inception.
In some point in our professional careers, we have experienced choking situations which heave the stress and anxieties leaving us with low self-worth. Then we try to locate reasons for our problems and finally the blame is shared between circumstances and the environment we work in, but hang on, is it actually only the result of toxic environment? Maybe we are so honey trapped by some toxic leaders who by virtue of their personality and style leave us in a worse-off situation than where they found us. They are venomous and bad to the bones of the organizations. What contaminated that environment? A recent report by workforce consulting firm “Life Meets Work” [2] claims that 56% of employees endure a toxic leader and his venomous behaviors leading to an obnoxious environment. Another research [3] by psychologist Nathan Brooks and Dr. Katarina Fritzon of Bond University and Dr. Simon Croom of the University of San Diego claims that around one in five bosses are found psychopaths in the upper echelons of the corporate world. That is a scary figure, which surely reveals that the problem of toxicity is so prevalent in the corporate corridors which gradually decay their subordinates’ morale, motivation and self-esteem.
For many of us we are no stranger to such situations directly or indirectly. However, still we wonder how we get trapped and how we were mistaken about the style of our leader. Media reports are full of numerous cover stories of corporate scandal or political scams unveiling the leaders that violated public trust.
Let us begin with a small case of Mr. Shetty, a revenue breeding executive director in an IT firm. He was not the easiest person to deal with. Although he had many awards and recognitions appreciating his excellent technical qualities and industry knowledge, his unpredictable behavior and culture of fear got on to people’s nerves. Shetty was manipulative, unethical, had angry outbursts, and critical about almost everything that others had done. He never shared credit with teams and was always involved in some sought of vengeance, compelling people to adhere to his instructions. Does this sound familiar? Beware, you may find lots of situational similarities within the literature; just fasten your seat belt to start a journey of exploring this lethal style of leadership.
2. Mapping the origins
The existence of the dark side of leadership could be traced back to the human civilization, but leadership as a concept has always been a synonym of positivity. Up till now, very few researches have really explored the dark side of leadership. Military has been the major research area for the construct until last decade. Even though this concept has been evolving, it is still indistinct. Certainly, authors do not convey an understandable picture of it and label this dark side differently: destructive [4], bad [5], evil [6], charismatic [7], narcissist [8], aversive [9], bullying [10], abusive [11] and toxic [12, 13]. The origin of the construct could be traced from the progress of research of the similar concepts of dark leadership.
Toxicity is acutely sniping. Toxic derives from Greek mythology: toxicus means “poison.” Dr. Marcia Lynn Whicker was the first to link toxicity with leadership and discussed in her research three types of leaders within workplaces: “trustworthy (green light), the transitional (yellow light), and the toxic (red light).”
The repertoire of toxic leaders covers a broad spectrum; it depends more not only on what they really are but also on how people perceive them. For some, they might be toxic and for some a charismatic hero [12]. It is quite difficult to craft a differentiation between destructive leaders that are genuinely toxic, bad leaders that are not toxic but are incompetent only in managerial skills and leaders with mental disorders and good leaders that are wicked people. The present state of research in the related concepts of dark leadership could be drawn together in the form of Figure 1. It clearly defines toxic leadership as an umbrella term including all other dark leadership constructs.
Figure 1.
Illustration on the research of the related constructs under the gamut of toxic leadership. Source: adapted from Thoroughgood et al. [14].
Much earlier, Reed [15] enquired the symptoms whether subordinates feel humiliated or de-energized after interpersonal exchange and whether the less powerful are victimized more than the powerful? Study justified both the symptoms of followers of toxic leadership. The approach was phrased as the “kiss up and kick down tendency,” where the toxic leader presents himself as a responsible and responsive follower to his superiors but acts miserably to his own subordinates.
Lipman-Blumen [12] analyzed toxic leadership as having serious outcomes in the long run rather than in the short term. Their strong personality may mask their deliberate ill intentions in the short term but have detrimental after-effects on individual and organization in the long run. Identifying a toxic leader is not a cake walk. One must evaluate the consequences, rather than the transitional effects of the leader’s influence on the follower. Recent studies by Mehta and Maheshwari [16] and Singh et al. [17] also reaffirm these thoughts.
An extensive literature available does not put toxic leadership in the normal category of impaired mental health, evil intentions or casual mismanagement. However, it could be construed as maliciously intended leadership behaviors that spitefully burn down efficiency and enthusiasm of the subordinate in inevitable ways.
Our empirically doctoral research on the dimensions of toxic leadership in Indian IT professionals deciphered the construct as a multidimensional construct. It could be described as “those narcissist, self-promoting leaders who by their derisive supervision, managerial incompetency and erratic behaviors intentionally tend to erode their self-esteem, burn out their employees, breed counterproductive performing subordinates and future overbearing bosses.” Toxic leaders’ authoritative and abusive methods not only present long-term risk for the organization but also trickledown to the society and the nation.
3. Behaviors and qualities that make a leader toxic
Once we are able to define and distinguish the construct of toxic leadership, our wary minds would ask for the behaviors and qualities of toxic leaders. There is overlap of the bad qualities in toxic leadership from various dark leaderships. Even though most of the behaviours hold true to a toxic leader as well, but an exception that they are excellent masquerades which shadows their ill intentions beautifully. A wolf in sheep’s clothing, one may find him extremely helpful who, charmingly contravenes basic standards of human rights by consciously reframing toxic agendas as noble endeavors.
While you comprehend these traits of toxic leaders, you will be definitely able to mirror few reasons behind your stressful work life. As recently suggested by Work Life consulting survey [2], 73% employees agree to having worked under a toxic boss leading to a traumatic work experience. Toxic leaders like to be aggressive toward their subordinates, be critical of them, blame them and try to intimidate them. Their actions are always dedicated to personal interest. They never renounce promoting self over the vision, mission of the organization and, worst of all, the interest of followers. Toxic leadership is evident when leaders demonstrate aggression toward their employees’ personalities and abilities [18]. A leader is considered toxic if he/she creates serious long-term harm to their employees [19].
Unfortunately, some leaders allow their current moods to create the climate of their organization, as illustrated in Green’s study [20]. Common characteristics found amongst such leaders include, but are not limited to, ethical failure, incompetence and neurosis. Furthermore, Mahlangu (as cited in Sasso [21] stated that there is a plethora of negative effects that toxic leadership has on teaching as well as learning in schools. These include intolerable working relationships amongst stakeholders, which in turn produced a multitude of negative effects.
While contrasting old and new leadership, Green [22] emphasized the transition of effective leadership from a reliance on power to a reliance on trust. Without risk, learning cannot happen. Without trust, risks become a rarity. Toxic leaders can be self-destructive sometimes because they lack interpersonal skills and emotional intelligence. Believe it or not, they are hoarders as well. They dig out information, resources and tasks to their subordinates in order to maintain tight-fisted control on them. Their modus operandi is culture of fear. The subordinates are threatened with negative consequences which seems interesting sometimes as a direct and easy technique to achieve the task but infuse toxicity to the organizational climate.
Authoritarianism is the favorite terminology in a toxic leaders’ dictionary. They do not care about the learning of a subordinate or team building, instead at every given opportunity, they denigrate them and act as if the subordinate is disposable and nothing more than a tool for them to use. Forget about the subordinate’s view point, even their agreement to official decisions has also no relevance as they are bound to follow a toxic leader’s decision. They like to micro-manage.
Kellerman [5] in his study placed the bad leader’s behavior on a band ranging from ineffective/incompetent to unethical/evil. When they embrace authority, toxic leaders—those who enjoy bullying others with their abusive behaviors and command total control—can be distinctly effective. However, regardless of some short-term benefits, toxic leaders bear out to be highly malicious and jeopardize the organizational success and sustainability, reveal many latest researches.
A toxic leader appears like a negative ninja who finds pleasure in turning every other thing and situations to a negative one. Such leaders are always finding ways to inflate the dark aspect of any situation or project, dragging down the morale and enthusiasm of their subordinates. For them counterproductive performance holds even greater credence than other positive performance while rating their overall performance.
One of the most damaging types of abuse is the marginalization of employees over non-merit factors or feelings of jealousy for those who have developed more advanced levels of critical thinking, and are viewed as threats to those in current leadership positions. “If work is perceived as a zero-sum game of winners and losers, then toxic leadership is a sensible strategy for presenting oneself as a winner,” states Kenneth Matos, the vice president of research for Life Meets Work. “However, if an organization depends on long-term collaborative work to succeed, toxicity advances the leader at the expense of the organization.”
4. What stimulates a leader’s toxicity?
The overall literature available highlights one important aspect of toxic leadership and that is, in leadership study the focus is often on individual leaders rather than on the process assimilating both follower and the context. Although we need to probe individual antecedents before devising an antidote for toxicity in organizations, these behaviors do not happen in isolation. A look into the past of toxic leaders shows that they do not develop toxic tendencies in a day; in fact, their style evolved over a period of time. By now you must have realized the difference between toxic leader and toxic leadership. Click your refresh button and recollect that toxic leader is a person with dark and destructive personality traits but in order for toxic leadership to thrive, other conditions need to be met as well. According to the Padilla [4], “negative organizational outcomes are not only the product of dysfunctional leader behaviors but also susceptible followers and the contributing environment in which they interact.” He termed it as, “toxic triangle.” The three components of the toxic triangle and their interaction with each other determine the intensity of toxicity existing in the organization. Authors like Uhl-Bien et al. [23] have also held toxic triangle responsible for the germination of toxicity in a leader. With no further detailing, look for Figure 2 to understand it.
Figure 2.
Toxic triangle. Source: Padilla et al. [4].
It indicates two kinds of a subordinate’s contribution in the toxic triangle. A “colluder” happily follows toxic leaders because of similar worldviews and high ambitions whereas the “Conformers” adhere to such leaders to avoid incongruities for risk of reprisal.
From King Henry VIII to Jeff Skilling, the history of politics and enterprise is crammed with toxic leaders who emerged as slow poison for their organization. Blaming immoral leaders for their foibles is easy, although a toxic boss is difficult to find without their followers’ compliance.
Prof. Lipman-Blumen [12] made an interesting point in describing the reason why we still continue to follow such destructive leaders. We not only tolerate but also even prefer and create a toxic leader. We always look up to some god-like figure human or divine to take care of us, to create an illusion that we are the heroes and that we are amongst the chosen ones. We can be at the center of the action and thus inspired to join them. These illusions are the conduits through which toxic leaders reach in and grab us.
