本論文主旨在探討針對一個參數變動混沌系統的最佳化軌跡追蹤器設計。首先，我們使用最佳線性化的方法去獲得一個離散時間、非線性、非時變系統的線性模型，使得傳統追蹤器的設計法則可以適用在非線性系統上，再將已設計好的追蹤器，利用狀態吻合數位再設計的方法去設計一個預測基底的數位追蹤器。然後，我們再進一步討論參數變動的系統。由於本文提出的參數變動系統的參數是在一有界的範圍內變動，故我們引用遺傳演算法來搜尋最佳化控制器，此法是一種強者生存並自我繁殖變化，進而成為更突出個體的演算法。最後，最佳化追蹤器的實現，也代表此系統最差情形的實現，將以上兩者一起在本文中討論是為了證明此最佳化追蹤器的效用。

The nominal optimal tracker for the chaotic nonlinear interval system is first proposed in this thesis. First, we use an optimal linearization methodology to obtain the exact linear models of a class of discrete-time nonlinear time-invariant systems at operating states of interest, so that the conventional tracker can work for the nonlinear systems. A prediction-based digital tracker using the state-matching digital redesign method from a predesigned, state-feedback, continuous-time tracker for a hybrid chaotic system is presented. Then, we discuss the system has interval parameters. The interval system treated has interval and bounded parameters. The proposed evolutionary programming (EP) technique yields the strongest species to survive, reproduces themselves, and creates more outstanding offspring. The worst-case realization of the sampled-data nonlinear uncertain systems represented by the interval form with respect to the implemented "best" tracker is also found in this thesis for demonstrating the effectiveness of the proposed tracker.

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