International Journal of Advance Engineering and Research Development Volume 4, Issue 9, September -2017 @IJAERD-2017, All rights Reserved 425 Scientific Journal of Impact Factor (SJIF): 4.72 e-ISSN (O): 2348-4470 p-ISSN (P): 2348-6406 SPEED CONTROL OF PMSM DRIVE USING ANFIS BASED SPEED CONTROLLER (Comparison with Ziegler Nichols‟ technique) Abhishek Verma 1 , Adarsh Singh 2 , Anurag Agrawal 3 Dept.Of Electrical Engineering, Shri Ramswaroop Memorial Group of Professional Colleges Lucknow, UP, India 1 Dept.Of Electrical Engineering, Shri Ramswaroop Memorial Group of Professional Colleges Lucknow, UP, India 2 Head of Electrical Engineering Dept., Shri Ramswaroop Memorial Group of Professional Colleges Lucknow, UP, India 3 Abstract— Nowadays, PMSM drive systems are being employed in many industrial applications ranging from low to medium power because of their simple structures, ease of maintenance and efficiency. However the non-linear behavior which arises mainly from motor dynamics and load characteristics and the presence of uncertainties makes control an extremely difficult task. So, the speed control technique should be adaptive and robust for better performance of the drive. The conventional PID controllers are used to control speed of the drive. Various tuning techniques are employed such as Z-N tuning, Cohen- Coon, manual tuning etc. of which Z-N tuning is the most widely used method. Since, PID controllers work well under linear operating conditions and fails when non-linearity arises. Therefore, Artificial Intelligence techniques are being implemented to achieve better performance either in isolation i.e. Fuzzy, ANN, GA, PSO etc. or in hybrid as Artificial Neuro Fuzzy Inference System. This project work presents design and simulation of a modern approach of speed control of PMSM using ANFIS based speed controller. Hence an artificial intelligence control strategy namely ANFIS has been used as it requires only a reduced computation power while maintaining satisfactory static and dynamic performance and a good insensitivity to perturbations and parameter uncertainties. A model of PMSM drive is simulated under various dynamic conditions like starting, load application and removal, speed reversal. The combined capability of Neuro Fuzzy Controller in handling uncertainties and learning process is proven to be advantageous in modeling highly non-linear systems. The designed ANFIS based speed controller is presented to develop a dynamic model of PMSM as it does not require the explicit mathematical model of the plant and automatically takes care of changing conditions as well. Hence ANFIS based speed controller gives better performance of the drive with linear as well as non-linear load as compared to conventionally used Z-N tuned PID speed controller. Keywords—PMSM, ANFIS, PID. I. INTRODUCTION For the speed control of PMSM, many controllers are used. In conventional P, PI and PID controllers, very fine tuning is required which cannot cope up with system‟s parameter variations. Also the performance of such controllers is affected due to variations in physical parameters like temperature, noise, saturation etc. Ziegler Nichols‟ technique that is probably the most known and the most widely used method for tuning of PID controllers is also known as online or continuous cycling or ultimate gain tuning method. The Ziegler-Nichols‟ tuning creates „quarter wave decay‟. This is an acceptable result for some purposes, but not opt imal for all applications. This tuning rule is meant to give PID loops best disturbance rejection. It yields an aggressive gain and overshoots [1] – some applications wish to instead minimize or eliminate overshoot, and for these this method is inappropriate. Many control systems use adaptive controllers for PMSM, which can track only linear systems. Therefore, fuzzy logic based controller may be used to achieve more accurate and faster solutions and to handle complicated non-linear characteristics. A simple structure fuzzy logic controller (FLC) is used in the speed control loop to regulate the motor speed. The fuzzy logic controller used here to control PMSM takes two inputs i.e. E (error) and CE (change in error) and gives controlled output (DU). A fuzzy logic controller with rule viewer block is used to control the output of PMSM. In this block FIS file is saved which includes input and output membership functions and fuzzy set of rules. Different membership functions can be selected to control the output variable. The main aim of this paper is to obtain better transient as well as steady state response for proposed PMSM drive by using ANFIS speed controller as this cannot be achieved from the Ziegler Nichols‟ PID tuning technique. Because, the outcome of the controller is also random and optimal results may not be obtained. Selection of the proper membership functions and in turn selecting the right rule base depending on the situation can be achieved by the use of an ANFIS controller, which becomes an integrated method of approach for control purposes and yields excellent results, which is the highlight of this paper. In the designed ANFIS scheme, neural network techniques are used to select a proper rule base, which is achieved using the back-propagation algorithm. This integrated approach improves the designed controller‟s performance in many ways in terms of cost -effectiveness and reliability. The aim is to reduce the dynamic parameters like rise time, peak time, settling time, maximum peak overshoot and steady state error during various changing conditions like starting, load application and load removal and speed reversal. A comprehensive comparison is being made between the two techniques in order to draw conclusions about the suitability of the ANFIS logic controller.