臺灣博碩士論文加值系統

PID控制器廣泛應用於控制系統中，主要原因在於其結構簡單以及具有不錯的強韌性，然而影響系統性能表現的控制器參數決定並不容易，因此目前發展出一些方法主要在於不會耗費太多時間的情況之下來決定其最佳之控制器之參數[12]。在本論文中提出使用遺傳基因演算法（Genetic Algorithm）與粒子群聚最佳化（Particle Swarm Optimization）演算法來調整PID控制器之參數，用以解決在船舶運動控制中的航向控制問題。經由上述兩種生物演算法調整後，PID控制器比使用Zeigler Nichols經驗法則調整控制器參數的整體系統性能響應更為理想。本文所提出之PID控制器經由遺傳基因演算法及粒子群聚最佳化演算法調整後對於船舶航向保持（course keeping），航向變換（course changing）等運動均可達穩定控制之目的，提升船舶自航器之控制性能響應表現，並在於船舶運動控制中使系統更具有其實用性。

PID controllers used in industrial control processes because of their simple structure and robust performance in wide range of operating conditions. So far, great effort has been devoted to develop methods to reduce the time spent on optimizing the choice of controller parameters[12]. In this thesis, we present the methods of tuning parameters of PID controllers in the ship manoeuvring problem by genetic algorithm and particle swarm optimization. It is found that the PID controllers adjusted by these two biological algorithms is more ideal than that by traditional experience methods. The PID controller which is adjusted by genetic algorithm and particle swarm optimization can control ship well in course keeping, course changing and course tracking. There are some numerical examples to demonstrate the advance of the proposed controller.

目錄
中文摘要 I
英文摘要 II
表目錄 III
圖目錄 IV
第一章 緒論 1
1.1 動機與目的 1
1.2 論文大綱 2
第二章 遺傳基因演算法 3
2.1 遺傳基因演算法之理論 4
2.1.1遺傳基因演算法之流程 4
2.2 離散型遺傳基因演算法 6
2.2.1離散型遺傳基因演算法之流程 6
2.3 實數型遺傳基因演算法 9
2.3.1實數型遺傳基因演算法之流程 9
第三章 粒子群最佳化演算法 12
3.1粒子群最佳化演算法之理論 13
3.1.2 粒子群最佳化演算法之流程 15
第四章 遺傳基因演算法及粒子群最佳化演算法與PID控制器之整合
應用 17
4.1 PID控制器之簡介 18
4.1.1 傳統PID控制器 18
4.1.2 PID控制器參數傳統調整之方法 20
4.2 遺傳基因演算法基於PID控制器之架構 23
4.3 粒子群最佳化演算基於PID控制器之架構 26
4.4 船舶於航道上之控制 32
4.4.1 船舶運動動態方程 32
4.5 模擬結果 34
第五章 結論與未來的展望 45
5.1 結論 45
5.2 未來的展望 46
參考文獻 47

參考文獻

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