Overlaps between sectors
Abstract
In this chapter, a new method for stator resistance compensation in direct torque control (DTC) drives, based on neural networks, is presented. The estimation of electromagnetic torque and stator flux linkages using the measured stator voltages and currents is crucial to the success of DTC drives. The estimation is dependent only on one machine parameter, which is the stator resistance. Changes of the stator resistances cause errors in the estimated magnitude and position of the flux linkage and therefore in the estimated electromagnetic torque. Parameter compensation by means of stator current phasor error has been proposed in literature. The proposed approach in this chapter is based on a principle that states the error between the measured current magnitude of the stator feedback and the stator’s command, verified with neural network, is proportional to the variation of the stator resistance and is mainly caused by the motor temperature and the varying stator frequency. Then the correction value of stator resistance is achieved by means of a fuzzy controller. For the first time, a combination of neural control and fuzzy control approach in stator resistance variations based on the stator current is presented. The presented approach efficiently estimates the correct value of stator resistance.
Keywords
 Fuzzy direct torque control
 neural compensation
 induction motor drives
1. Introduction
The direct torque control is one of the excellent control strategies available for torque control of induction machine. It is considered as an alternative to field oriented control (FOC) technique [1]. In fact, among all control methods for induction motor drives, direct torque control (DTC) seems to be particularly interesting being independent of machine rotor parameters and requiring no speed or position sensors [2].
A basic concept of direct torque control of induction motor drives is simultaneous control of the stator flux and electromagnetic torque of a machine. Compared to the conventional vectorcontrolled drives, the torque and flux of a DTCbased drive are controlled in a closedloop system that does not use the current loops.
In principle, DTCbased drives require only the knowledge of stator resistance and thereby decrease the associated sensitivity to parameter variations [3, 4]. Moreover, compared to the conventional vectorcontrolled drives, DTCbased drives do not require fulfilling the coordinate transformation between stationary and synchronous frames. Depending on how the switching sectors are selected, two different DTC schemes become possible [5].
Since a DTCbased drive selects the inverter switching states using a switching table, neither the current controllers nor the pulsewidth modulation (PWM) modulator is required. As a result, the DTCbased drive provides a fast torque response [6]. The conventional direct torque control (CDTC) suffers from some drawbacks such as high current, flux and torque ripple, difficulties in torque, and flux control at very low speeds [7]. However, the switchingtablebased DTC approach has some disadvantages. If the switching frequency of the inverter is not high, the torque and flux pulsation could be high; moreover, there would be a sluggish response during the startup or change of the reference flux or reference torque [8]. Hence, to improve the performance of the DTC drive during the startup or changes in the reference flux and torque, a fuzzylogicbased switchingvector process is developed in this chapter [9–15]. In DTC drives, the feedback of the electromagnetic torque and stator flux linkage is used as the input of controller. Using the measured stator currents and voltages, the electromagnetic torque and also stator flux linkages are estimated in stator reference frames [16, 17]. “The machine model is only dependent on stator resistance” [18]. There are different forms of direct torque control induction motor based on how currents and voltages are measured or estimated [19–21]. The stator current might be obtained using only the DClink current sensor, and the motor line voltages could be reconstructed inexpensively using gate signals [22]. Nevertheless, all the measured values suffer from precision and lowspeed operational problems caused by errors induced by the varying stator resistance in the flux and its angle calculator [23, 24]. The stator resistance change has a wide range, varying from 0.75 to 1.7 times the stator’s nominal value. The variation is largely due to temperature variations, and to a small extent, due to the stator frequency variations [21]. The variation deteriorates the drive performance by introducing errors in the estimated magnitude and position of the flux linkage and therefore in the electromagnetic torque estimation, particularly at low speeds [25]. Note that at low speeds, the voltage drops of the stator resistance constitute a significant portion of the applied voltages. Only a few control schemes have been proposed so far for overcoming the mentioned parameter sensitivity (which restricts the speed control range of the drives). The stator resistance has problems such as convergence and slowness of response. A partial operatingfrequencydependent hybridflux estimator has been proposed for tuning the stator resistance [10]. Adjustment of the stator resistance, based on the difference between the flux current and its command, has problems in identifying the actual flux current [26, 27]. Finding the stator resistance based on the steady state voltage has the shortcoming of using direct axis flux linkages that are affected by the stator resistance variations.
In this chapter, a neural network estimator is developed to find the reference stator current values at each moment. Later, the error difference between the measured and the real stator current values is fed to a fuzzy logic controller, which then outputs the correct stator resistance value.
2. Fuzzy logic direct torque control
In this section, the concept and principle of direct torque control approach of an induction motor is briefly introduced. A schematic diagram of the proposed drive is shown in Fig. 1. The feedback control of torque and stator flux linkages, which are estimated from the measured voltages and currents of the motor, is used in the proposed drive scheme. In this approach, statorreference frame model of the induction motor is used. To avoid the trigonometric operations faced in coordinate transformations of other reference frames, the same reference frame is used in the implementation [22]. This can be considered as one of the advantages of the control scheme. Through the integration of the difference between the phase voltage and the voltage drop in the stator resistance, Stator
And the flux linkage phasor is as follows:
The stator flux linkage phasor position is:
And the electromagnetic torque is given by:
According to Fig. 2, the inverter switching states are selected based on the errors of the torque and the flux (as indicated by
The optimum switching vector is selected to decrease the errors [23, 25, 26]. Using a fuzzylogicbased switchingvector selection process, it would be possible to improve the performance of the DTC drive during startup or changes in the reference flux and torque. For this, a Mamdani fuzzylogicbased system is used. Using the flux and torque deviation from reference ones and the position of the stator flux linkage space vector, it is possible to select different voltages. Then a rulebase has to be formulated based on these states. Thus the aim of the approach is to use a fuzzy logic system to expand the system performance (i.e., gives faster torque and flux response), outputs the zero and nonzero voltage switching states (






