輔以切換策略之模糊類神經網路於永磁同步馬達速度控制器設計

本論文是針對追蹤週期性參考訊號，提出一個輔以切換策略之模糊類神經網路控制器的設計方法。簡單來說，模糊類神經網路即是一個具有模糊推論能力之類神經網路，它結合了類神經網路與模糊邏輯兩者之優點。由於受控體之參數變動或外部擾動，導致無法精確地計算出系統的輸出。為了克服這個缺點並增加類神經連結權重的線上學習率，我們將透過所提出的切換策略選取較適當的輸出層誤差量。首先，利用速度誤差定義切換條件，再來藉由切換調整器判斷目前是否滿足切換條件。最後將以切換調整器的輸出，更新類神經網路連結權重及其他參數。為了得到自我建構模糊類神經網路控制器加入切換策略前後的性能比較，我們透過一些模擬結果證實之。由模擬結果可顯示出所提出的控制系統，在受控體參數變動及具有外部擾動的情況下，仍具有良好的強健特性。

關鍵字

切換策略；模糊類神經網路

並列摘要

In this thesis, a self-constructing fuzzy neural network controller (SCFNNC) design with switching strategy for permanent-magnet synchronous motor is proposed to track periodic reference input command. The SCFNN system is a straightforward implementation of fuzzy inference system with four layered neural network structure. This system combines the advantages of the neural networks and fuzzy logic theorem. The exact output result of the system cannot be determined due to the uncertainties of the plant dynamic such as parameter variations and external disturbance. To overcome the drawback and to increase the on-line learning rate of the weights, the switching strategy is proposed to choice a suitable parameter of error term. First, the switching condition is defined by speed error. Next, we will judge whether the switching condition is satisfied through proposed switching regulator. Finally, the switching regulator is back-propagated to the SCFNN and adjusted the link weights and other parameter. Several simulations are provided to compare the effectiveness with the SCFNN and proposed SCFNN using switching strategy. The simulation results for periodic reference trajectories show that the dynamic behavior of the proposed control system is robust with regard to plant parameter variations and external load disturbance.

並列關鍵字

switching strategy ； fuzzy neural network

參考文獻

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[2] J. S. Jang and C. T. Sun, “Neuro-Fuzzy Modeling and Control,” in Proc. of the IEEE, vol. 83, no. 3, pp. 378-406, 1995.

[3] C. T. Lin and C. S. G. Lee, “Neural-Network-Based Fuzzy Logic Control and Decision System” IEEE Trans. Comp., vol. 40, no. 12, pp. 1320-1336, 1991.

[4] H. C. Chang and M. H. Wang, “Neural Network-Based Self-Organizing Fuzzy Controllers for Transient Stability of Multimachine Power Systems,” IEEE Trans. Energy Conversion, vol. 10, no. 2, pp. 339-347, 1995.

[5] D. Batzel and K. Y. Lee, “An Approach to Sensorless Operation of the Permanent-Magnet Synchronous Motor Using Diagonally Recurrent Neural Networks,”IEEE Trans. Energy Conversion, vol. 18, no. 1, pp. 100-106, 2003.

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輔以切換策略之模糊類神經網路於永磁同步馬達速度控制器設計

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