非線性不確定系統之智慧型直接適應控制

本論文結合Takagi-Sugeno-Kang模糊推論機制和遞迴小腦模型控制器(RCMAC)的優點提出一TSK遞迴小腦模型控制器(TSKRCMAC)架構，而此架構是利用傳統的小腦模型控制器(CMAC)，直接在記憶體空間層加入一遞迴單元和權重記憶體空間層改為TSK空間，並藉由此遞迴單元來改善傳統小腦模型控制器只有靜態映射的特性，使得TSK遞迴小腦模型控制器具有動態映射的功能和即時的參數調整能力，而達到優異的近似能力，並以系統鑑別來驗證其網路的特性與優點。接著將以TSK遞迴小腦模型控制器為主架構，結合步階迴歸控制和控制技巧的優點，設計一強健性智慧型步階迴歸控制系統，此系統中之適應性TSK遞迴小腦模型控制器主要被用模擬一理想的步階迴歸控制法則，而強健控制器則被用來補償適應性TSK遞迴小腦模型控制器與理想的步階迴歸控制法則之間的近似剩餘部份以獲得控制性能。此外，泰勒線性化技巧也將運用到TSK遞迴小腦模型控制器上，並以Lyapunov穩定定理為基礎結合控制理論推導出控制系統之適應性學習規則(Adaptive Law)，以確保閉迴路系統之穩定。最後，此控制系統將應用在倒單擺、Genesio chaotic系統及Sprott circuit系統上，經由模擬結果證明強健性智慧型步階迴歸控制系統對於非線性系統能達到良好的追蹤目的。

關鍵字

小腦模型控制器；遞迴小腦模型控制器； TSK遞迴小腦模型控制器；步階迴歸控制； Lyapunov穩定定理；系統鑑別；小腦模型控制器、遞迴小腦模型控制器、TSK遞迴小腦模型控制器、步階迴歸控制、Lyapunov穩定定理、系統鑑別、H∞ 控制

並列摘要

In this thesis, we investigate a Takagi-Sugeno-Kang type cerebellar model articulation controller (TSKCMAC) that combines the advantages of the Takagi-Sugeno-Kang fuzzy inference mechanism and cerebellar model articulation controller (CMAC). Base on this TSKCMAC system, a Takagi-Sugeno-Kang recurrent cerebellar model articulation controller (TSKRCMAC) is proposed in this thesis. The proposed dynamic structure of TSKRCMAC has superior capability to the conventional static cerebellar model articulation controller in an efficient learning mechanism and dynamic response. Temporal relations are embedded in TSKRCMAC by adding feedback connections in the association memory space so that the TSKRCMAC provides a dynamical structure. So, the TSKRCMAC have been used for the identification and control of nonlinear dynamic systems. For the control problem, a robust intelligent backstepping control (RIBC) system combined with adaptive TSKRCMAC and control technique is proposed for a class of nonlinear systems with unknown system dynamics and external disturbance. In the proposed control system, an adaptive TSKRCMAC is used to mimic an ideal backstepping control, and a robust controller is designed to attenuate the effect of the residual approximation errors and external disturbances with desired attenuation level. Moreover, the all adaptation laws of the RIBC system are derived based on the Lyapunov stability analysis, the Taylor linearization technique and control theory, so that the stability of the closed-loop system and tracking performance can be guaranteed. The proposed control system is applied to control an inverted pendulum system, a Genesio chaotic system and Sprott circuit system. Simulation results demonstrate that the proposed control scheme can achieve favorable tracking performances for the uncertain nonlinear systems with unknown dynamic functions and under the occurrence of external disturbance.