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Mathematical Problems in Engineering
Volume 2019, Article ID 5765390, 13 pages
https://doi.org/10.1155/2019/5765390
Research Article

Synchronization of Synchronous Reluctance Motors Using the Discrete Sliding Mode Control Technique

1Department of Electrical Engineering, Kuwait University, P.O. Box 5969, Safat 13060, Kuwait
2Department of Mathematics, Kuwait University, P.O. Box 5969, Safat 13060, Kuwait

Correspondence should be addressed to Mohamed Zribi; moc.liamg@01ibirz.demahom

Received 30 July 2019; Revised 30 September 2019; Accepted 21 October 2019; Published 4 December 2019

Academic Editor: Haiyan Lu

Copyright © 2019 Salahuddin Abdul Rahman et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The synchronous reluctance motor (SynRM) drive system is known to exhibit chaotic behavior under specified conditions. In this paper, the discrete-time sliding mode control (DSMC) technique is used to synchronize two SynRMs starting from different sets of initial conditions. The mixed variable speed reaching law is adopted in the design of the controller scheme. The parameters of the designed control scheme are tuned using a genetic algorithm (GA). Simulation results are presented to demonstrate the effectiveness of the proposed controller. In addition, the performance of the proposed control scheme is studied through simulations when bounded disturbances and mismatches between the parameters of the systems and those of the control scheme exist. The simulation results show that the designed control scheme is robust to bounded external disturbances and to mismatches in the parameters of the systems.

1. Introduction

Chaos theory is related to the study of the behavior of dynamical systems that exhibit sensitive dependence on initial conditions. The early discovery of chaotic systems goes back to the work of Poincare [1] during his attempt to prove the stability of the solar system through his work on the restricted three-body problem. A major contribution of chaos theory goes to Lorenz in his work on weather prediction [2]. These days, chaos can be found in different branches of science including mechanics [3], biology [4], chemistry [5], electronics [6], optics [7], and secure communication [8].

The importance of investigating chaos in electric motors comes from its direct applications in many areas of real life. For example, electric motors play important roles in industrial machinery, electrical locomotives, and electrical submersibles thruster drives [9]. The work of Kuroe and Hayashi [10] in the late 1980’s addressed the occurrence of chaos in motor drives. After that time, many researchers worked on chaos, and its control and its synchronization in several types of motor drive systems.

Chaos was investigated in the brushless DC motor (BLDCM) [11, 12], permanent magnet synchronous motor (PMSM) [1315], stepper motor (SM) [16], induction motor (IM) [17, 18], switched reluctance motor (SRM) drive, and synchronous reluctance motor (SynRM) [1921].

Chaotification and chaos synchronization were utilized in some industrial applications. Chaos synchronization is important when two or more electric motor drives need to work in synchrony. Chaotification of electric motor drives was applied in several industrial applications. For example, chaotification of DC motors was applied for the purpose of industrial mixing by Ye and Chau in [22, 23]. The inherent nature of stretching and folding of chaos helped in improving the efficiency of mixing. Also, it was shown that an abrasive machine using chaotic rotation is superior to a machine using periodic waves in raising abrasive efficiency [24]. Other industrial applications of chaotic motor drives can be found in vibratory compaction [25], dishwashers [26], air-conditioners [27], etc.

The variable-reluctance motor (VRM) is divided into two types, namely, the SRM and the SynRM. While the SRM uses rectangular waves and concentrated windings, the SynRM adopts sinusoidal waves and distributed windings. Therefore, the SynRM is capable of eliminating torque pulsation and acoustic noise problems that are intractable in the SRM. Also, the SynRM can offer the capability of electrical fault tolerance, such as one-phase open circuit, one-phase short circuit, and phase-to-phase short circuit [20].

SRM drives and SynRMs were shown to exhibit chaotic behaviors by Chau et al. [19] and Gao and Chau [20], respectively. A modeling, analysis, and experimentation of chaos in a switched reluctance drive system are presented in the work of Chau and Chen [28].