There are also some psychological reasons that make us vulnerable to toxic leaders. We want immortality and to live forever, if not physically, then symbolically. So, if a leader can promise us that by joining his so-called noble vision or that imperative cause then we will be doing something that will be memorable and engrave our name in history.
One of the common reasons identified for the emergence of toxic leadership is perceived threat to the status, power and controls that may prompt toxic behavior in vulnerable leaders to sprout. The ambition to attain power and authority could also become an addiction for some leaders. Their personal agendas gain priority over the long-term welfare of the organization [24]. Impatient and grouchy leaders who are always on the verge of anxiety at workplace could also breed toxicity. These leaders develop a habit of throwing temper tantrums, often erratic behavior, shout, use abusive language, demean employees openly and make unreasonable demands.
The unquestioned supremacy can also be held responsible for sprouting toxicity in some rigid leaders ascending the organizational power ladder. The higher they ascend, the stronger is the impact and influence of their behavior. At such power positions, their inflated egos make them intolerable to others’ views and ideas and are unpredictable. This, together with an obstinate narcissist personality, could make them ignorant of their own attitude as well as the behavior that leads to dysfunctional outcomes to those around them.
The bottleneck competitive corporate corridors sometimes turn into breeding ground for toxicity. Few dark leadership traits meddle with leaders’ effectiveness in maintaining high-performing teams and efficiency in generating soaring bottom lines for organizations. Pressure is created on leaders for a profitable transformation in the organization. As exemplified by researchers, an extremely competitive environment tends to escalate the stakeholder pressures on corrupt behavior and justifies the emergence of the dark side of leadership. Lipman-Blumen [12] thus concluded that organizations could also become an incubator of toxic behavior, through counterproductive policies and practices, including unreasonable goals, excessive internal competition and cultures that encourage blame game. Thus, virulent strain by toxic leaders tax heavily on the creativity, innovativeness enthusiasm and sovereignty of the people around them aiming at the fulfillment of only the leader’s interest.
5. After effects of toxic leadership
Difficult people flout rules and logic. Some are blissfully unaware of the negative impact that they have on those around them, and others seem to derive satisfaction from creating chaos and pushing other people’s buttons. Either way, they create unnecessary complexity, strife and worst of all stress, sometimes compelling few to participate in counterproductive acts and others surrender to conform with the unethical, malicious acts.
A rational way to check the severity of toxicity in an organization is to estimate the dent on the culture of the organization. Statistical figure held toxic leadership responsible for 48% decrease in work effort and 38% in work quality. Another survey in 2017 by Life Meets Work consulting revealed some scary number as large as 73% turnover due to a toxic leader. It is reaffirmed time and again through various researches that the harmful after effects of toxicity may or may not seem prominent in short time but widens the dangerous ditch gradually over a period of time, claiming the very foundation of the organization. Such leaders mostly top the charisma list, therefore making it difficult to confirm their toxicity, which gradually surfaces with time. Perceived toxicity is individual specific; thus, a toxic leader for one may be a hero to another.
The reality of physical and psychological damage to the vicinity of destructive leadership has been proved empirically and theoretically through many research studies in the past. The penalties at both subordinate and organizational levels are estimated by souring figures of counterproductive work behavior and employee deviance working under the aegis of toxic supervisor.
Webster [25] included reduced employee satisfaction and commitment reason for augmented employee turnover. Organizational cynicism is fueled by toxic leaders. Aloof and distant autocratic managers, who prefer self-promotion and impress upper-level management, contribute immensely to ruin the organizational culture and its human assets. The sycophant approach to leadership and management is a clever con game causing extensive damage that stagnates performance and morale within the organization.
As Ross et al. [26] indicated, toxic leadership takes a toll on both the mental and physical health of employees, in addition to an increase in counterproductive work behavior, coming to work late, resignation, or transfers.
An array of detrimental effect of toxic leadership has been discussed by few studies [5, 27, 28]. At an individual level, the effects are more prominent and deep. On top of the stack is decreased self-esteem and self-insight which raises their doubt on self-capabilities leaving them with feelings of low self-worth. Consequently, some psychological reactions are but obvious including sense of threat, distress or sense of betrayal, a sense of mistreatment and lower motivation, helplessness and burnout compelling them to voluntary quitting. A number of survey reports that about 90% of all hospital visits are majorly stress and related problems like that of heart diseases and if persistently exposed to stress, lethal diseases as cancer. An empirical study by Yen [29] affirms that an organization stuck with toxicity may appear normal and progressive externally but the inside story is alarming and full of chaos.
If such toxic behavior trickles down the organization through the culture, the so-called leader with no true leadership qualities would be the main contributor to the crisis. A person who is incompetent in his leadership role seizes support from culture of fear and chaos to control and bully his subordinates. This type of sadistic philosophy, aids such pretending pirates to endorse lack of knowledge of professional directions creating the dependency of subordinates for all the professional answers and directions. This control tactic creates great crisis in the organization.
Feeling of helplessness, no opportunity for participation in innovation, no professional sovereignty, abridged efficiency, lower job satisfaction, job insecurity leading to an array of psychological and emotional problems such as anxiety, depression and frustration are few amongst the deleterious effects a toxic leader have on individuals. This may not always be silent or solitary. The abused employee’s tit-for-tat approach may persuade them to retaliate their supervisor’s exploitation through aggressive and counterproductive work behavior. It will be mediated by a distorted leader-member exchange further soaring the turnover intentions of the dejected employee, confirms a doctoral dissertation on the subject by Singh [30].
According to the theory of displaced aggressions [31], employees often tend to take out their anger on the organization, their subordinate and the colleagues when they cannot openly retaliate against the supervisor. The impact of power difference and their inability to face the abuser generates hatred and resentment, which finally affects the organization.
Toxic leaders tempt people to choose sides, although they have no other alternative than to comply with his orders than to perish from the system. They rule the system, so they oblige employees to “join his army or be ready to face it.” Consequently, people plan to leave, raising the turnover rate in organizations. This puts extra financial burden on organizations for recruitment and training of new employees. Not to mention the inexplicable talent drain they face. But the remaining intrepid hovers are also not good news for the organization. Those are either conformers or colluders who might not be loyal and committed to organizational goals and success.
Black [32] recently in his research concluded, “the experience of a toxic leadership is an institutional cancer with the high-propensity to metastasize, leaving destruction, poison, and scars in its path and beyond.” It is an established fact now that the influence of toxic leadership is severely damaging and its effects are far beyond the subordinates, project goals and organization. It percolates deep into the very roots of society jeopardizing growth of a progressive nation.
6. Common myths about toxic behavior
When the captain of a ship feels contented and inspired, he/she encourages his/her employees to take risk and innovate creating a blissful and engaging work environment. After going through pages-long discussion about such menacing form of leadership, few matters of concern surface from the corporate world. Working professionals breed certain myths about the toxic leaders. Few could be listed as below:
Myth 1: We would easily identify if there is someone toxic in our team.
Really!! Can we? On a second thought “No,” it is quite a challenging task. Such people are quite charismatic, witty and proficient in masking the toxicity for their advantage. Thus, it is sometimes not easy to make out from their overt behavior until you have spent quite long time in industry.
Myth 2: If their behavior continues immoral and ruthless, subordinates would not accommodate them.
Ahh! Is it possible always? Such critical pressure in corporate world leave subordinates with no choices. Owing to their pragmatic needs, insecurity and lack of courage, subordinates sometimes willingly or unwillingly put up with bad leaders. Their allure does not allow subordinates to doubt his means to ends. Toxic leaders generally display enormous energy levels and are able to overcome exigent circumstances and obstacles with effortless ease. His unethical means are overshadowed by the successful end of the task.
Myth 3: We cannot eliminate such people from our system, they are profit makers.
Surely we can, with little timely and vigilant actions. While toxic leaders’ need for recognition and power propel them to adopt any unethical means to attain professional targets successfully and gather accolades from top management, later it affects the bottom line through the brain drain of high-performing human assets of the organization. This is because; toxic leaders are self-destructive as well. Their stumpy interpersonal trait fails to reap rewarding performances and team spirit.
Myth 4: Toxicity in leader is a short cut to ascend success ladder and achieve a rewarding corporate career.
Ironically, sometimes it syncs in the highly competitive corporate corridors. But, as it is said, there are no shortcuts to success. Toxicity is not long lasting and has enduring harm to the individual as well as to the organization. Like a slow poison, it not only ruins the veins where it runs but also the whole body of the organization. The thin line between a transformational and toxic leadership should be dealt cautiously. The tempting short-term gains could not be claimed over the long-term ethical professionalism and leadership gained otherwise.
Myth 5: Bad leaders cannot be dealt individually.
Forlornly, it is not completely untrue. Shared efforts from both management and subordinates would expedient the counter process. But, yes, a whistle-blower is enough to get him identified in the system. First individual approach and then systems approach will be highly effective to curb toxic leaders from contaminating the organization.
Myth 6: A toxic person is a prerequisite to deal with another toxic manager.
Not necessary. Reed said. “We seem to have a band of tolerance for certain leadership styles that are not positively impacting our organization, and that could be the crux of the problem.” A senior manager could point out and discuss the toxic behavior with them and make them realize as it is challenging for them to self-realize their mistakes. An antidote could come from any source before the trickling effect of toxicity starts endangering the organization.
7. Let us ascertain a ray of hope: The positivity in gray areas
One of the most fascinating findings of a recent study by Life Meets Work revealed that 68% of employees working for an over-demanding, self-promoting and self-centered boss are highly engaged, compared to just 35% of workers reporting to nontoxic leaders. In addition, employees working for a toxic leader stay working for those bosses for an average of 7 years, compared to just 5years for employees who work for someone less demanding.
Even if a leader seems positive, there could be issues causing chaos within the organizational structure to include personality differences based on many philosophies of leadership or not as no one individual is a demigod. No leader is infallible, but it would be great to empower their followers.
While a number of studies have highlighted the negative effects that various dark side traits can have, some researchers have pointed out that there are times that these dark side traits can have “bright side” consequences. [33, 34]. It was established that certain dysfunctional personality styles correlated with leadership and effective leadership behaviors. Some researchers also discussed four possible implications for leader emergence and leadership effectiveness of traits as shown in Table 1.
