Australian Journal of Basic and Applied Sciences. Design of Pid Controller For Switched Reluctance Motor Using Genetic Algorithm
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1 AENSI Journals Australian Journal of Basic and Applied Sciences ISSN: Journal home page: Design of Pid Controller For Switched Reluctance Motor Using Genetic Algorithm 1 Belsam Jeba Ananth. M, 2 Vennila. M, 3 Anuja. V. 1 Professor / DMI College of Engineering, Chennai, India. 2 Associate Professor / DMI College of Engineering, Chennai, India. 3 Assistant Professor / DMI College of Engineering, Chennai, India. A R T I C L E I N F O Article history: Received 19 August 2014 Received in revised form 19 September 2014 Accepted 29 September 2014 Available online 22 October 2014 Keywords: Switched Reluctance(SR) motor, proportional-integral derivative (PID), Genetic Algorithm (GA) A B S T R A C T This study presents a speed controller design for a switched reluctance (SR) motor in order to achieve minimum torque ripple and high control performance. First of all, SR motor convertor designed for soft chopping is chosen. This converter as well as producing less torque ripple, provides more degrees of freedom for SR motor drive controller. A PID controller and a switching algorithm for turn-on and turn-off degree of each phase of motor form speed control loop of SR motor drive. The primary parameters of controller are achieved by trial and error. But eventually an optimization algorithm to reach the goals and constraints in different set points is defined and its parameters are optimized with a Genetic Algorithm (GA). This algorithm optimized the turn-on and turn-off degrees of each phase, the parameters of PID controller in transient state, and parameters of PID controller that considered for reducing the torque ripple in steady state. Also, GA simultaneously obtains the optimum parameters of three nonlinear gains considered for switching between the two PID controllers. The proposed control algorithm was simulated using MATLAB / Simulink software package and an application example of 6/4 SRM to validate the performance of designed algorithm AENSI Publisher All rights reserved. To Cite This Article: Belsam Jeba Ananth. M, Vennila. M, Anuja. V., Design of Pid Controller For Switched Reluctance Motor Using Genetic Algorithm. Aust. J. Basic & Appl. Sci., 8(17): , 2014 INTRODUCTION There are many linear and nonlinear models that have been suggested for modeling of SRM, but one of the most accurate models is purposed by Mat lab software in the Sim Power Systems Tools conforming to principles of energy conversion in the switched reluctance motor and the load.this model is based on the definition of magnetic flux linkage in the stator windings (Dong-Hee Lee, et al., 2007). In the first step of modeling, the magnetization curve at rotor position has been defined as a function of the stator current, and in the second step, electromagnetic torque has been expressed as a product of two functions: one depends on the stator current and the other depends on the rotor position. In the final step, the speed of rotor has been extracted according to load torque, electromagnetic torque, and total friction coefficient (Soares, F., P.J. Costa Branco, 2001). In an SRM, only the stator presents windings, while the rotor is made of steel laminations without conductors or permanent magnets (Torrey, D.A., J.H. lang, 1990). This very simple structure greatly reduces its cost. Motivated by this mechanical simplicity together with the recent advances in the power electronics components, much research has being developed. New industrial applications require variable-speed drives with high reliability (NihatInang, et al., 1997). Lately, much effort has been spent in developing SR Motor drives for variable-speed applications such as electric vehicle propulsion or industrial drives. In spite of its high torque ripple, this motor can provide several beneficial features, in particular the robust construction, high efficiency, and fault-tolerant operation (Chen, X., et al., 2005). The characteristics of SR Motor are highly nonlinear because it operates mostly in the saturated region of the magnetization characteristic. This characteristic makes the switched reluctance motors difficult to control (Adrian David Cheok, Yusuke Fukuda, 2002). The purpose of this paper is designing a speed controller for a 6/4 SR Motor in order to achieve minimum torque ripple and high performance in speed tracking (Nalina Shini, G., S. Sivaranjani, 2011). This paper is organized as follows. A precise model of SRM system is introduced. This model contains the model of SR motor, and details of SR motor drive that we mainly focus on this part. After the modification of controller of variable speed drive, its parameters optimize for satisfying the defined control goals (Dong-Hee Lee, et al, 2007). Some simulation result from new controller design compared with previous results that show Corresponding Author: Belsam Jeba Ananth. M, Professor / DMI College of Engineering, Chennai, India. belsamjeba@yahoo.co.in