Controlling chaotic systems is a challenging problem because of the sensitivity of these systems to perturbations. Many control techniques were proposed to control chaos in SRM drives and SynRMs. For example, a passive adaptive control of chaos in a SynRM is presented in the work of Wei and Luo [29]. Tang et al. [30] designed a controller for the purpose of stabilization of a SynRM. Also, chaos is controlled in a SynRM using a nonlinear feedback control technique via control of the d-axis and the q-axis components of the stator voltage in [31]. However, less work has been done on the chaos synchronization of SynRMs. Because of its wide industrial applications, chaos synchronization of electric motors problem has been extensively studied in the literature. Chaos synchronization has been investigated in IM [32], BLDCM [33], PMSM [34], and others. This motivates us to deal with the chaos synchronization problem of SynRMs in this paper since SynRM takes advantage of structure simplicity, power efficiency, and the core and winding high recyclability [20].

With the development of nonlinear control theory, the sliding mode control technique became an effective method to control nonlinear systems and to achieve the synchronization of chaotic and hyperchaotic systems [35]. The sliding mode control technique represents a powerful and robust technique [3639]. Also, since discrete-time systems are easier to implement and using computers and DSP chips to implement controllers of discrete-time systems has become very common and simple [40, 41], a discrete sliding mode control technique will be adopted in this work. Moreover, the mixed variable speed reaching law [42] which can overcome the chattering drawback of the well-known exponential reaching law [43] is used to further enhance the performance of the designed controllers.

Another problem that control designers are faced with is how to tune the parameters of a controller so that a good performance is obtained. Some researchers have used optimization techniques to tune the control parameters. Nature-inspired optimization algorithms have become increasingly common in solving optimization problems in recent years [44]. These algorithms include two types of methods: evolutionary algorithms (EAs) [45] and swarm intelligence (SI) algorithms [46]. EAs are inspired by the principles of population genetics and natural selection. Examples of evolutionary algorithms include genetic algorithms (GAs) [47], genetic programming [48], and others [49, 50]. Swarm Intelligence algorithms are founded on the principles of collective behavior observed in biological systems. Examples of SI algorithms are the cuckoo search (CS) [51], particle swarm optimization (PSO) [52], and others [53, 54]. In the literature, EAs and SI algorithms were successfully applied to the offline design of controllers. They have been used to tune the parameters of controllers [55, 56] or to obtain the structure of the controller [57], or both [58].

In this paper, the chaotic behavior in the SynRM is investigated. The DSMC technique is used to design controllers for the purpose of synchronization. The contributions of this paper can be summarized as follows: (i) chaos is investigated in the SynRM and chaotic behavior is verified by calculating the Lyapunov exponents of the system, (ii) the problem of chaos synchronization is formulated, and the DSMC technique is used to design the controllers to synchronize two SynRMs starting from different sets of initial conditions, (iii) the mixed variable speed reaching law is adopted to overcome the drawbacks of the other reaching laws, and (iv) a genetic algorithm is used to tune the parameters of the designed controller to guarantee the optimum choice of parameters so that a predefined index is minimized. Simulation results are presented to validate the designed controllers. In addition, the robustness of the designed controllers against external disturbances and parameters mismatch is verified using simulations.

The remainder of the paper is organized as follows: the model of the SynRM is described in Section 2. Section 3 formulates the synchronization problem of two SynRM chaotic systems. The design of a discrete sliding mode control scheme to synchronize the two SynRM systems is presented in Section 4. In Section 5, a robustness study of the designed controller scheme is presented. Finally, the conclusion is given in Section 6.

2. Model of the Synchronous Reluctance Motor

A detailed study of chaos in the SynRM was performed by Gao and Chau in [20]. By using field-oriented control and considering the feedback effect , the continuous-time model of the SynRM can be presented as follows:

In the above model, the states of the system are the d-axis stator current , the q-axis stator current , and the mechanical rotor speed . The parameters of the system are the number of poles P, the electrical rotor speed , the reference speed of the rotor , the proportional gain of the speed controller , the d-axis stator voltage , the stator resistance per phase , the d-axis stator inductance , the q-axis stator inductance , the viscous friction coefficient B, the moment of inertia of the drive system J, and the load torque .