315–75  345–45  15–75  45–105  75–135  105–165 






135195  165225  195255  225285  255315  285345 




PL  1  2  2 
PS  1  2  3 
ZE  0  0  0 
NS  6  0  4 
NL  6  5  5 
Since it was assumed that there were three and five fuzzy sets for the flux error and the torque error, respectively, there will be 15 rules for every sector. Table 2 shows the various rules for sector 1. In particular, the stator flux error (
Rule 1: If
Rule 2: If
Rule 3: If
The goal of the fuzzy system is to obtain a crisp value (as the appropriate switching state) on its output. A general “
Rule
Thus, by using the minimum operation for the fuzzy
where
where
In this case, the outputs include crisp numbers, switching states, and for defuzzification, the maximum used criteria.
3. Stator resistance compensation
3.1. Scheme
A mismatch between the controllerset stator resistance and its actual value in the machine can create the instability shown in Fig. 6a. This figure shows the simulations for the changes of the step stator resistance from 100% to 50% of its nominal value at second 0.5. The drive system becomes unstable if the controllerinstrumented stator resistance is higher than its actual value in the motor [11]. An explanation for this could be as follows: when motor resistance decrease in machine and the applied voltage is the same, the current increases, resulting in increased flux and electromagnetic torque [28]. The opposite effect occurs in controller. In fact, by current increments, which are inputs of the system, the stator resistance voltage drops will increase in the calculator. Therefore, lower flux linkages and electromagnetic torque estimations will present. Compared with their command values, they give large torque and flux linkages deviations, which result in commanding larger voltages and currents and leading to a run off condition as shown in Fig. 6a. “The parameter mismatch between the controller and machine will result in a nonlinear relation between the torque and the torque’s reference, making it a nonideal torque amplifier” [29]. This will have undesirable effect in a torque drive and speedcontrolled drive systems. Therefore, it will be reasonable to design a motor resistance adaption law to overcome instability and to guarantee a linear torque amplifier in the DTC drive. A new approach is presented in the next section for stator resistance parameter adaption.
3.2. Stator current phasor command
A diagram of the applied stator resistance compensation is shown in Fig. 4. The presented technique is based on the principle that the error between the measured stator feedback currentphasor magnitude
A neural network estimator, presented in the following, is designed to evaluate the stator current command from the torque and stator flux linkage commands.
The stator feedback current phasor magnitude
The stator command current phasor magnitude
where
Then the
It can be shown that
Equation 14 gives two solutions for
It is shown here that evaluation of the stator current command is a complicated and timeconsuming process. Instead of using the numerical solution for the system, it is possible to perform the stator current command by using an artificial neural network (ANN) since it is known that ANN is a general nonlinear function estimator. As a result, a multilayer feedforward backpropagation ANN, whose inputs are the torque and flux reference values, is trained to estimate the stator current command. A 2881 structure, which has two hidden layers with 8 hidden nodes, is obtained by trial and error. The activation functions of the hidden layers are tansigmoid functions. Fig. 5 shows the structure of the ANN estimator. The neural estimator evaluates the reference stator current with less than 0.01% error. Furthermore, it is shown that more complicated ANN structures result in higher error rates.
4. Results