Narcissistic individuals are typified by self-absorption, self-serving behaviors and aggression. They maintain exaggerated views of their own self-worth, but these behavioral traits sometimes have some positive associations in the leadership process. In an empirical study of 300 military cadets, the best rated leaders were those who were high in egotism and self-esteem, two positive aspects of a narcissistic personality [35]. Study has shown that to condense ego threatening conflicts, narcissistic leaders may adapt their interpersonal interactions for positive impressions on the people they want to control [36]. Moreover, narcissistic leaders favor aggressive, gallant and magnanimous actions which will uplift their image as a leader. This in turn acts as an advantage for subordinates and organizational performance.
Machiavellianism: The term is coined after Machiavelli’s famous book “the Prince” describing dark traits of individual [37]. Machiavellianism is used to describe individuals who are manipulative or cunning, with a strong need for power [38]. They tend to have high motivation to lead and are often distinguished as charismatic with willingness to empower their own social capital for the sake of accomplishment of their group goals.
Hubris: Hubris are people with excessive pride and self-confidence who socially play on impression management. In a leadership position, they are likely to project power, strength and authority in difficult situations, stimulating confidence amongst their group and peers. Indeed, hubristic leaders are more confident and committed in their tasks, support innovation [39] and test the limits of their organization’s productive capacity.
Social dominance: The literature reveals that the people who get high scores on ratings of dominance are the most preferred and suited for the authority and leadership positions. They display a strong desire for achievement and control [40], making them attractive to enthusiastic followers.
Social desirability
Actual effects in specific context or situation
Bright
Dark
Bright
Socially desirable trait has positive implications for leaders and stakeholders. Example: Conscientious leader displays high ethical standards in pursuing agenda in long-term interest of organization.
Socially desirable trait has negative implications for leaders and stakeholders. Example: Self-confident (high CSE) leader pursues risky course of action built on overly optimistic assumptions.
Dark
Socially undesirable trait has positive implications for leaders and stakeholders. Example: Dominant leader takes control of ambiguous situation and assumes responsibility for the outcome.
Socially undesirable trait has negative implications for leaders and stakeholders. Example: Narcissistic leader manipulates stock price to coincide with exercise of personal stock options.
Table 1.
Framework for discussion of implications of personality traits for leader effectiveness.
While discussing the dark side of leadership, a prominent fact that could not be ignored was that the so-called “bright side” can also have damaging outcomes for organizations and subordinates when taken to the extreme.
To discuss a few, highly conscientious leaders tend to be disciplined, cautious, inflexible, highly critical of subordinate performances and analytical, and therefore often resist any change or innovation and avoid taking risks [10]. This sometimes results in poor organizational performance, missing the apt opportunities and failure to make the best use of organizational resources.
The bright trait of core self-evaluation (CSE) capture one’s fundamental judgments about his potential and functioning in the world; extremely positive self-views can have the same adverse effects associated with narcissism and hubris [42].
Extraverted leaders are bold and quick decision-makers, so may be less expected to implore input from subordinates and peers. This aggressiveness often alienates the group members who deserve the credit and attention [10].
The leaders with high degree of emotional stability and agreeableness are often lenient in their team handling and performance evaluation. In order to minimize the conflicts in the interest of their peers their decisions are often skewed [43].
The charismatic leaders, through their excellent skill of public speaking, inspire unconditional devotion from followers even in radical situations. It is evident in the literature and the society around that in some bizarre cases, especially persuasive charismatic leaders misuse their interpersonal power for personal gain and exploit followers who are vulnerable to their manipulative appeal.
Instances of such deviant behavior are termed as personalized “dark side” of charismatic leadership [44, 45].
These findings from the literature strike a chord that both “bright side” and “dark side” traits can have positive or negative effects on individuals and the organization depending on the situation and the individual’s levels of the various traits.
8. Detoxifying the leaders
After such a fascinating journey of whereabouts of toxic leadership it would be unjustified if we do not dedicate some time and literature to get an antidote for such venoms. Now you will not get any brownie points to guesstimate the severity of the toxic behavior on the employee and organization. In order to combat such a lethal form of leadership, organizations need to first come to standings with and accept that it exists and that there could be a problem from within. “Once the light is shined on it, people can begin to talk about it. No one wants the badge of toxic leader” [15].
It is premature recognition in leader’s career also shrink the probability of building severe toxic behaviors later. As said “prevention is better than cure,” if diagnosed at right time the treatment becomes easier.
The subordinates working closely with the leader may prove to be the best judge and identifier of the toxic behavior in the leader. Thus, a 360° performance and personality evaluation of such leaders is asked for by the executive mentors. They should minutely monitor and ensure that a toxic leaders’ interaction with subordinates garner a healthy work environment. Also, welcoming and hassle free complaint windows and whistle blowing should be encouraged for any wrong doing in the organization. Once toxic leadership behaviors have been exposed, recognized and appropriate action taken within the organization, such lessons learned can become an integral part of the selection or promotion process for future leaders.
Prof. Lipman suggested for creation of enriched organizational and personal policies to regulate the risk and brunt of toxic leadership. On a personal front, someone experiencing toxicity should endeavor to not lose calm, distillate oneself from such filthy environment, concentrating more on their own assigned tasks, creating a coalition and evading solo confrontations.
A pinch of advice for the guardians of the organization is to cautiously formulate checks and controls for prompt identification of toxic leadership behavior persisting in the organization. It will render them some extra time to intervene and assist in reorienting those deviant leaders. This could be as early as at time of recruitment. Few of the personality and attitude tests along with technical assessment could help better understand personality shade of the interviewee. This could save organization from future catastrophes. Even restructuring of some aspect at performance appraisal procedure could aid up to certain extents.
Sometimes such circumstances arise in organizations when a good performer is gradually exposed to a high-risk zone of emergence of toxic traits. It is a high-alert situation for the human resource manager and signal to recheck organizational policies and its implementation. History is bursting with examples of organizations perishing to their aggressive and recursive policies that concentrate only on the upsurge of financial numbers. It prompted leaders to become toxic and yield profit, as in corporate scams like those of Enron, Lehman Brothers, Bear Sterns and WorldCom. A developing economy like India too has no exceptions to corporate and government project catastrophes like that of Satyam Computer Services Ltd. and Common Wealth Games, Coalgate scams to name just a few.
Other steps to generate an antidote of toxicity are interpersonal and technical skill development training programs. HR strategies should be aimed to counter the components of “Toxic triangle” at primary level. Only fixing toxic behavior could be too meager a step to resolve the grave crisis organizations go through. Perhaps it solicits a strong dedicated group of key opinion-shapers from within the firm to confront and counsel them. Proper verbal and strict written warnings to the nuisance creators should be raised from the appropriate authorities on time.
In case organizations do not have mechanisms to monitor toxic leadership behaviors, they can seek professional intervention by external counselors for helping the victims and also reeducating the deviant leaders. Even if after multiple warnings the behavior has not changed, HR must decide what to do. If the person has skills useful to the company and is a good worker, you may consider transferring him/her out of a managerial position but keep him/her in the company. Some people just do not work well with others, but may blossom when working in a narrower sphere of interaction. The last option available with an organization, after the intervention and follow-up period, is to offer for the safe exit of the leaders responsible for spreading toxicity.
Once you have addressed your current toxic managers, you have to make sure that much do not spring up in future. To begin with, make sure job descriptions include treating employees in a gracious and appropriate manner. Clearly define the behaviors that will not be tolerated and hold them accountable for turnover. Vigilant monitoring and effective policies can alone eliminate huge percentage of the risk of toxicity development in an organization.
9. Importance and conclusion
The ratio of toxic leaders to effective leaders is unbalanced and, thankfully, the majority of leaders are not toxic. LTG. Walter F. Ulmer estimated in an article entitled “Toxic Leadership” [46] that 30–50% of leaders are essentially transformational, while only 8–10% are essentially toxic. The unfortunate reality is that one toxic leader in an organization can do such incredible damage; he or she can bring down an entire culture without even realizing it. As one rotten apple can spoil the whole basket, one toxic leader is enough for menace.
Leadership toxicity may be an omnipresent facet of organizations; however, it attracts far less consideration than it merits. It is inevitable that a pacesetter as a social personality always stays slanted to the vulnerabilities regardless of their position, professional and educational experience and capability. Many a times, the workplace culture and environments are what prompt leaders toward toxicity to some degree. Leadership toxicity is by all accounts an unavoidable part of organizational life undermining individual and organizational performance.
Toxic leadership may be portrayed as a silent killer as it positions leaders as invincible to sabotage, cease, and punish those who question such supremacy. In sum, toxic leadership is an expensive anomaly. It incapacitates individuals, groups and organizations, even nations. Neglecting to bargain unflinchingly with the multifaceted strengths that encourage our passive consent to toxic leaders will only endorse the decimation such leaders create.
People and the organization define a nation. Toxic leadership could be held responsible not only for organizational but also for the kind of political and economic turmoil South Asia beholds in the present decade. Conflict in interest and intentions of senior leaders of nations and political catastrophes are results of toxicity in leadership. Alarming growth in terrorist organization is due to misguidance of present youth. Even though they are taught good leadership, toxic leadership is more appealing to the masses than the good leaders, be it because they have such strong emotion for power that their energy pull followers into their wake or because they manage to fool people.
Before we conclude our chapter and let you free to observe and tackle the toxicity around, it must be borne in mind that toxic leadership is enormously treacherous not only to individuals that bear the brunt of it but also to the sustainability of the affected organization. It may not directly distress you but the ashes of the fire are surely going to bother you. Thus, make efforts not to let people showing traces of toxicity or dysfunctional behavior take charge of you and contaminate the organization, the society and the nation as a whole. Percolating and growing of such weeds should not be permissible in an organization under any state of affairs. Although, our chapter tried to assist you to assimilate the construct and clear out the hazy picture of the most menacing form of leadership, that is, toxic leadership, however, scope still persists to design/develop and implement specific methods and mechanism to identify, control and even eliminate toxic leadership behavior before it becomes the new culture of the organization. The basic objective of this chapter is to amplify awareness and promote positive social change within organizations. This may encourage and assist others to lend a hand to sufferers of toxic leadership and also minimize stress on their subordinates. This eventually will endow subordinates with proficiency to counter and make a toxic leader more accountable and ethical, which might in due course reduce the prevalence of toxic leadership and increase organizational success and well-being.
