ISSN (Online) 2321-2004 ISSN (Print) 2321-5526 INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH IN ELECTRICAL, ELECTRONICS, INSTRUMENTATION AND CONTROL ENGINEERING Vol. 3, Issue 3, March 2015 Copyright to IJIREEICE DOI 10.17148/IJIREEICE.2015.3304 15 Speed Control of Induction Motor by Z-N Method and Genetic Algorithm Optimization with PI and PID Controller Jaya Raju Manepalli 1 , CH.V.N. Raja 2 PG Student, Department of EEE, ANITS, Visakhapatnam, Andhra Pradesh, India 1 Associate Professor, Department of EEE, ANITS, Visakhapatnam, Andhra Pradesh, India 2 Abstract: This Paper presents a comparative study of Z-N method and Genetic Algorithm method (GA) to determine the optimal proportional-integral-derivative (PID) controller parameters, for speed control of a Field Oriented Control (FOC) induction motor; the GA algorithm has been programmed and implemented in MATLAB. Z-N method and trial and error and open loop IM has been modelled in MATLAB (SIMULINK).comparing with traditional Ziegler- Nicholson method, it has been observed that during optimizing the controller parameters of a FOC IM drive with evolutionary algorithms (EA), the performance of the controller is improved for the step input in speed control as well as for speed tracking problem more efficiently under no load condition, if the load is placed on IM, the performance characteristics have changed for ZN and trial and error method, but even if load change occur, there is no much variation in the evolutionary algorithms (GA) than and Ziegler – Nicholson method. Keywords: FOC (IM), fitness function, GA, Z-N method, PIControl, PID control I. INTRODUCTION The i nduction motors has been widely used in various industries due to its robustness maintenance free operation, better efficiency and lower cost. In different industries, wide range of speed control with fast torque response regardless of load variation is required this can be achieved very efficiently for induction motor using Field Oriented Control (FOC) [1, 2]. For speed control of induction motor, PI (proportional-integral) and PID (Proportional-integral-derivative) controllers are generally used. To find out the optimum parameters of the controller to obtain a good closed loop response at different operating conditions is a trivial task and these parameters can be optimized by conventional tuning methods, such as Ziegler-Nicholson (Z-N) method [6]. Other tuning methods like pole placement optimization technique are also done [4]. Now a day, Evolutionary methods like Genetic Algorithm (GA), is used for tuning the parameters. These new tuning techniques can very efficiently solve complex problems like speed tracking problems, where demand speed is a complex function of time, where the conventional methods may not optimize the controller parameter so easily. Genetic Algorithm is a heuristics search method based on ωharles‟s Darwin principle of Natural Selection which narrates „the survival of the fittest‟ of each and every individual on earth. At each step, the GA selects individuals from the current population as parents and uses them to produce the offspring‟s for the next generations. The fitness of all the individual of the population is calculated and the convergence of the generation is based on this fitness criterion. It is well suited for its solving complex design Optimization problem as it can handle discrete and continuous variables, nonlinearity and different constrain functions of a system, without requiring gradient information [9]. The major objective of this work is to compare efficiency of both Z-N method and GA optimization technique Applied to a direct field oriented Control Induction motor drive for a simple speed demand problem as well as for a complex speed problem. Here both Z-N method and GA have been applied to search for the optimal PID controller parameters of FOC IM drive. The error criteria for both the methods are set to improve transient error and steady state error Fig.1: Schematic diagram of GA based optimizer for PI and PID controller of FOC IM The PID controller‟s gain parameters viz. K p , K i and K d are optimized, by GA, to have the optimum output of the controllers are given by Eqn.4 and Eqn.5 [6, 7, 8]. Here e (t) is the difference between the demand speed and the actual speed of the system is denoted by Ȧ dem and Ȧ act . For the speed tracking problem, the parameters are optimized obeying the same procedure as stated above.