本論文提出一個以實數型基因編碼之去氧核糖核酸演算法(DNA-RGA Computing Algorithm)。此演算方法係結合基因與DNA演算法之特點，去設計模糊控制器以改善整個迴授系統響應。此方法特點在於演化過程中不只對於參數進行最佳化，也能對系統架構做出最合適的選擇。此概念有別於其它演化演算法只專注於數值的搜尋。DNA是由四種不同鹼基組合而成之染色體序列，其中更包含大量遺傳資訊並透過世代演化進行傳承。DNA演算法擁有基因演算法之特性，包括選擇、交配、突變與精英策略等運算機制。除此之外，DNA擁有包含酶(Enzyme)與病毒(Virus)二種突變方式，藉以調整系統結構。
為了驗證所提方法之優點，將類PID模糊控制器四種架構編碼成染色體序列，利用所提出之演算進行演化。針對不同系統與對應不同的控制目標時，演算過程將從中選擇合適之控制器架構並同時對控制器之參數進行最佳化。最後，藉由模擬結果證明此方法之有效性與可行性。

In this thesis, a novel computing methodology called DNA-RGA computing algorithm,
which combines the characteristics of DNA and Genetic algorithm, is proposed to design
fuzzy controller and to improve the performance of systems. This algorithm presents a new
idea to optimize the parameters and structure of controller simultaneously.
DNA is known to be a chromosome string, which consists of four kind of chemical
components and is able to transmit huge amount of hereditary information from generation
to generation. DNA computing algorithm involves the basic and traditional operations of
GA algorithms such as selection, crossover, mutation, and elite strategy. In addition, the
DNA has an extra operational mechanism that includes enzyme and virus operators to
provide flexibility for the structure of systems.
In order to explore the major merit of DNA-RGA computing algorithm in the field of
control systems, this thesis presents a variable PID-Like fuzzy controller design based on
aforementioned methodology to attain the proper type of controllers, such as P-Like,
PD-Like, PI-Like, or PID-Like fuzzy controllers, corresponding to different plants. Finally,
the simulation results demonstrate the validity and feasibility of the proposed
methodology.

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