Compound intelligent control system combining fuzzy control with neural networks in a permanent magnetic synchronous motor

Zhiyuan Zhang; Weili Li; Taifu Li

doi:10.1117/12.664629

2 May 2006 Compound intelligent control system combining fuzzy control with neural networks in a permanent magnetic synchronous motor

Zhiyuan Zhang, Weili Li, Taifu Li

Author Affiliations +

Proceedings Volume 6042, ICMIT 2005: Control Systems and Robotics; 60421S (2006) https://doi.org/10.1117/12.664629
Event: ICMIT 2005: Merchatronics, MEMS, and Smart Materials, 2005, Chongqing, China

Abstract

An AC motor belongs to the category of a controlled object that is multi-variable, nonlinear and strong correlation, complex to mathematical model, and whose control performance is affected by a time-changing parameter. Therefore, it is very difficult to obtain the desired static and dynamic characteristic through a general fixed regulator. In this paper, the authors present a compound intelligent control strategy, combined with a neural network and fuzzy control. Considering that a neural network is good at self-learning, and a single fuzzy control algorithm is rapid in its response characteristics, the compound control strategy can compensate for a disadvantage of fuzzy control, which is associated with poor stability and precision and also requires solving a puzzle in the time-changing parameters in the controlled object. On the basis of a dynamic model of the permanent magnetic synchronous motor and its working principle, the authors designed the block diagram of a control system, combined a neural PID control and fuzzy control, and studied the corresponding control algorithm in detail. The simulation results show that the compound intelligent control system is good in dynamic performance and robustness.

Citation Download Citation

Zhiyuan Zhang, Weili Li, and Taifu Li "Compound intelligent control system combining fuzzy control with neural networks in a permanent magnetic synchronous motor", Proc. SPIE 6042, ICMIT 2005: Control Systems and Robotics, 60421S (2 May 2006); https://doi.org/10.1117/12.664629

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