本文將針對多輸入多輸出的典型與非典型的未知非線性系統，提出一個輻狀基底類神經網路適應性控制器，在針對典型未知非線性系統的控制器的架構中，我們運用輻狀基底類神經網路來近似未知的非線性函數，並且利用適應律來調整輻狀基底類神經網路的權重值，在運用倒階法來設計控制器時會造成基底多次微分的問題，而這個問題會往往使得設計出來的控制器無法運用在高階的系統，也因此我們在設計控制器的過程中加入一階濾波器來避免這類問題的發生，在針對非典型未知非線性的控制器的設計上，運用的方法與針對典型未知非線性系統的控制器相似，都是需要用到輻狀基底類神經網路來近似未知的非線性函數，以及用適應律來調整輻狀基底類神經網路的權重值，最後再加入一階濾波器來避免基底多次微分的問題，不過在這之前我們利用均值定理的特性，將控制器與虛擬控制器從非典型的函數中分離出來，才可利用設計好的控制器加以控制，當然我們也會運用李亞普諾夫定理來證明此控制器可以使系統達到穩定。最後附上一些範例模擬，從模擬圖中可得知系統輸出值會盡追蹤到參考訊號。

This thesis proposes a radial basis function neural network adaptive backstepping controller (RBFNN_ABC) for multiple-input multiple-output (MIMO) affine and nonaffine nonlinear systems in block-triangular form. The control scheme incorporates the adaptive neural backstepping design technique with a first-order filter at each step of the backstepping design to avoid the higher-order derivative problem, which is generated by the backstepping design. This problem may produce an unpredictable and unfavorable influence on control performance because higher-order derivative term errors are introduced into the neural approximation model. Finally, simulation results demonstrate that the output tracking error between the plant output and the desired reference output can be made arbitrarily small.
Keywords: Radial basis function (RBF) neural networks (NNs), adaptive, backstepping, MIMO nonlinear systems.

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