本論文探討使用回授線性化的方法，解決非線性系統控制問題。回授線性化的方法主要將非線性系統轉換成等效線性系統後，再使用傳統線性系統技巧來解決問題。本項研究提出了一種新穎的回授線性化與前饋類神經網路控制器獲得幾乎雜訊消除的效能。此外，經由本文提出的複合式里亞普諾應用方法，可驅動系統軌跡進入全域終極吸子而獲得終極均勻有界穩定度。設計回授線性化與前饋類神經網路控制器之整合控制器解決對象包含單輸入單輸出及多輸入多輸出非線性系統。對整個閉迴路系統給定初始條件下，本論文所提出的控制定律，可使得系統指數全域穩定且同時獲得可幾乎雜訊消除效能。而且，如果提出的充分條件被滿足，則所提出新的方法將驅動整個系統的軌跡誤差進入全域終極吸子，再調整權重以提高性能達到幾乎雜訊消除效能。將本論文所發展的定律應用到實務非線性系統上，包含雙螺旋槳多輸入多輸出系統、衛星系統及雙倒單擺大尺寸系統。實驗模擬結果顯示，所提供研究法可以成功的快速收斂穩定及具幾乎雜訊消除效果。

This dissertation investigates feedback linearization method for nonlinear systems. The main part of feedback linearization method is to transform the nonlinear system into an equivalent linear system, and then traditional linear techniques are applied to solve it. This dissertation proposes a novel feedback linearization and feedforward neural network controller design to achieve almost disturbance decoupling (ADD) performance. In addition, the composite Lyapunov approach is utilized to drive the system trajectories into the global attractor and achieve the ultimately uniform bounded stability. Feedback linearization and feedforward neural network integrated controller are designed for both single-input single-output and multi-input multi-output nonlinear systems. The control law is proposed to achieve a global exponential stability and almost disturbance decoupling performance for overall closed loop system with given initial conditions. Moreover, if some sufficient conditions is satisfied, the proposed controller will drive the entire errored system into the global attractor, and then adjust the weights to improve the almost disturbance decoupling performance performances. The proposed control methodology has been successfully applied to practical nonlinear systems including the twin rotor multi-input multi-output system (TRMMS), the satellite system, and the two-inverted-pendulum large-scale system. Simulations show that the proposed methodology has been successfully applied to give fast convergence with ADD performance of the overall system.

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