\n',keywords:"toxic leadership, behavior, stimulators of toxic leaders, myths, detoxifying toxic leaders, positivity in gray area",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/60316.pdf",chapterXML:"https://mts.intechopen.com/source/xml/60316.xml",downloadPdfUrl:"/chapter/pdf-download/60316",previewPdfUrl:"/chapter/pdf-preview/60316",totalDownloads:2205,totalViews:2322,totalCrossrefCites:3,totalDimensionsCites:3,hasAltmetrics:1,dateSubmitted:"November 22nd 2017",dateReviewed:"February 15th 2018",datePrePublished:"November 5th 2018",datePublished:"January 3rd 2019",dateFinished:null,readingETA:"0",abstract:"Recent misuses of power in politics, corporate and religious arena have invigorated interest in dark side of leadership. This chapter sheds light on a menacing type of dark leadership—toxic leadership. Owing to the dearth of a comprehensive delineation of “toxic” leadership from its related phenomena, this chapter addresses the paucities and clarifies the nature, process, reasons and consequences of “toxic” leadership. It reviews, summarizes and integrates the existing literature on toxic leadership to draw nomological distinctions amongst different constructs of dark leadership and eventually presents stimulators and behavioral symptoms of toxic leadership. Few contemporary myths and detoxification measures are discussed to combat toxicity in a leader for a sustainable organization. The goal of the chapter is to reach our readers’ curiosity, enhance their frame of reference and bring new insights to educate them by providing guidelines and awareness about toxic leadership.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/60316",risUrl:"/chapter/ris/60316",book:{slug:"dark-sides-of-organizational-behavior-and-leadership"},signatures:"Nivedita Singh, Santoshi Sengupta and Santosh Dev",authors:null,sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Mapping the origins",level:"1"},{id:"sec_3",title:"3. Behaviors and qualities that make a leader toxic",level:"1"},{id:"sec_4",title:"4. What stimulates a leader’s toxicity?",level:"1"},{id:"sec_5",title:"5. After effects of toxic leadership",level:"1"},{id:"sec_6",title:"6. Common myths about toxic behavior",level:"1"},{id:"sec_7",title:"7. Let us ascertain a ray of hope: The positivity in gray areas",level:"1"},{id:"sec_8",title:"8. Detoxifying the leaders",level:"1"},{id:"sec_9",title:"9. Importance and conclusion",level:"1"}],chapterReferences:[{id:"B1",body:'Ross DB, Rande M, Julie E. Servant leadership to toxic leadership: Power of influence over power of control. Fischler College of Education: Faculty Presentations; 2014. p. 244'},{id:"B2",body:'Detoxifying your Culture and Encouraging More Mindful Leadership [Internet]. Life meets work. 2017. Available from: https://www.lifemeetswork.com/insights-archive/toxic-leadership'},{id:"B3",body:'Patty A. One in five bosses is a psychopath. The Sydney morning herald [Internet]. 2016. [cited 10 January, 2018]. Available from: http://www.smh.com.au/business/workplace-relations/one-in-five-bosses-is-a psychopath-research-reveals-20160913-greyg6.html'},{id:"B4",body:'Padilla A, Hogan R, Kaiser R. The toxic triangle: Destructive leaders, susceptible followers, and conducive environments. The Leadership Quarterly. 2007;18(3):176-194'},{id:"B5",body:'Kellerman B. 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Good times, bad times: How personal disadvantage moderates the relationship between social dominance and efforts to win. Personality and Social Psychology Bulletin. 2008;34:1420-1433'},{id:"B41",body:'Judge TA, Piccolo RF, Kosalka T. The bright and dark sides of leader traits: A review and theoretical extension of the leader trait paradigm. The Leadership Quarterly. 2009;20:855-875'},{id:"B42",body:'Hiller NJ, Hambrick DC. Conceptualizing executive hubris: The role of (hyper-) core self-evaluations in strategic decision-making. Strategic Management Journal. 2005;26:297-319'},{id:"B43",body:'Graziano WG, Eisenberg NH. Agreeableness: A dimension of personality. In: Hogan R, Johnson JA, Briggs SR, editors. Handbook of Personality Psychology. San Diego, CA: Academic Press; 1997. pp. 767-793'},{id:"B44",body:'Conger JA. The dark side of leadership. Organizational Dynamics. 1990;19:44-55'},{id:"B45",body:'Howell JM. Two faces of charisma: Socialized and Personalized Leadership in Organizations. Charismatic Leadership: The Elusive Factor in Organizational Effectiveness. San Francisco: Jossey-Bass; 1988. pp. 213-236'},{id:"B46",body:'Ulmer WF Jr. Toxic leadership: What are we talking about. Army Magazine. 2012 Jun;62(6):47-52'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Nivedita Singh",address:"nivedita.sh10@gmail.com",affiliation:'
Department of Humanities and Social Sciences, Jaypee Institute of Information Technology, India
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Naranjo-Valencia and Gregorio Calderon-Hernández",authors:[{id:"247967",title:"Dr.",name:"Julia",middleName:null,surname:"Naranjo-Valencia",fullName:"Julia Naranjo-Valencia",slug:"julia-naranjo-valencia"},{id:"260915",title:"Dr.",name:"Gregorio",middleName:null,surname:"Calderon-Hernández",fullName:"Gregorio Calderon-Hernández",slug:"gregorio-calderon-hernandez"}]},{id:"63695",title:"The Role of National Cultures in Shaping the Corporate Management Cultures: A Three-Country Theoretical Analysis",slug:"the-role-of-national-cultures-in-shaping-the-corporate-management-cultures-a-three-country-theoretic",signatures:"Mohammad Ayub Khan and Laurie Smith Law",authors:[{id:"247709",title:"Prof.",name:"Mohammad",middleName:null,surname:"Khan",fullName:"Mohammad Khan",slug:"mohammad-khan"},{id:"247712",title:"Prof.",name:"Laurie Smith",middleName:null,surname:"Law",fullName:"Laurie Smith Law",slug:"laurie-smith-law"}]},{id:"62016",title:"Project Organizational Culture Framework in Construction Industry",slug:"project-organizational-culture-framework-in-construction-industry",signatures:"Luong Hai Nguyen and Tsunemi Watanabe",authors:[{id:"243375",title:"Ph.D.",name:"Luong Hai",middleName:null,surname:"Nguyen",fullName:"Luong Hai Nguyen",slug:"luong-hai-nguyen"},{id:"243408",title:"Prof.",name:"Tsunemi",middleName:null,surname:"Watanabe",fullName:"Tsunemi Watanabe",slug:"tsunemi-watanabe"}]},{id:"61335",title:"Organizational Culture as a Determinant of Construction Companies’ Competitiveness: Case Study of Croatia",slug:"organizational-culture-as-a-determinant-of-construction-companies-competitiveness-case-study-of-croa",signatures:"Ivana Šandrk Nukić",authors:[{id:"246557",title:"Ph.D.",name:"Ivana",middleName:null,surname:"Šandrk Nukić",fullName:"Ivana Šandrk Nukić",slug:"ivana-sandrk-nukic"}]},{id:"62015",title:"Reflex-Adaptive Organizational Structure in the Implementation of Large-Scale Projects",slug:"reflex-adaptive-organizational-structure-in-the-implementation-of-large-scale-projects",signatures:"Andrey Morozenko",authors:[{id:"246628",title:"Dr.",name:"Andrey",middleName:null,surname:"Morozenko",fullName:"Andrey Morozenko",slug:"andrey-morozenko"}]},{id:"63974",title:"Leadership Competencies Affecting Projects in Organization",slug:"leadership-competencies-affecting-projects-in-organization",signatures:"Riaz Ahmed",authors:[{id:"195432",title:"Dr.",name:"Riaz",middleName:null,surname:"Ahmed",fullName:"Riaz Ahmed",slug:"riaz-ahmed"}]},{id:"63543",title:"Transformational Leadership and Organizational Culture: Keys to Binding Employees to the Dutch Public Sector",slug:"transformational-leadership-and-organizational-culture-keys-to-binding-employees-to-the-dutch-public",signatures:"Saniye Çelik",authors:[{id:"246109",title:"Dr.",name:"Saniye",middleName:null,surname:"Çelik",fullName:"Saniye Çelik",slug:"saniye-celik"}]}]}]},onlineFirst:{chapter:{type:"chapter",id:"71323",title:"Wavelet Neural Networks for Speed Control of BLDC Motor",doi:"10.5772/intechopen.91653",slug:"wavelet-neural-networks-for-speed-control-of-bldc-motor",body:'\n
\n
1. Introduction
\n
Brushless DC (BLDC) motors have a wide application in our life due to their high-power density and high dynamic response. In addition, the BLDC motor is utilized with constant loads, varying loads, and position applications with high accuracy. This motor is generally controlled utilizing electronically commutation by three-phase power semiconductor bridge inverter with rotor position sensors that are required for starting and providing proper firing sequence to turn on the power devices in the inverter bridge. Based on the rotor position, the power devices are commutated sequentially every 60° [1, 2]. The mathematical model and the Simulink model of BLDC motor to control the speed of a BLDC by using conventional methods are introduced in Refs. [2, 3, 4, 5, 6]. The DC-DC converter technique is utilized to control the speed of the motor [5, 6].
\n
In the past decade, artificial intelligence techniques such as neural networks, fuzzy-neural networks, and wavelet neural networks control have been utilized to control the speed of the BLDC motor [7, 8, 9, 10]. Since BLDC motor is a multivariable and nonlinear system, it is complex to obtain high performance by applying classical PID control. The main objective of this chapter is to develop wavelet neural networks (WNNs) to control the speed of the BLDC motor, and the recurrent wavelet neural network (RWNN). These methods lead to an enhanced dynamic performance of the system of motor drive and are resistant to load perturbations. The learning strategy for the wavelet neural network and PID controller is developed based on PSO algorithm.
\n
\n
\n
2. Wavelet networks
\n
The combination of wavelet principle and neural networks has led to producing new representing network of wavelet neural network (WNN). Wavelet networks are feedforward networks utilizing wavelets as activation functions. Wavelet networks substitute the sigmoid activation components of the classical feedforward artificial neural networks (ANNs) with wavelets transform function. In wavelet neural networks, both the translation (position) and the dilation are tuning besides weights. The utilization of wavelet node outcomes in efficient networks are optimally approximated and estimated for nonlinear and nonstationary functions [11, 12]. There are two main types to construct the wavelet neural network:
\nWavenet (fixed grid WNs): in this type, the neural network and the wavelet processing are accomplished separately. The input signal is first decomposed utilizing some wavelet bases by neurons in the hidden layer with fixed wavelet bases; positions and dilations of the wavelets are preset and only the weights have to be adjusted by learning the network. The main problem is the choice of wavelet frames/bases [12, 13].
