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Variable, Fractional-Order PID Controller Synthesis Novelty Method

By Piotr Ostalczyk and Piotr Duch

Submitted: July 28th 2020Reviewed: November 25th 2020Published: December 28th 2020

DOI: 10.5772/intechopen.95232

Downloaded: 53


The novelty method of the discrete variable, fractional order PID controller is proposed. The PID controllers are known for years. Many tuning continuous time PID controller methods are invented. Due to different performance criteria there are optimized three parameters: proportional, integral and differentiation gains. In the fractional order PID controllers there are two additional parameters: fractional order integration and differentiation. In the variable, fractional order PID controller fractional orders are generalized to functions. Nowadays all PID controllers are realized by microcontrollers in a discrete time version. Hence, the order functions are discrete variable bounded ones. Such controllers offer better transient characteristics of the closed loop systems. The choice of the order functions is still the open problem. In this Section a novelty intuitive idea is proposed. As the order functions one applies two spline functions with bounded functions defined for every time subinterval. The main idea is that in the final time interval the variable, fractional order PID controller transforms itself to the classical one preserving the stability conditions and zero steady-state error signal. This means that in the last time interval the discrete integration order is −1 and differentiation is 1.


  • fractional-Calculus
  • PID controller
  • discrete system

1. Introduction

A continuous-time proportional–integral–derivative controller (PID controller) [1] invented almost 100 years ago is one of the most widely applied controllers in the closed-loop systems [2] with many industrial applications [3, 4, 5]. Currently the continuous-time control is successively replaced by discrete-time one in which the integration is replaced by a summation and differentiation by a difference evaluation. So, in the discrete PID controller the classical integral is replaced by a sum and the derivative by a backward difference, [6]. The discrete controller’s PID algorithm is mainly realized by micro-controllers [7].

At 70s of the 20-th Century the Fractional Calculus [8] with a great success started a considerable attention in mathematics and engineering [9, 10, 11, 12]. Now, the fractional-order backward-difference (FOBD) and the fractional-order backward sum (FOBS) [6, 13] are applied in the dynamical system modeling [14] and discrete control algorithms. The continuous-time FOPID controllers are more difficult in a practical realization [15, 16, 17, 18].

There are numerous continuous and discrete-time PID and FOPID controller synthesis methods [16, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31]. One should mention that the optimisation of the closed-loop system in this case is more complicated because of the controller optimization. Apart from the three classical gains there are two additional parameters, namely, a fractional order of differentiation and summation [32]. The FOPID control characterizes by slow achieving the steady state and growing calculation “tail” [12].

In the paper a novelty variable, the fractional-order PID (VFOPID) [6, 28, 33, 34, 35, 36, 37, 38, 39, 40, 41] controller synthesis is proposed. It consists of dividing the closed-loop system discrete-transient time division into the finite time intervals over which are defined fractional orders summation and differentiation functions. The main idea is that for the final infinite interval kL+the difference order equals 0 and the summation is !preserving quick reaching the zero steady state value. Thus, in the VFOPID control the disadvantages of FOPID are extracted. One should admit that in the FOPID or VFOPID control the microcontrollers are numerically loaded.

Fractional-orders systems are characterized by the so called system “memory”. This, in practice, means that in every step the FOPID controller computes its output signals taking into account step-by-step linearly computed number of samples. This causes in practice the micro-controllers realization problems. It is known as “Finite memory principle” [12].

The paper is organized as follows. In Section 2 the basic information related to the fractional calculus and variable, fractional order Grünwald-Letnikov backward difference is given. The main result of the paper includes Section 3. It contains the proposed VFOPID controller synthesis method with the proposal of the order functions form. The brief description of the controller parameters evaluation algorithm is given. The investigations are supported by a numerical example presented in Chapter 4.

2. Mathematical preliminaries

In the paper the following notation will be used. N0=0,1,2,3, Nl=ll+1l+2R+=0+. 0kwill denote the zero column vector of dimensions k+1×1whereas 0k,kis k+1×k+1zero matrix. Similarly will be denoted a k+1×k+1unit matrix 1k.

In general, a fractional-order functions will be denoted by Greek letters ν:N0Rνwhereas the integer orders will be denoted by Latin ones nR+. In practice, for lN0: 0<νl1. For k,lN0and a given order function νlthe function of two discrete variables k,lN0is defined by the following formula: aνlkas follows:

Definition 2.1. For k,lN0and a given order function νone defines the coefficients function of two 13 discrete variables as


One should mention that function (1) for νl=nl=constN0


The above function will be named as: the “oblivion function” or “decay function”.