Gao and Chau [20] and Chau and Wang [9] simplified the above model by adopting a transformation and to such thatwhere , , and .

Then, the system given by equation (1) can be written as follows:where , , , , , and .

It was shown in [20] that the above system behaves chaotically for the following choice of the parameters: , , and .

A discretized version of the system given by (3), using a first-order approximation (Euler method), is as follows:

The states of the above system are defined as follows:

Therefore, system (4) can be written as

We simulated system (6) for with parameters , , and and with time step set to 0.001 seconds. These parameters result in a chaotic behavior of the system. The Lyapunov exponents of the system were calculated and we found them to be , , and . Since the signs of the Lyapunov exponents are , then the system is chaotic. The initial conditions are taken to be , , and .

The simulation results of the chaotic system (6) are presented in Figures 14. Figures 13 show the states of the system. Figure 4 shows the plots of the strange attractor of the SynRM.

Figure 1: The state versus time.
Figure 2: The state versus time.
Figure 3: The state ω versus time.
Figure 4: Plots of the strange attractor of the SynRM.

Therefore, the chaotic behavior of the synchronous reluctance motor is evident in the above figures.

3. Problem Formulation

This paper deals with the synchronization of two synchronous reluctance motors. Therefore, we define the master system as follows:

Also, the slave system is defined as follows:where and are the controllers to be designed such that the master and the slave systems are synchronized.

The error vector is defined as follows:

Using equations (7)–(9), the error dynamics can be written as follows:

The objective of this paper is to synchronize the master and the slave systems by forcing the state vector of the slave system (8) to track the state vector of the master system (7), i.e., . This synchronization is done by forcing the error to converge to as . The next section deals with the design of a sliding mode controller to synchronize the master and the slave systems.

4. Sliding Mode Control Design

4.1. Design of the Sliding Mode Controller

The error model system given by (10) is considered. The first step in the design of a sliding mode control is to choose the sliding surfaces. Since the system has two control inputs, we need to choose two sliding surfaces. The sliding surfaces and are chosen such that

Also, define the sign function such that

The subsequent development requires that we define the concept of quasisliding mode (QSM) and the concept of quasisliding mode band (QSMB). These concepts were defined in [43]. The quasisliding mode (QSM) is defined as the motion of the system in an ε vicinity of the sliding surface . The width of the band which contains the QSM is called the quasisliding mode band (QSMB); it is defined such that where is the width of the band.

Let the positive control parameters be , , , , , , and such that and .

Also, define the 1 norm of the error such that .

The following theorem gives the first result of the paper.

Theorem 1. The sliding mode control law:when applied to the error model given by (10) guarantees the convergence of the errors , , and to zero as .

Proof. Using the sliding surface given by (11), using the dynamical model of the errors given by (10), and using the control laws given by (14), we obtainAlso, using the sliding surface given by (12), using the dynamical model of the errors given by (10), and using the control laws given by (15), we obtainEquations (16) and (17) can be written as follows:Equation (18) corresponds to the mixed variable speed reaching law defined in [42]. Note that equation (18) satisfies Sarpturk et al. reaching and existence condition given by ; this condition can be decomposed into the following two inequalities [59]:Condition (19) implies that the closed-loop system should be moving in the direction of the sliding surface. On the contrary, condition (20) implies that the closed-loop system is not allowed to go too far in that direction. Thus, conditions (19) and (20) define the lower bound and the upper bound for the control action, respectively.
The above two inequalities can be proven as follows:when the sampling time is very small, outside the QSMB. Using inequalities (21) and (22), it follows that equation (18) satisfies the condition outside the QSMB.
On the sliding surface, , we haveAlso, on the sliding surface, , we haveSince and converge to zero, then the third difference equation in the error model given by (10) reduces toThe solution of the above equation is . Since , the error converges to zero as . Therefore, the errors , , and converge to zero as .