\nWavelet network (adaptive WNs): this type merges the two theories, which are the dilation and the translation of wavelets along with the summer weights that are adjusted in conformity with some learning procedures. Generally, the modeling of the wavelet network involves two steps: determining the network construction (the number of neurons in each layer, the number of layers, and the type of activations function (wavelet transform)) and modifying the wavelet network parameters by some optimizing algorithm method [12, 13].
\n\n
\n
\n
3. Structure of wavelet neural network (WNN)
\n
The arrangement of WNN is similar to that of the neural network. The hidden layer contains neurons, whose activation functions are driven from a wavelet basis. These wavelet neurons are generally referred to as wavelons, whose parameters of the inputs contain the wavelet dilation and translation elements [12, 14]. Wavelet networks can be categorized into recurrent and nonrecurrent (feedforward) types.
\n
\n
3.1 Feedforward wavelet neural network (FFWNN)
\n
The FFWNN has no feedback connection. That is, the output is calculated straightly from the input with feedforward connections [12]. There are two arrangements of feedforward wavelet networks:
\n
\n
3.1.1 Radial basis wavelet neural network (RBWNN)
\n
Radial basis wavelet neural network (RBWNN) is the simplest form of the wavelet network [15, 16]. The arrangement of radial basis wavelet neural network (RBWNN) is shown in \nFigure 1\n. This network approaches any required signal f(t) by simplifying a linear combination of a group of daughter wavelets \n\n\nψ\n\na\n,\nb\n\n\n,\n\n where \n\n\nψ\n\na\n,\nb\n\n\n\n are created by dilation a and translation b from mother wavelet \n\nψ\n\n [11, 17].
\n
Figure 1.
The building of radial basis wavelet neural network (RBWNN).
where x is the input signal, N is the number of neuron in the hidden layer, and \n\n\nw\nN\n\n\n is the weights of the output. The network parameters \n\n\nw\nN\n\n,\n\na\nN\n\n,\nand\n\n\nb\nN\n\n\n can be training and optimizing by any optimization technique. In this chapter, the PSO algorithm is used to minimize the error according to the fitness function as will be demonstrated later.
\n
\n
\n
3.1.2 Conventional wavelet neural network
\n
The conventional WNN is a general form of radial basis wavelet neural network [19]. \nFigure 2\n depicts the building of the conventional wavelet network, the number of hidden layers and neurons that are selected to create an appropriate WNN, and the parameters that are optimized by the PSO algorithm. The input layer can be represented by a vector \n\nx\n=\n\n\nx\n1\n\n\nx\n2\n\n…\n\nx\nM\n\n\n\n, the output layer represented by a vector \n\ny\n=\n\n\ny\n1\n\n\ny\n2\n\n…\n\ny\nK\n\n\n\n, and the activation function of hidden layer is the wavelet basis function. The output \n\n\nY\nj\n\n\n can be given as follows [11, 19]:
\n
Figure 2.
The building of conventional wavelet neural network.
where, \n\nj\n=\n1\n,\n2\n,\n3\n,\n…\n,\nK\n;\nM\n\n is the number of inputs; K is the number of output layers; N is the number of hidden layers; g is the bias; and \n\nσ\n\nu\n\n\n\nis the activation function of the output layer, the most common formula of activation function being sigmoid function which can be illustrated as follows [12]:
In recurrent WNNs, the output depends not only on the present inputs of the network but also on the prior outputs or conditions of the network [12, 15]. Recurrent networks have feedback and are also known as feedback networks. There are several types of recurrent networks that depend on the feedback connection [12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22].
\n
In the recurrent wavelet network structures, the wavelet network input involves delayed samples of the system output y(k). The number of inputs increases with the order of the system actuality demonstrated. \nFigure 3\n depicts the structure of recurrent wavelet network. Hence, the output for each layer can be calculated as [20, 23, 24]:
where \n\n\na\nN\n\n\n and \n\n\nb\nN\n\n\n are translation and dilation parameters of wavelets. The inputs of this layer for time n can be denoted as:
where x is the input signal, N is the number of neuron in the hidden layer, \n\n\nw\nN\n\n\n is the output weight, \n\n\nD\ni\n\n,\n\nD\n0\n\n\n is the number of delay for the input and output network, and \n\n\nr\nn\n\n\nis\n\n the weight of the output feedback loop.
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\n
\n
\n
4. Particle swarm optimization
\n
Particle swarm optimization is an inhabitance-based computational procedure motivated from the simulation of gregarious behaviors (social-psychological): fish schooling, bird flocking, and swarm theory. PSO was firstly invented and established by Eberhart and Kennedy [25, 26]. In the PSO algorithm, in place of utilizing evolutionary operators such as mutation and crossover to operate algorithms, the population dynamics emulates a “bird flock” behavior, where social sharing of information takes place and individuals can yield from the finds and prior experience of all the other companions through the search for food. Therefore, each companion, called particle, in the population, which is called swarm intelligence as shown in \nFigure 4\n, is assumed to fly in several directions over the search space to meet the request fitness function [27, 28].
\n
Figure 4.
Swarm intelligence.
\n
\n
4.1 Particle swarm optimization algorithm
\n
The PSO algorithm is one of the evolutionary computation techniques to solve optimization troubles. In this algorithm, a swarm of individuals or entities called particles flies over the exploration space [29, 30]. Each particle acts as a probable solution to the optimization troubles. The position of a particle is influenced by the best position visited by itself, i.e., its own knowledge or experience and the position of the best particle in its knowledge of neighboring particles. When the neighborhood is the entire swarm, the best position in the neighborhood of the particle is denoted as the global best position and the resulting algorithm is referred to as the global best position PSO, where the finest prior position that gives the minimum fitness value of any particle is called local best position (lbest). The index of the best particle of all particles in the population is called global best position (gbest). The algorithm is generally referred to as the lbest PSO when smaller neighborhoods are used. For each particle, the performance is measured utilizing an objective function that differs depending on the optimization challenge. The basic PSO algorithm is given below according to the flow chart which is shown in \nFigure 5\n [31, 32, 33, 34, 35].
\n
Step 1. Generation of population particles
\n
Create particles regularly distributed over x, then choose the number of particles, number of iterations, modification accelerating coefficients \n\n\nc\n1\n\n\n\nand \n\n\nc\n2\n\n\n, the inertia weight (w) and random numbers \n\n\nR\n1\n\n\n, and \n\n\nR\n2\n\n\n to start the optimum searching.
\n
Step 2. The initialization for each particle
\n
Initialize the present position \n\n\nx\ni\n\n\nt\n\n\n and the velocitiy\n\n\n\nv\ni\n\n\nt\n\n\n for each particle. The particles are randomly produced among the minimum and maximum limits of parameter values. Each particle is treated as a point in a D-dimensional space. The ith particle is denoted as \n\n\nx\ni\n\n=\n\n\nx\n\ni\n1\n\n\n\nx\n\ni\n2\n\n\n…\n\nx\niD\n\n\n\n. The velocity for the particle i is represented as \n\n\nv\ni\n\n=\n\n\nv\n\ni\n1\n\n\n\nv\n\ni\n2\n\n\n…\n\nv\niD\n\n\n\n, then the local best position (lbest) and the global best position (gbest) are initialization.
\n
Step 3. Evaluation of fitness function
\n
The overall performance (speed of convergence, efficiency, and optimization accuracy) of the PSO algorithm counts on the objective function that observes the optimization search. The objective function is chosen to minimize the reference constraints. The popular performance standards based on the error condition are integrated absolute error (IAE), integrated of time weight square error (ITSE), and integrated of square error (ISE) that can be estimated theoretically in the frequency domain [31, 32, 36]. In this chapter, multiobjective functions are utilized based on the integral of the squared error (ISE) criterion and overshoot (\n\n\nM\np\n\n)\n\n criterion as follow [37, 38]:
where y(i) is the system output and D(i) is the desired output, while n is the actual speed and \n\n\nn\nref\n\n\n is the desired speed.
\n
Step 4. Update the swarm
\n
The updating of the velocity \n\n\n\nv\ni\n\n\nt\n\n\n and the present position \n\n\nx\ni\n\n\nt\n\n\n for each particle in the swarm is done according to Eqs. (13) and (14). Then the main loop and the objective function are calculated for updating positions of particles. If the new value is improved than the previous lbest, the new value is fixed to lbest. Similarly, gbest value is also updated as the best lbest. Velocity of each agent can be modification by the following:
where \n\n\nx\ni\nk\n\n\n is the present position of particle i at iteration k, \n\n\nv\ni\nk\n\n\n is the velocity of particle i at iteration k, w is the inertia weight which can be represented in Eq. (15), \n\n\nc\n1\n\n\n, \n\n\nc\n2\n\n\n represent positive acceleration constants and \n\n\nR\n1\n\n\n, \n\n\nR\n2\n\n\n are random variables uniformly distributed in the range [0; 1].
where,\n\n\n\nw\nmin\n\n\nis the inital weight\n\n,\n\n\n\nw\nmax\n\n\nis the final weight\n\n, \n\n\niter\nmax\n\n\n is the maximum iteration number.
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Step 5. Stopping criteria
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If the current iteration number reaches the predetermined maximum iteration number, then exit. Otherwise, execute another initialization for each particle and reiterate the process.
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\n
\n
5. Speed control of BLDC motor based on wavelet neural network
\n
The WNN-PID controller based on PSO is proposed in this section, which combines the ability of the artificial neural networks for learning with the ability of wavelet for identification, control of dynamic system, and also having the capability of self-learning and adapting [10, 11, 19, 37]. Two types of wavelet network are modified in this section, feedforward WNN and proposed recurrent WNN with online tuning optimization using PSO algorithm [22, 23, 24].
\n
\n
5.1 WNN-PID controller based on PSO
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In this type of controller, the WNN is utilized with PID controller based on PSO algorithm. WNN-PID controller utilizes online learning by PSO algorithm, where the PSO learning algorithm is used to train the translation parameters \n\n\na\nk\n\n\n and \n\n\nb\nk\n\n\n, weights connection in the WNN, and the parameter (\n\n\nk\np\n\n\n,\n\n\n\nk\ni\n\n\n,\n\n\n\nk\nd\n\n\n) of PID controller on-line with the model of BLDC motor to control the speed at the desired value. There are two major issues to implement any wavelet neural networks. First, the network architecture is used and second, the algorithm is used to learn the network by the PSO algorithm. \nFigure 6\n depicts the block diagram of the BLDC motor with WNN-PID based on PSO algorithm. The structure and the design of the WNN-PID controller will be given in the next subsection.
