Research On Intelligent Control Of Multiple Phase Permanent-magnet Synchronous Motor Speed Control System

For multiphase permanent-magnet synchronous motor(PMSM) frequency conversion speed control system has high reliability, so it becomes one of research emphases in the field of electric transmission. The performance of AC variable speed drive system has been improved greatly since field-oriented control is applied. This paper expatiates and analyses the vector control system of multiphase PMSM, analyses the importance of intelligent control used in multiphase PMSM speed control system. After the field-oriented control of three-phase PMSM drive system is introduce in brief, a mathematical model of multiphase PMSM, taking a six-phase PMSM with a dual-star winding for example, is derived by coordinates transformation and the control picture is designed. In this paper, intelligent control methods applied in multiphase speed control system are deep investigated.In allusion to the nonlinearity and strong coupling of multiphase PMSM, fuzzy vector control method is brought forward first in the paper. Simulation is done on the multiphase system which is built previously. Then fuzzy vector control is improved and get better performance.Secondly, considering the respective advantages of PI control and fuzzy control, a double mode vector control system is designed. Simulation results indicate that the proposed method combines the strongpoint of PI control and fuzzy control perfectly and the results is famous.Because its good robustness and independence on the model of plant of fuzzy control and good self-learning ability of neural network, Gauss fuzzy network is presented. It also receives good simulation result.Finally, multiphase PMSM speed control system based on B-spline basis function neural network is presented. This intelligent control method combines the better local control performance of B-spline basis function and good robustness and independence on the model of plant of fuzzy control. It shows excellent speed control result. The results of simulation shows that these four intelligent control systems have better performance over traditional PID control and general fuzzy control. The control system has quick response,no over shoot and good dynamic and static behaviors as well as robustness.