2 602 Belsam Jeba Ananth. M et al, 2014 considerable improving. In this part the controller algorithm is modified to admit perfect output tracking (speed tracking) with minimum torque ripple (Jianing Liang, et al., 2006). There is not a definite analytical approach to determine the controller parameters for fulfilling a multi objection cost function, because of the high nonlinearity of SR motor. Therefore, we need a nonlinear optimizer tool to solve this problem. Genetic algorithm is an appropriate tool to optimize nonlinear equations and cost functions (Guoqiangang, et al., 2008) II. Switched Reluctance Motor: The SRM is an electric machine that converts the reluctance torque into mechanical power. In the SRM, both the stator and rotor have a structure of salient-pole, which contributes to produce a high output torque. The torque is produced by the alignment tendency of poles (Cenwei Shi, et al., 2007). The rotor will shift to a but there are no windings or permanent magnets on the rotor (Lawrenson, 1980). The rotor is basically a piece of steel (and laminations) shaped to form salient poles. So it is the only motor type with salient poles in both the rotor and stator. As a result of its inherent simplicity, the SRM promises a reliable and a low-cost variable-speed drive and will undoubtedly take the place of many drives now using the cage induction, PM and DC machines in the short future (Xue, X.D., et al., 2002). The number of poles on the SRM s stator is usually unequal (Ryan, E. Janzen, 2006) to the number of the rotor to avoid the possibility of the rotor being in a state where it cannot produce initial torque, which occurs when all the rotor poles are aligned with the stator poles. Fig.1 shows a 8/6 SRM with one phase asymmetric inverter. This 4-phase SRM has 8 stator and 6 rotor poles, each phase comprises two coils wound on opposite poles and connected in series or parallel consisting of a number of electrically separated circuit or phases (Minh, C. Ta, Christian Dufour, 2011). These phase windings can be excited separately or together depending on the control scheme or converter position where reluctance is to be minimized and thus the inductance of the excited winding is maximized. TheSRM has a doubly salient structure (Feng Liang Yuefeng Liao Thomas A. Lipo, 1992). Fig. 1: SRM Drive The aligned position of a phase is defined to be the situation when the stator and rotor polesof the phase are perfectly aligned with each other,attaining the minimum reluctance position and at this position phase inductance is maximum (HiroyuluKinyama, et al., 1998). The phaseinductance decreases gradually as the rotor poles move away from the aligned position ineither direction. When the rotor poles are symmetrically misaligned with the stator poles ofa phase,the position is said to be the unaligned position and at this position the phase has minimum inductance.although the concept of inductance is not valid for ahighly saturated machine like SR motor, the unsaturated aligned and unaligned incrementalinductances are the two key reference positions for the controller. The equivalent circuit for SRM can be consisting of resistance and inductance with some condition. The effects of magnetic saturation, fringing flux around the pole corners, leakage flux, and the mutual coupling of phases are not considered (Jisheng Li, et al., 2010). The linear analytical model of the SRM can be described by three differential equations, which can be classified as the voltage equation, the motional equation and the electromagnetic torque equation. The voltage equation is d (, i) V R. i dt (1)
3 603 Belsam Jeba Ananth. M et al, 2014 An equivalent circuit of the SRM is shown in Fig. 2. Where V is the applied phase voltage to phase, R is the phase resistance, and e is back-emf. Ordinarily, e is the function of phase current and rotor position, and can be expressed as the product of inductance and winding current: (, i) L(, i). i From 1 & 2 the equation can be rewritten as follows (2) V d (, i) di d (, i) d R. i.. di dt d dt (3) Fig. 2: Equivalent Circuit III. Overview of Genetic Algorithm: In this proposal a PID controller is suggested for the speed control of the SR Motor the proposed PID controller parameters are optimized by using Genetic Algorithm (GA). GA is one of the most efficient methods in soft computing techniques. By using this method the speed control and also the problems of SR Motor such as torque ripples and acoustic noises are reduced this is done by changing the switching ON,OFF timings (Lachman, T., et al., 2004). This method rectifies the shortcomings experienced in the Torque Control Method the detailed explanation of this method is given in this chapter. Fig. 3: Block Diagram of PID-GA Genetic algorithm (GA) uses the principles of evolution, natural selection and genetics from natural biological systems in a computer algorithm to simulate evolution. Essentially, the genetic algorithm is an