\n
Figure 6.
Block diagram of the BLDC motor with WNN-PID controller based on PSO algorithm.
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\n
\n
5.2 Design of the structure of WNN-PID controller based on PSO training algorithm
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\n
\n1. Design of PSO algorithm: the PSO algorithm is discussed in Section 5, where each particle parameters are initiated to make a population and then the algorithm is accomplished according to the flow chart given in \nFigure 6\n, which includes training the parameters of this controller to guarantee the minimization of an objective function. The objective fitness is evaluated as follows:
where ISE is the integrated of square error and \n\n\nM\np\n\n\n is the maximum peak overshoot.
\n2. Design of WNN-PID controller: to design the WNN-PID controller, the type of WNN must be selected as shown in Section 3 and also the number of layers and neurons and the wavelet function type must be selected [16]. In this chapter, the input layer has two inputs: the speed error and the change of this error. One hidden layer with four neurons is used. Three types of mother wavelet functions are used and they are: the Mexican hat function is [10, 11, 22, 37, 39]
where x is the desired signal and \n\nw\n\n is a variable value, which was adopted to satisfy the admissibility condition. w = 5 is chosen. The output layer contains one output which is the sum of PID controller and WNN outputs. The parameters values of the WNN-PID controller, such as the dilation factors\n\n\n\na\n\nk\n′\ns\n\n\n\n and the translation factors \n\n\nb\n\nk\n′\ns\n\n\n\n\nof the mother wavelet function, the weights connection \n\n\nw\n\nk\n′\ns\n\n\n\n of the WNN, and PID parameters (\n\n\nk\np\n\n\n, \n\n\nk\ni\n\n\n, \n\n\nk\nd\n\n\n), are optimized online in PSO algorithm. The results given in this chapter are for Mexican hat function only. The results for the rest functions are similar to that in Mexican hat and are not given.
\n
\n
\n
\n
6. Simulink implementation and results for a BLDC motor drive based on WNN-PID controller
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\n
6.1 Speed control based on feedforward WNN-PID controller
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The feedforward WNN with PID controller (FWNN-PID) is utilized to control the speed of the BLDC motor as shown in the Simulink model in \nFigure 7\n. The inputs of the WNN are the speed error and the change of this error, while the hidden layer has four neurons and one output in the output layer. The translation and dilation factors, weights connection for WNN, and PID parameters are learning on-line in PSO algorithm. The output of WNN is given by Eq. (2).
\n
Figure 7.
Simulink model of WNN-PID controller.
\n
The PSO parameters are given in \nTable 1\n. These parameters are chosen to get optimal parameters for the PID controller and the wavelet neural network; when it is tuned on-line in PSO algorithm and BLDC motor drive, the optimal values for the PID controller parameters and the WNN parameters (a\'s, b\'s, w\'s) are given in \nTables 2\n and \n3\n, respectively.
\n
\n
\n
\n\n
\n
PSO_Parameters
\n
Value
\n
\n\n\n
\n
Size of the swarm “no of birds”
\n
50
\n
\n
\n
Maximum iteration number
\n
50
\n
\n
\n
Dimension
\n
15
\n
\n
\n
PSO parameter \n\n\nc\n1\n\n\n\n
\n
1.2
\n
\n
\n
PSO parameter \n\n\nc\n2\n\n\n\n
\n
1.2
\n
\n
\n
Wmax\n
\n
0.9
\n
\n
\n
Wmin\n
\n
0.3
\n
\n\n
Table 1.
PSO parameter values.
\n
\n
\n
\n
\n
\n\n
\n
Parameters
\n
\n\n\n\nK\np\n\n\n\n
\n
\n\n\n\nK\ni\n\n\n\n
\n
\n\n\n\nK\nd\n\n\n\n
\n
\n
\n
Values
\n
6.958
\n
3.241
\n
0.006274
\n
\n\n
Table 2.
PID parameters tuned using PSO for WNN-PID controller.
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n\n
\n
WNN dilation parameters
\n
WNN translation parameters
\n
WNN weights parameters
\n
\n
\n
\n\n\n\na\n1\n\n\n\n
\n
\n\n\n\na\n2\n\n\n\n
\n
\n\n\n\na\n3\n\n\n\n
\n
\n\n\n\na\n4\n\n\n\n
\n
\n\n\n\nb\n1\n\n\n\n
\n
\n\n\n\nb\n2\n\n\n\n
\n
\n\n\n\nb\n3\n\n\n\n
\n
\n\n\n\nb\n4\n\n\n\n
\n
\n\n\n\nw\n1\n\n\n\n
\n
\n\n\n\nw\n2\n\n\n\n
\n
\n\n\n\nw\n3\n\n\n\n
\n
\n\n\n\nw\n4\n\n\n\n
\n
\n\n\n
\n
4.535
\n
1.227
\n
3.013
\n
3.141
\n
1.248
\n
0.314
\n
2.576
\n
1.352
\n
3.632
\n
3.321
\n
2.2843
\n
2.4722
\n
\n\n
Table 3.
WNN parameters tuned using PSO.
\n
The BLDC motor drive is implemented in Simulink/Matlab program as shown in \nFigure 6\n with the optimal values of PID controller parameters and the optimal values of WNN parameters. \nFigure 8\n shows the speed response of the BLDC motor due to change in reference speed. The motor is started at a speed of 500 rpm and then is changed in step to a speed of 500 rpm for every 0.2 s. The actual speed of the motor is tracking the desired speed with a good response. The system starts at no load and suddenly a torque 2 N m (full load) is added at t = 0.4 s. \nFigure 9\n shows the speed response of the BLDC motor at 2000 rpm during no load and load condition. The developed torque during no load and load condition is shown in \nFigure 10\n. The position signal, the torque-speed characteristics, the phase current \n\n\ni\na\n\n\n, Phase Back-emf \n\n\ne\na\n\n\n voltage, and line voltage \n\n\nv\nab\n\n\n are given in \nFigures 11\n\n–\n\n15\n, respectively.
\n
Figure 8.
Step change in speed response with feedforward WNN-PID controller.
\n
Figure 9.
Speed response of the BLDC motor with WNN-PID controller.
\n
Figure 10.
Developed torque of BLDC motor with feedforward WNN-PID controller.
\n
Figure 11.
Position stair signal of BLDC motor with WNN-PID controller.
\n
Figure 12.
Torque-speed characteristics of BLDC motor with feedforward WNN-PID controller.
\n
Figure 13.
Phase current ia of BLDC motor with feedforward WNN-PID controller.
\n
Figure 14.
Phase back-emf ea voltage for BLDC motor with WNN-PID controller.
\n
Figure 15.
Line voltage vab for BLDC motor with feedforward WNN-PID controller.
\n
\n
\n
6.2 Speed control based on proposed recurrent WNN-PID controller
\n
The RWNN that is proposed here is similar to that of feedforward WNN with feedback connections. The RWNN consists of three layers, with two inputs in the input layer, the hidden layer has four neurons, with one output in the output layer and feedback connection for each layer. In this section, the feedback connection is called “Fully feedback.” Besides, the RWNN contains a number of delay samples in the input and output layers as shown in \nFigure 16\n. The translation and dilation factors, weights and PID parameters are learning on-line to utilize PSO method in the same manner used in the previous subsection and the results are given in \nTables 4\n\n–\n\n6\n. The output of WNN is described by Eqs. (7) and (8).
\n
Figure 16.
Simulink model for a proposed recurrent WNN-PID controller.
\n
\n
\n
\n
\n
\n\n
\n
Parameters
\n
\n\n\n\nK\np\n\n\n\n
\n
\n\n\n\nK\ni\n\n\n\n
\n
\n\n\n\nK\nd\n\n\n\n
\n
\n
\n
Values
\n
4.8256
\n
2.6003
\n
0.0105
\n
\n\n
Table 4.
PID parameters tuned using PSO for RWNN-PID controller.
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n\n
\n
WNN dilation parameters
\n
WNN translation parameters
\n
WNN weights parameters
\n
\n
\n
\n\n\n\na\n1\n\n\n\n
\n
\n\n\n\na\n2\n\n\n\n
\n
\n\n\n\na\n3\n\n\n\n
\n
\n\n\n\na\n4\n\n\n\n
\n
\n\n\n\nb\n1\n\n\n\n
\n
\n\n\n\nb\n2\n\n\n\n
\n
\n\n\n\nb\n3\n\n\n\n
\n
\n\n\n\nb\n4\n\n\n\n
\n
\n\n\n\nw\n1\n\n\n\n
\n
\n\n\n\nw\n2\n\n\n\n
\n
\n\n\n\nw\n3\n\n\n\n
\n
\n\n\n\nw\n4\n\n\n\n
\n
\n\n\n
\n
2.1781
\n
3.0484
\n
3.0181
\n
1.9509
\n
0.1266
\n
5.3694
\n
1.8668
\n
3.2328
\n
0.8769
\n
3.2828
\n
2.7151
\n
0.04124
\n
\n\n
Table 5.
RWNN parameters tuned using PSO.
\n
\n
\n
\n
\n
\n
\n\n
\n
Feedback parameters of RWNN
\n
\n
\n
qq1
\n
qq2
\n
qq3
\n
qq4
\n
qL
\n
\n\n\n
\n
0.2957
\n
0.4738
\n
1.0581
\n
4.2460
\n
3.6623
\n
\n\n
Table 6.
Feedback parameters of RWNN tuned using PSO.
\n
The BLDC motor drive system with RWNN-PID controller is simulated in Matlab/Simulink program as shown in \nFigure 6\n. The time period that is assumed in this model is 1 s. The WNN-PID controller can be utilized for speed control in a wide range between 0 and the rated value, with better performance and more flexibility in the controller. \nFigure 17\n depicts the step change in speed of the BLDC. The motor is started at a speed of 500 rpm and then is changed in step to 500 rpm each 0.2 s. The actual speed of the motor is tracking the desired speed with a good response. The system starts at no load and suddenly a torque 2 N m (full load) is added at t = 0.4 s. \nFigure 18\n shows the speed response of the BLDC motor at 2000 rpm during no load and load conditions. The developed torque during no load and load conditions is shown in \nFigure 19\n. The position signal, the torque-speed characteristics, the phase current \n\n\ni\na\n\n\n, Phase Back-emf \n\n\ne\na\n\n\n\nvoltage and line voltage \n\n\nv\nab\n\n\n are given in \nFigures 20\n\n–\n\n24\n, respectively.
