THE 10 th INTERNATIONAL SYMPOSIUM ON ADVANCED TOPICS IN ELECTRICAL ENGINEERING March 23-25, 2017 Bucharest, Romania ISBN: 978-1-5090-5160-1/17/$31.00 ©2017 IEEE Parameter Optimization for a fuzzy logic control of a Permanent Magnet Brushless Motor F. Philibert ANDRINIRINIAIMALAZA 1 , N. Jean RAZAFINJAKA 2 , Liviu M. KREINDLER 3 , Member IEEE 1 Digital Control Laboratory of Electrical Drive Systems, Electrical Engineering Faculty, University Politehnica, Bucharest. 2 Automatic Laboratory, Higher Polytechnic School of Antsiranana, Madagascar. 3 Digital Control Laboratory of Electrical Drive Systems, Electrical Engineering Faculty, University Politehnica, Bucharest. philibert.andriniriniaimalaza@gmail.com , razafinjaka@yahoo.fr , l_kreindler@technosoftmotion.com Abstract- This paper proposes a strategy to optimize parameters in a fuzzy logic control of a Permanent Magnet Brushless Motor. The proposed system uses a neural network optimization to determine all the static parameters of the fuzzy logic control strategy. Analysis of the vector control scheme and the fuzzy controller are illustrated from the simulation with the MATLAB Environment to compare their performance. The main objective behind this optimization strategy is to improve the performance of the fuzzy logic control scheme. This approach has been experimentally evaluated on a 1.5 kW permanent magnet brushless motor drive. Keywords: Brushless Motor, Neural network optimization, Fuzzy logic control, vector control I. INTRODUCTION Brushless motors, called Permanent Magnet Brushless Motors, are used in various applications such as defense, industries, robotics, etc. It begun more popular because of its high performance in front of electromagnetic disturbances, noises… and its suitability for any safety critical applications [1]. The length of the motor can be reduced for all the applications needed because of the absence of commutator and brushes. Better speed versus torque characteristics, high dynamic response, high efficiency and reliability, long operating life, noiseless operation, higher speed ranges, and reduction of electromagnetic interference (EMI) represent all the advantages of this motors over others like brushed and induction motors [2]. Additionally, a permanent magnet brushless machine is defined as an electrical motor that does not require an electrical connection between stationary and rotating parts, and is categorized based on PMs mounting and the back-EMF shape. For motor control implementation, mathematical modelling is needed, a difficult task for an accurate result. An alternative is to use an intelligent controller which can provide high accuracy without a mathematical model for its control strategy. One type of such intelligent controllers is the fuzzy logic controller [3], which provides high accuracy in a high performance drive system without the need of a mathematical model of the system. The fuzzy logic controller uses fuzzy logic as a design methodology, which can be applied in developing nonlinear system for embedded control. Fuzzy Logic controller is an attractive choice when precise mathematical formulations are not possible. Nowadays, a lot of fuzzy logic controller strategy was developed and presents a better control performance. In this control strategy, some constant parameters are needed to be defined and determined carefully but there is no precise methodology for that. So, an attempt is made to develop a methodology using a neural network system to define and determine their parameters in a fuzzy controller for a permanent magnet brushless motor drive. Firstly, an introduction about the paper is given. In the second section, the proposed systems were presented with the vector control and fuzzy logic control strategies of the brushless motor. An approach of a neural network strategy is developed in the next section. The fourth section deals with the simulation of the proposed methodology through the MATLAB environment. The results and discussions are presented in the fifth section and the final section presents the conclusions. II. PROPOSED SYSTEMS Fig. 1 presents the block diagram of the proposed system. The system consists of an AC/DC converter (or DC source), six step inverter, brushless motor, TP (position transducer), and the controller block which combines gate drive for inverters, controller strategy and all the switching logic. Fig. 1. Proposed system