4 604 Belsam Jeba Ananth. M et al, 2014 optimization technique that performs a parallel, stochastic, but directed search to evolve the fittest population (Zhang Qianfan, et al., 2007). V. Implementation of GA-PID Controller: The initial population of size N is generated randomly to start the optimization process. The next generation can be obtained through the genetic operators. The decision to make during implementation of genetic algorithm is the choice of genetic operators that are to be used (Wang Shuanghong et al., 2004). Fig. 4: Flow Chart of GA This chapter discusses in brief about the results from Mat lab simulation software. Mat lab software gives aclear idea of the proposed model. The comparison of speed controlling of the SR Motor using PID and GA-PID controller this controllers not only controls the speed but also reduces the torque ripples and also acoustic noise in SR Motor. Fig. 5: Pid Simulation Circuit
5 605 Belsam Jeba Ananth. M et al, 2014 Fig. 6: Ga-Pid Simulation Circuit RESULTS AND DISCUSSION This chapter discusses in brief about the results from Mat lab simulation software. Mat lab software gives aclear idea of the proposed model. The comparison of speed controlling of the SR Motor using PID and GA-PID controller this controllers not only controls the speed but also reduces the torque ripples and also acoustic noise in SR Motor. Fig. 7: Torque Characteristics of PID
6 606 Belsam Jeba Ananth. M et al, 2014 Fig. 8: Speed Characteristics of PID The characteristics of the PID controlled SR Motor is analyzed mathematically by tabulating parameters such as Max.Overshoot, Rise time, Settling time.which are helpful in determining the working and controllability of SR Motor by PID Controller. Table 1: PID Controller time response values TORQUE SPEED MEASURING FACTORS TIME MAX.OVERSHOOT RISE TIME SETTLING TIME MAX.OVERSHOOT NA RISE TIME 0.01 SETTLING TIME 0.08 Fig. 9: Torque Characteristics of GA-PID
7 607 Belsam Jeba Ananth. M et al, 2014 Fig. 10: Speed Characteristics of GA-PID The characteristics of the GA-PID controlled SR Motor is analyzed mathematically by tabulating parameters such as Max. Overshoot, Rise time, Settling time. Which are helpful in determining the working and controllability of SR Motor by GA-PID Controller. Table 7.2: GA-PID Controller Performance TORQUE SPEED MEASURING FACTORS TIME MAX.OVERSHOOT NA RAISE TIME SETTLING TIME MAX.OVERSHOOT NA RASIE TIME SETTLING TIME The simulation results clearly shows that torque ripples and acoustic noise are very much reduced in GA- PID when compared with PID controller. By using this GA-PID the usability of SR Motor is extended in large number of applications. The motor and controller specifications and provided in the Mat lab working environment. For each and every specified controller values the characteristics and output of the drive is varied. Conclusion: Simulation results show that not only the controller response is robust against operating point changes, but also the torque ripples reduces dramatically. In this design separating steady state and transient controllers from each other helped to satisfy two different goals which operate against each other. Also, considering fuzzy gains helped us to prevent severe changes of output performance during the switching period of controllers which results in a continuous and smooth operation of the system. Although we have developed this algorithm for a special case study, it can be implemented as software for other cases in the startup stages of driver during the installation and startup period which includes on-line identification of unknown motor parameters and off-line optimization of SR motor drive parameters.sr Motor is one of the most efficient and robust type of motor which is best at many environmental aspects. But the main problem concerned with SR Motor is torque ripples and acoustic noise which reduces the efficiency and working ability of SR Motor. This properties of SR Motor reduces its extended usage. But coming out from the conventional PID controller and new GA-PID controller is introduced to control the SR Motor which not only gives an effective control and also reduces the complexity in controlling this motor. By using this type of GA-PID the problems associated with controlling of SR Motor is eliminated. Moreover torque ripple, acoustic noises are very much reduced in future this project may be extended by using higher techniques evolved from Genetic Algorithm techniques.