\n
Figure 17.
Step change in speed of BLDC motor with RWNN-PID controller.
\n
Figure 18.
Speed response of the BLDC motor with RWNN-PID controller.
\n
Figure 19.
Development torque of BLDC motor with RWNN-PID controller.
\n
Figure 20.
Position stair signal of BLDC motor with RWNN-PID controller.
\n
Figure 21.
Torque-speed characteristics of BLDC motor with RWNN-PID controller.
\n
Figure 22.
Phase current ia of BLDC motor with RWNN-PID controller.
\n
Figure 23.
Phase back-emf ea voltage of BLDC motor with RWNN-PID controller.
\n
Figure 24.
Line voltage vab for BLDC motor with RWNN-PID controller.
\n
\n
\n
\n
7. Comparison of two methods for speed control of BLDC motor drive
\n
Comparing among various wavelet neural network schemes shows that the WNN-PID is a preferable method to overcome the nonlinearity in this model with high reliability, more robustness and being good with better performance than the RWNN method as shown in \nTable 7\n. In addition, the performance of the WNN controller in the real application tends to make the system more robust and less sensitive as well as high precision and excellent flexibility.
\n
\n
\n
\n
\n\n
\n
Performance
\n
WNN-PID
\n
RWNN-PID
\n
\n\n\n
\n
Rise time (s)
\n
0.0035
\n
0.0038
\n
\n
\n
Settling time (s)
\n
0.03
\n
0.04
\n
\n
\n
Steady state error
\n
3 × \n\n\n10\n\n−\n3\n\n\n\n%
\n
2 × \n\n\n10\n\n−\n3\n\n\n\n%
\n
\n
\n
Overshoot
\n
0.12%
\n
Approximately 0%
\n
\n\n
Table 7.
Performance of speed control of BLDC motor in each methods.
\n
\n
\n
8. Conclusion
\n
In this chapter, the WNN is used with the PID controller to make an adapted controller named as the WNN-PID controller. This controller is utilized to control the speed of BLDC motor in an extensive range and can stock preferable performance than a traditional controller. Two schemes of wavelet neural network are modified for speed control of BLDC motor such as WNN and RWNN. PSO algorithm is utilized for tuning and learning the parameters of the two controllers. The two methods are implemented and tested for different conditions and the performance is compared as shown in \nTable 7\n. From the simulation results, one can conclude that the proposed WNN controller with PID controller is the best scheme in performance and stability. In addition, using the proposed WNN controller to control the speed of BLDC motor gives better results compared to traditional methods.
\n
\n\n',keywords:"BLDC motor, particle swarm optimization (PSO), wavelet neural network (WNN), speed control",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/71323.pdf",chapterXML:"https://mts.intechopen.com/source/xml/71323.xml",downloadPdfUrl:"/chapter/pdf-download/71323",previewPdfUrl:"/chapter/pdf-preview/71323",totalDownloads:151,totalViews:0,totalCrossrefCites:1,dateSubmitted:"October 21st 2019",dateReviewed:"February 6th 2020",datePrePublished:"March 5th 2020",datePublished:null,dateFinished:null,readingETA:"0",abstract:"In the recent years, researchers have sophisticated the synthesis of neural networks depending on the wavelet functions to build the wavelet neural networks (WNNs), where the wavelet function is utilized in the hidden layer as a sigmoid function instead of conventional sigmoid function that is utilized in artificial neural network. The WNN inherits the features of the wavelet function and the neural network (NN), such as self-learning, self-adapting, time-frequency location, robustness, and nonlinearity. Besides, the wavelet function theory guarantees that the WNN can simulate the nonlinear system precisely and rapidly. In this chapter, the WNN is used with PID controller to make a developed controller named WNN-PID controller. This controller will be utilized to control the speed of Brushless DC (BLDC) motor to get preferable performance than the traditional controller techniques. Besides, the particle swarm optimization (PSO) algorithm is utilized to optimize the parameters of the WNN-PID controller. The modification for this method of the WNN such as the recurrent wavelet neural network (RWNN) was included in this chapter. Simulation results for all the above methods are given and compared.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/71323",risUrl:"/chapter/ris/71323",signatures:"Ameer L. Saleh, Adel A. Obed, Hamza H. Qasim, Waleed I.H. Breesam, Yasir I.A. Al-Yasir, Naser Ojaroudi Parchin and Raed A. Abd-Alhameed",book:{id:"9893",title:"Automation and Control",subtitle:null,fullTitle:"Automation and Control",slug:null,publishedDate:null,bookSignature:"Prof. Constantin Volosencu, Dr. Serdar Küçük, Dr. José Guerrero and Dr. Oscar Valero",coverURL:"https://cdn.intechopen.com/books/images_new/9893.jpg",licenceType:"CC BY 3.0",editedByType:null,editors:[{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:null,sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Wavelet networks",level:"1"},{id:"sec_3",title:"3. Structure of wavelet neural network (WNN)",level:"1"},{id:"sec_3_2",title:"3.1 Feedforward wavelet neural network (FFWNN)",level:"2"},{id:"sec_3_3",title:"3.1.1 Radial basis wavelet neural network (RBWNN)",level:"3"},{id:"sec_4_3",title:"3.1.2 Conventional wavelet neural network",level:"3"},{id:"sec_6_2",title:"3.2 Recurrent wavelet neural networks (RWNNs)",level:"2"},{id:"sec_8",title:"4. Particle swarm optimization",level:"1"},{id:"sec_8_2",title:"4.1 Particle swarm optimization algorithm",level:"2"},{id:"sec_10",title:"5. Speed control of BLDC motor based on wavelet neural network",level:"1"},{id:"sec_10_2",title:"5.1 WNN-PID controller based on PSO",level:"2"},{id:"sec_11_2",title:"5.2 Design of the structure of WNN-PID controller based on PSO training algorithm",level:"2"},{id:"sec_13",title:"6. Simulink implementation and results for a BLDC motor drive based on WNN-PID controller",level:"1"},{id:"sec_13_2",title:"6.1 Speed control based on feedforward WNN-PID controller",level:"2"},{id:"sec_14_2",title:"6.2 Speed control based on proposed recurrent WNN-PID controller",level:"2"},{id:"sec_16",title:"7. Comparison of two methods for speed control of BLDC motor drive",level:"1"},{id:"sec_17",title:"8. Conclusion",level:"1"}],chapterReferences:[{id:"B1",body:'\nPadmaraja Y. Brushless DC (BLDC) Motor Fundamentals. Microchip Technology Inc.; 2003\n'},{id:"B2",body:'\nStefan B. BLDC motor modeling and control—A MATLAB/SIMULINK implementation [master thesis]. Gothenburg, Sweden: Electrical Power Engineering, Chalmers University of Technology; 2005\n'},{id:"B3",body:'\nRambabu S. Modeling and control of a brushless DC motor [master thesis]. In: Power Control and Drives Technology. Rourkela: National Institute of Technology; 2007\n'},{id:"B4",body:'\nUlasyar A, Zad HS, Zohaib A. Intelligent speed controller design for brushless DC motor. In: 2018 International Conference on Frontiers of Information Technology (FIT); January; Islamabad, Pakistan. 2019\n'},{id:"B5",body:'\nSaleh AL, Obed AA. Speed control of brushless DC motor based on fractional order PID controller. International Journal of Computer Applications. 2014;95:1-6\n'},{id:"B6",body:'\nObed AA, Kadhim AK. Speed and current limiting control strategies for BLDC motor drive system: A comparative study. International Journal of Advanced Engineering Research and Science. 2018;5(2):119-130\n'},{id:"B7",body:'\nNiasar AH, Vahedi A, Moghbelli H. Speed control of a brushless DC motor drive via adaptive neuro-fuzzy controller based on emotional learning algorithm. In: IEEE Proceedings of the Eighth International Conference on Electrical Machines and Systems; vol. 1. 2005. pp. 230-234\n'},{id:"B8",body:'\nAwadallah MA, Bayoumi EHE, Soliman HM. Adaptive deadbeat controllers for brushless DC drives using PSO and ANFIS techniques. Journal of Electrical Engineering. 2009;60(1):3-11\n'},{id:"B9",body:'\nKumar NS, Kumar CS. Design and implementation of adaptive fuzzy controller for speed control of brushless DC motors. International Journal of Computer Applications. 2010;1(27):36-41\n'},{id:"B10",body:'\nMaohua Z, Changliang X, Yang T, Dan L, Zhiqiang L. Speed control of brushless DC motor based on single neuron PID and wavelet neural network. In: IEEE International Conference on Control and Automation Guangzhou, China; June. 2007. pp. 617-620\n'},{id:"B11",body:'\nHameed WI, Sawadi BA, Al-Kamil SJ, Al-Radhi MS, Al-Yasir YIA, Saleh AL, et al. Prediction of solar irradiance based on artificial neural networks. Inventions. 2019;4:45\n'},{id:"B12",body:'\nDavid V. Wavelet neural networks [dissertation submitted for the Master Science]. In: Data Analysis, Networks and Nonlinear Dynamics. Department of Mathematics, University of York; 2005\n'},{id:"B13",body:'\nBillings SA, Hua-Liang W. A new class of wavelet networks for nonlinear system identification. IEEE Transactions on Neural Networks. 2005;16:862-874\n'},{id:"B14",body:'\nDhiraj A, Sunil K. Adaptive fuzzy wavelet network control design for nonlinear systems. International Journal of Advanced Technology & Engineering Research (IJATER). Jan. 2013;3:148-157\n'},{id:"B15",body:'\nCheng-Jian L, Hung-Ming T. FPGA implementation of a wavelet neural network with particle swarm optimization learning. Mathematical and Computer Modelling. 2008;47:982-996\n'},{id:"B16",body:'\nGaviphat L. Adaptive self-tuning neuro wavelet network controllers [doctor of philosophy thesis in Electrical Engineering]. Virginia Polytechnic Institute and State University; March 1997\n'},{id:"B17",body:'\nBhowmik PS, Pradhan S, Prakash M, Roy S. Investigation of wavelets and radial basis function neural network for incipient fault diagnosis in induction motors. In: IEEE International Conference on Circuits, Controls and Communications (CCUBE); December. 