The convergence of the errors , , and to zero as guarantees that the states of the slave systems , , and converge to the states of the master systems , , and , respectively, as . Hence, the states of the master and the slave chaotic systems are synchronized.

The proposed sliding mode control scheme contains six control parameters. These parameters are , and . A good performance can be achieved by fine tuning the parameters of the controllers and . The process of tuning the parameters of the controllers is not an easy process since multiple parameters must be tuned simultaneously and each tuning must start by repeating the simulations. For the simulation studies, we considered two scenarios. First, we chose the parameters of the controllers manually. Then, we used a genetic algorithm to select the parameters of and . The next subsection briefly describes the algorithm used for tuning the parameters of the controllers.

Remark. The parameters of the controllers are , and . For a chosen set of parameters, the controllers and can be easily computed using equations (14) and (15). Hence, it can be concluded that the proposed discrete sliding mode controller involves low computational complexity.

4.2. Sliding Mode Control Parameters Tuned by a Genetic Algorithm

The performance of the closed-loop system is determined by the parameters of the controller: , and . It is important to choose their values using an optimization algorithm. In the literature, different cost functions were used to optimize certain cost function. These cost functions evaluate the performance of the controller. Examples of the commonly used cost functions include the integral square error (ISE) [60], the integral time absolute error (ITAE) [61], the weighted sum of the absolute errors [62], and others [63]. Also, the multiobjective functional optimization was used where several performance measures are optimized [64, 65]. Among the most commonly used cost functions is the quadratic cost function [66]. This cost function is used in this study because it is simple and it guarantees a good performance of the controller. Hence, the GA is adopted to optimize the parameters of the control scheme with respect to the following performance index:where “N” is the total number of samples which is chosen to be 8000. The matrices Q and R are weighted coefficient matrices that are chosen such as (3 by 3 identity matrix) and . The methodology for the adjustment of the parameters of the sliding mode controllers using the genetic algorithm is described in [67].

The parameters of the GA are listed in Table 1.

Table 1: GA parameters.

The parameters of the controller obtained by the end of the genetic algorithm tuning process are , , , , , and .

4.3. Simulation Results

The designed sliding mode control laws in (14) and (15) are applied to the master system given by equation (7) and the slave system given by equation (8). The performance of the controlled error system is simulated using the MATLAB/Simulink software. The parameters of the system are taken as [20] , , , , , , , , and . In the simulation, the initial states of the master system are taken to be , , and . The initial states of the slave system are taken to be , , and .

The performance of the system is simulated for 100 seconds. For the first , the error system is simulated when . Then, for the next , the controller u is applied.

Moreover, chattering is suppressed by replacing the discontinuous sign function in the control law with the saturation function such that where δ is an arbitrarily small positive scalar which can be designed to get suitable approximation. The simulations were carried for two sets of the parameters of the controllers. The first simulation was done when the parameters are chosen manually. The second simulation was done when the parameters are determined by the genetic algorithm. The parameters of the controllers are given in Table 2.

Table 2: Controller parameters and performance.

The simulation results are depicted in Figures 517.

Figure 5: The states and versus time when using the sliding mode controller with manual tuning of the parameters.
Figure 6: The states and versus time when using the sliding mode controller with GA tuning of the parameters.
Figure 7: The states and versus time when using the sliding mode controller with manual tuning of the parameters.
Figure 8: The states and versus time when using the sliding mode controller with GA tuning of the parameters.
Figure 9: The states and versus time when using the sliding mode controller with manual tuning of the parameters.
Figure 10: The states and versus time when using the sliding mode controller with GA tuning of the parameters.
Figure 11: The error versus time when using the sliding mode controller.
Figure 12: The error versus time when using the sliding mode controller.
Figure 13: The error versus time when using the sliding mode controller.
Figure 14: The controller versus time.
Figure 15: Zoomed in part of the controller versus time.
Figure 16: The controller versus time.
Figure 17: Zoomed in part of the controller versus time.