2.1 Variable, fractional-order backward difference

Next one defines the Grünwald–Letnikov variable, fractional-order backward difference (VFOBD). For a discrete-variable bounded real-valued function fdefined over a discrete interval 0kthe VFOBD is defined as a sum (see for instance [6, 9]).

Definition 2.2. The VFOBD with an order function ν, with values νk01, is defined as a finite sum, provided that the series is convergent


Relating to (2) as the first special case of the defined above VFOBD and a constant order function νk=ν=constfrom (2.1) one gets the fractional-order backward difference (FOBD). The second special case is for a constant integer order function νk=ν=n=constwhere the integer-order backward difference (IOBD) is a classical one.

Equality (3) is valid for k,k1,k2,,k0+1,k0. Hence, one gets a finite set of equations. Collecting them in a vector matrix form one gets




2.2 Variable, fractional-order linear time-invariant difference equations

On the base of the Grünwald-Letnikov variable, fractional-order linear time-invariant backward-difference the difference Eqs. (GL-VFOBE) for i=1,2,,pand j=1,2,,qrepresenting discrete models of real dynamical systems or discrete control strategies are defined by the variable, fractional-order linear time-invariant difference equation (VFODE). h>0denotes the sampling time.


where mini, νni,lkνni,l1kνi,1kνi,0k=0, μmi,lkμmi,l11kμi,1kμi,0k0, ai,land bi,lare constant coefficients for l=0,1,,niand l=0,1,,mi, respectively. It is assumed that a0,n0=1.

According to the notation (5) Eq. (7) takes the form


The vector ujksatisfies the condition ujk=0kfor k<k0. In the general solution of (8) to the assumed ujkand initial conditions vector yi,k01=yi,k01yi,k02T(T denotes the transposition) must be taken into account with =k0<0k0k. Then, the infinite number of initial conditions (8) are formed in the following vector


and the combined Eq. (8) is of the form


or after simple transformation


2.3 Main assumptions

To preserve the VFOBDE order one assumes that


In the transfer functions defined by the one-sided Ztransform one assumes zero initial conditions. Following this assumption equality (11) simplifies to


Defining matrices


one gets


Under assumption (12) k0Dkνkis invertible, so for k0=0one can write




one gets similar to the transfer function description


or for simplicity


Remark 2.1. Though the relation (19) looks similar to the classical discrete transfer function it is different by the real discrete variables. It relates discrete SISO systems by vectors and matrices related to its dimensions k+1N0.

2.4 VFO linear system description

One considers a closed-loop system illustrated in Figure 1. Where a plant is described by (19) where ekhand ukh.

Figure 1.

Closed-loop system.

2.4.1 VFO_PID

The classical PID controller output is desribed by three terms


and in the convention proposed above as


which may be expressed as


where νCk,μCk0and controlling and error signals are denoted as ukand ek, respectively. Then, denoting.

Remark 2.2. The plant may be described by classical integer order, fractional or even variable, fractional - order difference equations. The matrix - vector description used makes it possible.


one gets a VFOPID controller transfer function-like description


To simplify the description one assumes a sensor matrix as


The closed-loop system is presented in Figure 1 from which one gets the following relations



  • rkh- a reference signal vector,

  • dkh- an external disturbance signal vector,

  • yokh- a plant output signal vector,

  • ykh- a closed-loop system output signal vector,

  • ekh- a closed-loop system error signal,

A system error is evaluated by the formula


3. Variable, fractional-order PID controller synthesis

In the synthesis of the classical PID controller there are three parameters to evaluate. Namely, K,KI,KDknown as the proportional, integral and differential gains. In the fractional-order PID controllers there are two additional parameters: the differentiation order νkhR+and the integration one μkhR+. In the variable, fractional-order PID controller the mentioned orders are generalized to functions. This means that there are three constant coefficients and two discrete variable functions to find


In the rejection of the external disturbation one can assume that rkh=0so Eq. (29) simplifies to


Usually the sensor matrix Hkhis treated as constant, by assumption that sensors do not introduce its own dynamics to the system. Hence, Hkh=H=const. It may be assumed that H=h01kor further, for h0=1, formula (30) takes a form


The optimal parameters (29) are evaluated due to the assumed optymality criterion. The most popular is so called ISE one (Integral of the Squared Error) or in the discrete-system case: Sum of the Squared Error (SSE).