8 608 Belsam Jeba Ananth. M et al, 2014 REFERENCES Dong-Hee Lee, Zhen-Guo Lee, Jianing Liang, Jin-Woo Ahn, Single- Phase SRMDrive With Torque Ripple Reduction and Power Factor Correction,IEEE Trans. OnIndustrial Applications, 43: 6. Torrey, D.A., J.H. lang, Modeling a nonlinear Variable reluctance motor drive IEE Proc., 5(37): NihatInang, Adnan Derdiyok, VeyselOzbulur, Torque Ripple Minimization of a Switched Reluctance Motor Including Mutual Inductances Via Sliding Mode Control Technique, IEEE. Chen, X., M. Salem, T. Das, S. Gopalswamy, Speed Control of Switched Reluctance Motor Drive Powered by A Fuel Cell, American Control Conference. Adrian David Cheok, Yusuke Fukuda, A New Torque and Flux Control Method for Switched Reluctance Motor Drives,IEEETrans.on power electronics, 17: 4. Nalina Shini, G., S. Sivaranjani, Development of Nonlinear Model of Axially Laminated Switched Reluctance Motor with two Phase Excitation, Prodeedings of ICETECT, IEEE. Dong-Hee Lee, Zhen-Guo Lee, Jianing Liang, Jin-Woo Ahn, Single-Phase SRM Drive With Torque Ripple Reduction and Power Factor Correction,IEEETrans.on Industrial Applications, 43: 6. Jianing Liang, Zhen-Guo Lee, Dong-Hee Lee, Jin-Woo Ahn, Torque Ripple Reduction Drive of Single-Phase SRM with PFC, (c) IEEE. Guoqiangang, Li yaohua,mengyanjing,liuweiguo, Modelling and Simulation Study on Matrix Converter Fed Induction Motor Drive System Implemented by Direct Torque Control, IEEE. Cenwei Shi, JianqiQiu,Ruiguang Lin, A Novel Self-Commutating Low-Speed Reluctance Motor for Direct-Drive Applications,IEEETrans.on Industrial Applications, 43: 1. Xue, X.D., K.W.E. Cheng, S.L. Ho, N.C. Cheung, Investigation of the Effects of the Control Parameters and Outputs on Power Factor of Switched Reluctance Motor Drive Systems, IEEE. Ryan, E. Janzen, Efficiency improvements from an electric vehicle Induction motor drive,with augmentations to a PI Control, IEEE. Minh, C. Ta, Christian Dufour, Real-Time Simulation and Control of Reluctance Motor Drives for High Speed Operation with Reduced Torque Ripple, IEEE. Feng Liang Yuefeng Liao Thomas A. Lipo, A New Variable Reluctance Motor Utilizing An Auxiliary Commutation Winding, IEEE. HiroyuluKinyama,ShinichiroKawano,YukioHonda,ToshiroHigaki, High Performance Synchronous Reluctance Motor with Multi-flux Barrier for the Appliance Industry, IEEE. Jisheng Li, Zhifeng Li, Shuying Lei, Research on Position Detection Methods of Reluctance Motor Based on Sliding Mode Observer, 2nd International Asia Conference on Informatics in Control, Automation and Robotics, IEEE. Lachman, T., T.R. Mohamad, C.H. Fong, Nonlinear modelling of switched reluctance motors using artificial intelligence techniques, IEE Proc.-Electr. Power Appl., 151: 1. Zhang Qianfan, Cui Shumei, TianXinjia, Hybrid Switched Reluctance Motor Applied in Electric Vehicles,, IEEE. Wang Shuanghong, Zhan Qionghua, Ma Zhiyuan, Zhou Libing, Implementation of a 50kW 4-Phase Switched Reluctance Motor Drive System for HEV, IEEE. Soares, F., P.J. Costa Branco, Simulation of a 6/4 Switched Reluctance Motor Based on Matlab/Simulink Environment, IEEE Trans. On Aerospace and Electronic systems, 37: 3.
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