Design of Adaptive TSK Fuzzy Self-organizing Recurrent Cerebellar Model Articulation Controller for Switched Reluctance Motor and Chaotic Systems

• Main Authors: Chen-Hao Li, 李振豪
• Other Authors: 王順源
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/93s9h4

碩士 === 國立臺北科技大學 === 電機工程系 === 106 === In this study, an adaptive Takagi-Sugeno-Kang fuzzy self-organizing recurrent cerebellar model articulation controller (ATFSORC) is proposed for speed control of switched reluctance motor (SRM) drive systems and for the control of chaotic systems. The proposed ATFSORC is composed of a set of TSK fuzzy rules, a cerebellar model articulation controller (CMAC), a recurrent CMAC (RCMAC), a self-organizing cerebellar model articulation controller (SOCMAC) and a compensation controller. The novel design is that the association memory layers of ATFSORC will be adjusted systematically by the self-organizing mechanism, in order to reduce the structure complexity and improve control performance of ATFSORC. In addition, the concept of Takagi-Sugeno-Kang fuzzy rules is introduced to increase the learning speed of ATFSORC. A integrated error function is used as input to ATFSORC. Furthermore, the improved compensating controller is designed to dispel the errors between an ideal controller and the TFSORC. Moreover, the adaptive laws of TSK parameters, recurrent weights, the Gaussian function mean parameters and the Gaussian function standard deviation are online tuned, and the Lyapunov function is applied to guarantee the stability of the system. Finally, simulation studies show that the proposed ATFSORC can achieve favorable control performance when the SRM drive systems is operated at different speed command and the chaotic systems are operated at different parameters. In this study, the root-mean-square error (RMSE), average error and max error are used as performance indexing. According to simulation result, the proposed ATFSORC can achieve faster convergence of the tracking error than fuzzy CMAC (FCMAC) and CMAC.