Substitution of (31) into (32) gives


In the proposed VFOPID controller synthesis method with partially intuitive and supported by closed-loop systems synthesis experience the classical optimisation due to the performance criterion (32) is performed. The pre-defined differentiation and integration order functions orders are as follows




Every function νikhfor i=1,2,,Nand μikhfor i=1,2,,Mis characterized by a sets of parameters cijand dij, respectively.

In the classical closed-loop system with PID controller there is introduced the integration part preserving the steady - state error signal tending to zero. So, in (38) there is a constant order 1for kKMM+.

Now, for initially assumed order functions one applies the following algorithm based on well known Gauss method.

  1. Chose a starting set of coefficients KP,KI,KD, c11,and d11,,

  2. Applying the classical Gauss algorithm find a minimal SSE performance index value alongside the first variable (eg. K_P),

  3. Repeat step 2 for the next parameter,

  4. If the SSE value is satisfactory stop else return to step 2.

Remark 3.1. Algorithm described above can be applied also to the classical discrete PID controller with three parameters.

4. Numerical example

One considers a closed-loop system depicted in Figure 1.

A plant is described by a transfer function



  • a1=0.5

  • a0=0.1

  • b0=a0

The plant is discretized with the sampling time h=0.5and a VFOPID controller is applied


and controller gains KP,kI,KDand order function parameters d1,d2.

Hence, there are 5 parameters to evaluate. Due to the performance index (33) the optimal parameters are as follows

  • KP=1.000

  • Ki=0.514

  • KD=0.890

  • d1=0.35

  • d2=0.5

The VFOPID controller order functions are plotted in Figure 2 whereas the PID and VFOPID controllers unite step responses are given in Figure 3.

Figure 2.

VFOPID controller order functions: νkh (in black) and μkh (in red).

Figure 3.

VFOPID (in red) and IOPID (in black) controller unit step response.

The achieved VFOPID controller synthesis result is compared with the classical discrete-time PID controller optimized due to criterion (30). The optimal parameters are

  • KP=1.00

  • Ki=0.81

  • KD=0.90

Figure 4 contains the closed - loop systems with PID (in blue) and VFOPID (in red) controllers unit step responses. There is included a plant unit step response of the plant (in black.)

Figure 4.

The closed-loop system response with the VFOPIS (in red) and IOPID controllers (in blue).

In Figure 5 the controlling signals are presented (PID - in black, VFOPID - in red). The controlling signals have typical shapes: first differentiation action and finally the classical integration preserving zero steady - state closed - loop system error.

Figure 5.

The closed-loop controlling signals.

Remark 4.1. In the Numerical example proposed here the VFOPID and the classical PID controllers maximal control signal values are the same reaching assumed bounding value maxuIkl,maxuFkh=2.

Remark 4.2. In the Numerical example


5. Conclusions

One should emphasize that the proposed solution of the VFOPID controller do not guarantee the absolute optimum of the closed-loop control system synthesis. It proves that the proposal of a physically realizable VFOPID controller by micro-controller (with finite memory) leads to better results due to the assumed performance criterion.

The main idea of the proposed method is to assume a priori the order functions with unknown parameters. In the VFOPID controller synthesis essential is an assumption that the summation order equals 1 One can express the action as the assumption of skeleton order functions with unknown parameters evaluated further in an SSE optimization algorithm.

Here, it is worth mentioning that there are still open problems of the VFOPID controllers tuning.

  • One should define a program evaluating the order functions.

  • For evaluation of the VFOPID controller parameters one can apply another optimization methods. It seems that optimization methods based on the artificial intelligence will be very effective.

  • Another performance index may be applied. Some penalty functions may be introduced to SSE as well a term taking into account the minimal value of the error signal.


The work was supported by the National Science Center Poland by Grant no. 2016/23/B/ ST7/03686.


PIDproportional-integral-derivative controller
FOBDfractional-order backward difference
FOBSfractional-order backward sum
FOPIDfractional-Order proportional, fractional-order integral and differential controller
VFOPIDvariable, fractional-order PID controller
SSEsquared sum of the error

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Piotr Ostalczyk and Piotr Duch (December 28th 2020). Variable, Fractional-Order PID Controller Synthesis Novelty Method [Online First], IntechOpen, DOI: 10.5772/intechopen.95232. Available from:

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