2013. pp. 1-5\n'},{id:"B18",body:'\nShouxin R, Ling G. Application of a wavelet packet transform based radial basis function neural network to analyze overlapping spectra. In: IEEE Congress on Image and Signal Processing; China; May. 2008. pp. 228-232\n'},{id:"B19",body:'\nHamza MI. A wavelet network control scheme for path tracking of mobile robot [master thesis in Electrical Engineering]. University of Basrah, Aug. 2013\n'},{id:"B20",body:'\nSung Jin Y, Park JB, Choi YH. Direct adaptive control using self recurrent wavelet neural network via adaptive learning rates for stable path tracking of mobile robots. In: IEEE American Control Conference, Vol. 1. 2005. pp. 288-293\n'},{id:"B21",body:'\nHameed WI, Saleh AL, Sawadi BA, Al-Yasir YIA, Abd-Alhameed RA. Maximum power point tracking for photovoltaic system by using fuzzy neural network. Inventions. 2019;4:33\n'},{id:"B22",body:'\nObed AA, Saleh AL. Speed control of BLDC motor based on recurrent wavelet neural network. Iraqi Journal of Electrical and Electronic Engineering. 2014;10(2):118-129\n'},{id:"B23",body:'\nSaleh AL, Obaid BA, Obed AA. Motion control of linear induction motor based on optimal recurrent wavelet neural network-PID controller. International Journal of Engineering & Technology. 2018;7(4):2028-2034\n'},{id:"B24",body:'\nObed AA, Saleh AL, Kadhim AK. Speed performance evaluation of BLDC motor based on dynamic wavelet neural network and PSO algorithm. International Journal of Power Electronics and Drive System (IJPEDS). 2019;10(4):1742-1750\n'},{id:"B25",body:'\nMehdi N, Hossein N, Malihe M. A PSO-based optimum design of PID controller for a linear brushless DC motor. Proceedings of World Academy of Science, Engineering and Technology. 2007;20:211-215\n'},{id:"B26",body:'\nReddy MB, Obulesh YP, Raju SS. Particle swarm optimization based optimal power flow for volt-var control. ARPN Journal of Engineering and Applied Sciences. 2012;7:20-25\n'},{id:"B27",body:'\nEffatnejad R, Bagheri S, Farsijani M, Talebi R. Economic dispatch with particle swarm optimization and optimal power flow. International Journal on “Technical and Physical Problems of Engineering” (IJTPE). 2013;5:9-16\n'},{id:"B28",body:'\nMohammed HJ, Abdullah AS, Ali RS, Abd-Alhameed RA, Abdulraheem YI, Noras JM. Design of a uniplanar printed triple band-rejected ultra-wideband antenna using particle swarm optimisation and the firefly algorithm. IET Microwaves, Antennas and Propagation. 2016;10(1):31-37\n'},{id:"B29",body:'\nPortillo AA, Michael F, Chunjiang Q. Particle swarm optimization for PID tuning of a BLDC motor. IEEE International Conference on Systems, Man, and Cybernetics; October. 2009. pp. 3917-3922\n'},{id:"B30",body:'\nSivanandam SN, Visalakshi P, Bhuvaneswari A. Multiprocessor scheduling using hybrid particle swarm optimization with dynamically varying inertia. International Journal of Computer Science & Applications. 2007;4:95-106\n'},{id:"B31",body:'\nSoni YK, Rajesh B. BF-PSO optimized PID controller design using ISE, IAE, IATE and MSE error criteria. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET). 2013;2:2333-2336\n'},{id:"B32",body:'\nMohammed HJ, Abdullah AS, Ali RS, Abdulraheem YI, Abd-Alhameed RA. Performance comparison of particle swarm optimization, and genetic algorithm in the design of UWB antenna. Journal of Telecommunications. 2014;27(2):22-26\n'},{id:"B33",body:'\nTaeib A, Ltaeif A, Chaari A. A PSO approach for optimum design of multivariable PID controller for nonlinear systems. In: International Conference on Control, Engineering & Information Technology (CEIT’13) Proceedings Engineering & Technology. Vol. 2. 2013. pp. 206-210\n'},{id:"B34",body:'\nMohammed HJ et al. Evaluation of genetic algorithms, particle swarm optimization, and firefly algorithms in antenna design. In: 2016 13th International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design (SMACD), Lisbon. 2016. pp. 1-4\n'},{id:"B35",body:'\nSharaf AM, El-Gammal AAA. A novel particle swarm optimization PSO tuning scheme for PMDC motor drives controllers. In: IEEE International Conference on Power Engineering, Energy and Electrical Drives; March. 2009. pp. 134-139\n'},{id:"B36",body:'\nXie W, Wang J-S, Wang H-B. PI controller of speed regulation of brushless DC motor based on particle swarm optimization algorithm with improved inertia weights. In: Mathematical Problems in Engineering. 2019. pp. 1-12\n'},{id:"B37",body:'\nSaleh AL, Mohammed MJ, Kadhim AS, Raadthy HM, Mohammed HJ. Design fuzzy neural petri net controller for trajectory tracking control of mobile robot. International Journal of Engineering & Technology. 2018;7(4):2256-2262\n'},{id:"B38",body:'\nSaleh AL, Obed AA, Al-Yasir YIA, Elfergani ITE, Rodriguez J, Clarke RW, et al. Anti-windup scheme based on 2DOF-PIλDμ controller for velocity tracking of linear induction motor. International Transactions on Electrical Energy Systems. 2019;40:1-17\n'},{id:"B39",body:'\nObed AA, Kadhim AK. Multi-resolution wavelet PID speed and current controllers of BLDC motor based on invasive weed optimization technique. International Journal of Applied Engineering Research. 2018;13(8):6234-6243\n'}],footnotes:[],contributors:[{corresp:null,contributorFullName:"Ameer L. Saleh",address:null,affiliation:'
Department of Electrical Engineering, College of Engineering, University of Misan, Iraq
'},{corresp:null,contributorFullName:"Adel A. Obed",address:null,affiliation:'
Department of Electrical Engineering Technical College, Middle Technical University, Iraq
'},{corresp:null,contributorFullName:"Hamza H. Qasim",address:null,affiliation:'
Department of Communication Engineering, Universiti Tun Hussein Onn Malaysia, Malaysia
Faculty of Engineering and Informatics, University of Bradford, UK
'},{corresp:null,contributorFullName:"Raed A. Abd-Alhameed",address:null,affiliation:'
Faculty of Engineering and Informatics, University of Bradford, UK
'}],corrections:null},book:{id:"9893",title:"Automation and Control",subtitle:null,fullTitle:"Automation and Control",slug:null,publishedDate:null,bookSignature:"Prof. Constantin Volosencu, Dr. Serdar Küçük, Dr. José Guerrero and Dr. Oscar Valero",coverURL:"https://cdn.intechopen.com/books/images_new/9893.jpg",licenceType:"CC BY 3.0",editedByType:null,editors:[{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}}},profile:{item:{id:"115861",title:"Dr.",name:"Salvador",middleName:null,surname:"De Las Heras Jimenez",email:"delasheras@mf.upc.edu",fullName:"Salvador De Las Heras Jimenez",slug:"salvador-de-las-heras-jimenez",position:null,biography:null,institutionString:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",totalCites:0,totalChapterViews:"0",outsideEditionCount:0,totalAuthoredChapters:"1",totalEditedBooks:"0",personalWebsiteURL:null,twitterURL:null,linkedinURL:null,institution:null},booksEdited:[],chaptersAuthored:[{title:"Expert System Design for Sewage Treatment Plant",slug:"expert-system-design-for-sewage-treatment-plant",abstract:null,signatures:"J. Bouza-Fernandez, G. Gonzalez-Filgueira, S. de las Heras Jimenez and D.Vazquez-Gonzalez",authors:[{id:"112997",title:"Dr.",name:"Javier",surname:"Bouza Fernandez",fullName:"Javier Bouza Fernandez",slug:"javier-bouza-fernandez",email:"jbf@udc.es"},{id:"115857",title:"Dr.",name:"Gerardo",surname:"González-Filgueira",fullName:"Gerardo González-Filgueira",slug:"gerardo-gonzalez-filgueira",email:"gerardo.gonzalez@udc.es"},{id:"115861",title:"Dr.",name:"Salvador",surname:"De Las Heras Jimenez",fullName:"Salvador De Las Heras Jimenez",slug:"salvador-de-las-heras-jimenez",email:"delasheras@mf.upc.edu"},{id:"115862",title:"Mr.",name:"Diego",surname:"Vazquez-Gonzalez",fullName:"Diego Vazquez-Gonzalez",slug:"diego-vazquez-gonzalez",email:"dvg103@hotmail.com"}],book:{title:"Emerging Informatics",slug:"emerging-informatics-innovative-concepts-and-applications",productType:{id:"1",title:"Edited Volume"}}}],collaborators:[{id:"81490",title:"Mrs.",name:"Eva",surname:"Pajorová",slug:"eva-pajorova",fullName:"Eva Pajorová",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Institute of Informatics",institutionURL:null,country:{name:"Slovakia"}}},{id:"107110",title:"Prof.",name:"Shah Jahan",surname:"Miah",slug:"shah-jahan-miah",fullName:"Shah Jahan Miah",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/107110/images/2057_n.jpg",biography:"Dr Shah J. Miah is a Lecturer in Information Systems (IS) of the School of Management and Information Systems at the Victoria University, Melbourne, Australia. 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UK Research and Innovation (former Research Councils UK (RCUK) - including AHRC, BBSRC, ESRC, EPSRC, MRC, NERC, STFC.) Processing charges for books/book chapters can be covered through RCUK block grants which are allocated to most universities in the UK, which then handle the OA publication funding requests. It is at the discretion of the university whether it will approve the request.)
UK Research and Innovation (former Research Councils UK (RCUK) - including AHRC, BBSRC, ESRC, EPSRC, MRC, NERC, STFC.) Processing charges for books/book chapters can be covered through RCUK block grants which are allocated to most universities in the UK, which then handle the OA publication funding requests. It is at the discretion of the university whether it will approve the request.)
Wellcome Trust (Funding available only to Wellcome-funded researchers/grantees)
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