具自我學習能力之智慧型控制系統設計

Design of an intelligent control system with online learning algorithms

邱健榮(Chien-Jung Chiu)

指導教授 ： 許駿飛 ； 蔡章仁

國立中央大學/資訊電機學院/電機工程學系/博士(2012年)

摘要

一般控制器設計需要事先知道受控系統方程式，但該方程式往往很難獲得，本論文提出了多種智慧型控制系統的設計方法，將適應性控制、類神經網路控制與滑動模式控制等的技巧結合應用在控制系統參數學習能力上。首先在適應性比例積分微分控制器設計方面，透過最陡坡降法調整法則達到線上調整控制器增益參數。在適應性類神經網路控制器設計方面，則提出回饋型小波類神經網路適應性控制、輻狀基底類神經網路適應性控制與模糊小波類神經網路適應性控制系統，分別利用了最陡坡降法調整法則與李亞普諾夫穩定定理論推導出系統參數的學習法則，因此可保證經由線上調整系統參數後均能使得系統趨向於穩定，並提出PID型式學習法則加快追蹤誤差與系統參數收斂速度。進一步，本文提出一動態滑動模式類神經網路適應性控制器用來解決控制訊號抖動缺點。最後，本文將所開發之多種智慧型控制方法分別運用於感應伺服馬達定位控制、無刷直流馬達定位控制、混沌控制系統以及混沌系統同步控制，經由模擬或實驗結果均可發現所提出方法之可行性。

關鍵字

模糊小波類神經網路 ； 回饋型小波類神經網路 ； 輻狀基底類神經網路

並列摘要

The traditional control system designs are always based on the system dynamic equations; however, it is difficult to be described as the plants are too complex. This dissertation proposes several intelligent control systems based on the adaptive control, sliding-mode control and neural network control technologies. For the adaptive proportional integral derivative (PID) controller design, the adaptive PID controller can automatically tune the controller gain factors based on the gradient descent method. For the adaptive neural network controllers design, a recurrent-wavelet-neural-network-based adaptive control, RBF-neural-network-based adaptive control and fuzzy-wavelet-neural-network-based adaptive control methods are proposed. In these control system designs, an online parameter tuning methodology, using the gradient descent method or the Lyapunov stability theorem, is developed to increase the learning capability and to guarantee the system’s stability. Moreover, a PID type adaptation tuning mechanism is derived to speed up the convergence of the tracking error and controller parameters. Furthermore, the dynamic-sliding-mode-neural-network-based adaptive control design method is developed with dynamic learning rate which is proposed to reduce the chattering phenomenon. Finally, the developed control system design methods are applied to some control system applications, such as induction servomotor system, brushless DC motor system, chaotic system and chaotic synchronization system. The simulation and experimental results have demonstrated that the effectiveness of the proposed design methods.

並列關鍵字

fuzzy wavelet neural network ； recurrent wavelet neural network ； radial basis function neural network

參考文獻

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