Dynamic simulations are performed to validate the performance of the proposed technique. The induction motor details, used in the simulation, are given in the appendix. Fig. 6a and 6b show the simulations for a step change in the stator resistance parameteruncompensated and compensated torque drive system respectively. The system controller has the nominal value of the stator resistance, and after half a second, the stator resistance is changed to ½ of its nominal value. Then the corresponding effects are studied. In the compensated system, it is observed that the estimation of stator resistance has experienced an initial transient state, and after a short time, it converges gradually to its final actual value in a steady state. The similar transitions are observed in other variables. However, all variables reach to their steady state situation. A step variation in the stator resistance is rather an extreme test and not a significant case encountered in practice. In real operating conditions, the temperature change rate is very slow and so is the stator resistance.
Stator flux linkages and the torque command are proportionally decreased and increased linearly from/to their original reference values. The tracking of motor variables and stator resistance is achieved, thus proving the effectiveness of the adaptive controller in the fluxweakening region. It also perfectly operates in stator resistance incremental case and in gradually stator resistance changes due to temperature changes. In these cases, there is not any oscillation even at the initial moments of resistance variations.
5. Conclusion
A fuzzy direct torquecontrolled drive was introduced, and an adaptive stator resistance compensation scheme was applied to a typical threephase induction motor. With this approach, the elimination of parameter sensitivity of the stator resistance by using only the existing stator current feedback occurred. The scheme was simple to implement, and its realization was indirectly dependent on stator inductances. Since the flux was controlled in the machine, the inductances used in the computation of stator phasor current command were constants. A procedure for finding the phasor command of the stator current from the torque and stator flux linkage commands was derived to realize the complication of this method. The ANN estimator was designed to effectively evaluate the reference stator current value. The scheme was verified via dynamic simulation for various operating conditions, including the fluxweakening mode. The scheme was successful despite rapid changes in the stator resistance, such as step changes. It was observed that the scheme adapted very well without transients even for simultaneous variations of the torque and flux linkages command while the stator resistance was varying. Finally, a simple fuzzy controller was used to generate the exact stator resistance value.
Variable  Symbol  Variable  Symbol 
Stator 

Stator command current phasor magnitude  
Stator 

Torque command  
Stator resistance  
Stator flux linkage command  
Stator 

Stator selfinductance  
Stator flux linkage phasor position  
Mutual inductance  M 
Motor torque  
Rotor selfinductance  
Number of poles 

6. Appendix
Nominal operation condition  
Rated power  4  kW 
Rated voltage  480  V 
Rated speed  3000  rpm 
Rated frequency  50  Hz 
Parameters of simulated induction motor  
Stator resistance  1.115  
Stator selfinductance  0.005974  H 
Mutual inductance  0.2037  H 
Friction factor  0.00051  N·m/s 
Rotor resistance  1.083  
Rotor selfinductance  0.005974  H 
Rotor inertia  0.0333  kg/m^{2} 
Number of poles  2 
Acknowledgments
The authors would like to thank the school of railway engineering in Iran University of Science and Technology for the great support of this project. The assistance of Mr. H. Zafari for revising this paper is appreciated as well.
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