輔以幅狀基底函數網路之自我產生模糊類神經 控制器於永磁同步線型馬達之應用

RADIAL BASIS FUNCTION NETWORK BASED AUTOMATIC GENERATION FUZZY NEURAL NETWORK CONTROLLER FOR PERMANENT MAGNET LINEAR SYNCHRONOUS MOTOR

徐文彥(Wen-Yen Hsu)

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

在本論文中，輔以幅狀基底函數網路為基礎之自我產生模糊類神經網路控制器被提出來控制永磁同步線型馬達轉子位置去追踨週期參考軌跡。所提出的控制器不僅有倒傳遞網路的優點，可以去調整所連結權重的參數，同時也有切換法則、誤差項和幅狀基底函數網路的優點，可以改善追踨誤差和穩態響應。其中結構學習是基於馬氏距離而參數學習是基於監督式倒傳遞方法。所提出控制器的模擬結果，在追踨週期參考軌跡圖形中其追踨誤差和穩態響應有更好的性能，對於系統中的參數變化和外部的負載干擾下也有強健的性能。

關鍵字

幅狀基底函數網路 ； 模糊類神經網路 ； 永磁同步線型馬達

並列摘要

In this thesis, a radial basis function network (RBFN) based automatic generation fuzzy neural network (AGFNN) is proposed to control the rotor position of the permanent magnet linear synchronous motor (PMLSM) to track the period reference trajectories. The proposed RBFN based AGFNN not only has the advantages of the back-propagation algorithm, in which the parameter of the connected weights are adjusted but also has the advantages of the switching law, momentum term and RBFN, in which the tracking error and steady state responses will be betterment. The structure learning is based on the Mahalanobis distance and the parameter learning is based on the back-propagation algorithm. The simulation results of the proposed RBFN-based AGFNN, in which the periodic reference trajectories show that the tracking error and steady state responses are better performance, and the parameter variation and external load disturbance of system have robustness performance.

並列關鍵字

radial basis function network ； fuzzy neural network ； permanent magnet linear synchronous motor

參考文獻

[1] S. Horikawa, T. Furuhashi, and Y. Uchikawa, “On fuzzy modeling using fuzzy neural networks with the back-propagation algorithm” IEEE Transaction Neural Networks, vol. 3, no. 6, pp. 801-806, 1992.

[2] C. C. Lee, “Fuzzy logic in control systems: Fuzzy logic controller-Part I and part II,” IEEE Transaction System, Man, Cybern., vol. 20, pp. 404-436, 1990.

[3] R. R. Yager and D. P. Filev, Essentials of Fuzzy Modeling and Control, New York: Wiley, 1994.

[4] T. Fukuda and T. Shibata, “Theory and applications of neural networks for industrial control systems,” IEEE Transaction Industrial Electron., vol. 39, no. 6, pp. 472-491, 1992.

[5] P. S. Sastry, G. Santharam, and K. P. Unnikrishnan, “Memory neuron networks for identification and control of dynamical systems,” IEEE Transaction Neural Networks, vol. 5, no. 2, pp. 306-319, 1994.