輔以監督控制的自我產生模糊類神經網路控制器用於永磁式線型同步馬達

AUTOMATIC GENERATION FUZZY NEURAL NETWORK CONTROLLER WITH SUPERVISORY CONTROL FOR PERMANENT MAGNET LINEAR SYNCHRONOUS MOTOR

柯耀華(Yao-Hua Ke)

指導教授 ： 呂虹慶

大同大學/電機資訊學院/電機工程學系所/碩士(2007年)

摘要

本論文提出輔以監督控制的自我產生模糊類神經網路控制器用於永磁式線型同步馬達。輔以監督控制的自我產生模糊類神經網路控制器是由一個有神經元自我產生且能線上學習的自我產生模糊類神經網路控制器，和一個被設計來使系統狀態在一個有界區域內穩定之監督控制器所組成。馬氏距離的法則被利用來使類神經網路有判斷神經元是否要產生的能力。為了增加自我產生模糊類神經控制器中倒傳遞演算法的學習速度，一個切換法則和一個慣性項在本研究中被使用。監督控制器的設計由里亞普諾夫(Lyapunov)概念中得到，同時控制系統的穩定可以獲得保證。由模擬的結果可以顯示出所提出的控制器系統，在受控體之參數變動及具有外部干擾的情況下，仍具有良好的強健特性。

關鍵字

監督控制 ； 自我產生模糊類神經網路 ； 永磁式線型同步馬達

並列摘要

The automatic generation fuzzy neural network (AGFNN) controller with supervisory control for permanent magnet linear synchronous motor (PMLSM) is proposed in this thesis. It comprises an AGFNN controller, which has ability of neuron automatic generation with on-line learning and a supervisory controller, which is designed to stabilize the system states around a bounded region. The Mahalanobis distance (M-distance) formula is employed that the neural network has the ability of identification of the neurons will be generated or not. To improve the learning speed of back-propagation algorithm in AGFNN controller, a switching law and a momentum term are used in this study. The design of supervisory controller is derived in the Lyapunov sense; thus, the stability of the control system can be guaranteed. Simulation results show that the proposed controller is robust with regard to plant parameter variations and external load disturbance.

並列關鍵字

PERMANENT MAGNET LINEAR SYNCHRONOUS MOTOR ； SUPERVISORY CONTROL ； AUTOMATIC GENERATION FUZZY NEURAL NETWORK

參考文獻

[1] I. Boldea and S.A Nasar, Linear Electric Actuators and Generators. Cambridge University Press, London, UK, 1997.

[2] M. Sanada, S. Morimoto, and Y. Takeda, “Interior permanent magnet linear synchronous motor for high-performance drives,” IEEE Trans. Ind. Appl., vol. 33, pp. 966–972, 1997.

[3] C. J. Kempf, and S. Kobayashi, “Disturbance observer and feedforward design for a high-speed direct-drive positioning table,” IEEE Trans. Control Syst. Tech., vol. 7, pp. 513–526, 1999.

[4] F. J. Lin, R. J. Wai, and C. M. Hong, “Hybrid supervisory control using recurrent fuzzy neural network for tracking periodic input,” IEEE Trans. Neural Netw. , vol. 12, pp. 68–88, 2001.

[5] S. Sankaranarayanan and F. Khorrami, “Adaptive variable structure control and application to friction compensation,” IEEE CDC Conf. Rec. , vol. 5, pp. 4159–4164, 1997.