The synchronized states versus time when using the manual tuning of the parameters are shown in Figures 5, 7, and 9. The synchronized states versus time when using the GA-based tuning of the parameters are shown in Figures 6, 8, and 10. The errors , , and versus time are depicted in Figures 1113. It is clear from these figures that the errors converge to zero. The simulation results indicate that the proposed control scheme works well. However, it is clear that better results were obtained when using parameters which are tuned using a genetic algorithm. This is especially true when one considers the simulation results of versus time.

The controllers versus time are presented in Figures 1417. Again, better results were obtained when using parameters of the controller which are tuned using a genetic algorithm.

Therefore, it can be concluded that the proposed controller works well for both sets of parameters. However, a better performance is obtained when using parameters of the control scheme which are tuned using a genetic algorithm.

5. Robustness Studies

In this section, we will study the robustness of the proposed control scheme. The robustness will be studied for two different cases. The first case will test the robustness to bounded external disturbances. The second case will test the robustness to model uncertainties.

5.1. Case 1: Robustness to External Disturbances

In this case, the effects of bounded external disturbances are added to the slave system. So the equations of the slave system after the addition of the disturbances are as follows:where , , and represent the disturbances. Moreover, it is assumed that these disturbances are bounded with their bounds being known. If a bound on the disturbances is not available, then a disturbance estimator can be used.

In the simulations, the disturbances are taken to be

Figure 18 depicts the errors , , and versus time when using the proposed sliding mode control scheme and when disturbances are present in the model of the slave system. The simulation results show that the errors converge to zero, and hence, it can be concluded that the proposed control scheme works well and it is able to suppress the effect of bounded external disturbances.

Figure 18: The errors , , and versus time when using the sliding mode controller with disturbances.
5.2. Case 2: Robustness to Mismatches in the Parameters of the Systems and Those of the Controllers

For the second case, we will assume that there is a mismatch between the parameters and of the motors and the parameters and of the controllers. The parameters and of the controllers will be denoted as and such that and . The sliding mode controllers (14) and (15) with the parameters and become

The sliding mode controllers given by (29) and (30) are applied to the chaotic synchronous motors given by (7) and (8) for and . The initial conditions and the other parameters of the controllers are the same as in the previous subsection.

Figure 19 depicts the errors , , and versus time when using the proposed sliding mode control scheme and when there is a mismatch between the parameters of the systems and those of the controllers. The simulation results show that the errors converge to zero, and hence, it can be concluded that the proposed control scheme works well and it is able to suppress the effect of mismatches in the parameters of the systems with those of the controllers.

Figure 19: The errors , , and versus time when using the sliding mode controller with mismatch in the parameters of the systems and those of the controller.

Hence, it can be concluded that Figures 18 and 19 indicate that the proposed control scheme works well when there are bounded disturbances acting on the slave system and when there are mismatches between the parameters of the controller and those of the motors systems. Thus, the simulation studies indicate that the developed sliding mode control scheme is robust with respect to disturbances and with respect to mismatches between the parameters of the controllers and those of the motor systems.

6. Conclusion

In this paper, the discrete sliding mode control technique is used to design a robust synchronization control scheme for two chaotic synchronous reluctance motors. It is shown that the proposed control scheme guarantees the convergence of the state of the slave system to the states of the master system. The proposed control scheme is simulated using MATLAB using manually chosen control parameters and control parameters which are determined by using a genetic algorithm. The simulation results clearly show the convergence of the errors to zero which validate the proposed control design. Moreover, the simulation results indicate that better results were obtained when using control gains determined by a genetic algorithm. In addition, the simulation studies indicate that the proposed controller is robust to bounded disturbances and to mismatches in the parameters of the systems with those used in the control scheme.

Future work will address the design of observer-based controllers to synchronize chaotic synchronous reluctance motors. Other avenues for further research include the design of second-order sliding mode controllers as well as fuzzy controllers for chaotic synchronous reluctance motors.

Data Availability

The manuscript contains theoretical studies and simulation studies. All the data are available in the manuscript.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

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