以小腦模型為基底之強健控制器用於不確定非線性系統

本論文之主旨係在於發展適應性強健型小腦模型控制器，並結合適應性控制、與強健控制等理論，再依據李雅普諾夫穩定性定理設計小腦模型控制器的參數適應性調整法則，因此整個閉迴路控制系統的穩定性可以被保證，最後並廣泛的應用在一些具有非線性且不確定系統之閉迴路控制上。本論文首先設計小腦模型控制器，並提出三種不同的補償控制器分別結合小腦模型控制器進行設計，在應用上首先針對單輸入-單輸出之機翼震盪問題進行模擬驗證，多輸入-多輸出系統方面則結合滑動模式控制系統，設計多輸入-多輸出之適應性強健型小腦模型控制器，並用於高度非線性且時變系統之軌跡追蹤問題，例如：人造衛星姿態控制系統。最後，經由模擬結果顯示，對於這些具有不確定且非線性之系統，本論文所提出的控制系統均能達到令人滿意的控制性能。

關鍵字

適應性；強健型；小腦模型控制器；李雅普諾夫；多輸入多輸出

並列摘要

The purpose of this thesis is to develop an adaptive robust cerebellar model articulation controller (CMAC) system by integrating CMAC with adaptive control and robust control technologies for the control application to nonlinear systems. According to Lyapunov synthesis approach, the adaptive tuning laws of CMAC can be derived and the system stability can be guaranteed. The control system can be applied to uncertain nonlinear systems. This thesis designs the CMAC first; then, CMAC is integrated with three kinds of compensation controllers. For applications, we consider the single-input single-output (SISO) system first, and simulate in wing rock control system. Moreover, this thesis also proposes the robust CMAC control system using sliding-mode technology for the uncertain nonlinear multi-input multi-output (MIMO) system and its simulation for satellite attitude control is demonstrated. From the simulation results, the control schemes proposed in this thesis have been shown to achieve satisfactory control performance for the considered nonlinear systems.

並列關鍵字

Adaptive ； Robust ； CMAC ； Lyapunov synthesis ； MIMO

參考文獻

[2] Y. G. Leu, T. T. Lee, and W. Y. Wang, “On-line tuning of fuzzy-neural network for adaptive control of nonlinear dynamical systems” IEEE Trans. Syst., Man, Cybern. B, vol. 27, no. 6, pp. 1034-1043, 1997.

[3] C. H. Wang, T. C. Lin, T. T. Lee, and H. L. Liu, “Adaptive hybrid intelligent control for uncertain nonlinear dynamical systems” IEEE Trans. Syst., Man, Cybern. B, vol. 32, no. 5, pp. 583-597, 2002.

[4] J. Y. Chen, P. S. Tsai, and C. C. Wong, “Adaptive design of a fuzzy cerebellar model arithmetic controller neural network” IEE, Contr. Theory Appl., vol. 152, no. 2, pp. 133-137, 2005.

[5] C. M. Lin and Y. F. Peng, “Adaptive CMAC-based supervisory control for uncertain nonlinear systems” IEEE Trans. Syst., Man, Cybern. B, vol. 34, no. 2, pp. 1248-1260, 2004.

[6] J. H. Park, S. H. Huh, S. H. Kim, S. J. Seo, and G. T. Park, “Direct adaptive controller for nonaffine nonlinear systems using self-structuring neural networks” IEEE Trans. Neural Networks, vol. 16, no. 2, pp. 414-422, 2005.

國際替代計量

以小腦模型為基底之強健控制器用於不確定非線性系統

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