臺灣博碩士論文加值系統

本篇論文針對含有模式不確定性之直流無刷線性馬達設計強健追蹤控制器。控制器之性能指標包含頻域及時域，此設計可同時兼顧強健穩定性、最佳控制能量以及暫態響應。對於控制器主體的設計，我們提出雙參數方式之改良式PID控制器，分別透過基因演算和進化演算的平行策略搜尋確保參數設計達到性能的要求。
本論文同時考慮混合 性能指標最小化的多目標評價泛函數(multiple objective cost functional)求解之問題，該設計係使得最終決定之控制器同時滿足混合時域及頻域的性能規格，其目的有二，(i)應用 靈敏度函數呈現系統的頻域性能，(ii)應用 追蹤誤差函數界定系統的時域性能，同時以頻域強健穩定條件限制控制參數的選定範圍以確保閉路系統的強健穩定性。針對此受限制之多目標評價函數，我們提出一種求解該問題的進化策略(evolutionary strategy)，來快速求解。
對於本論文所提出之設計法，我們均輔以廣泛的模擬和實驗驗証，其結果並與一些傳統PID控制器設計法加以比較，以充份確認這些方法的可行性和優越性。

This thesis presents a robust output tracking control design method for a linear brushless DC motor with modeling uncertainties. Two frequency-domain specifications directly related to the mixed sensitivity function and control energy consumption are imposed to ensure stability and performance robustness. With regard to time-domain specifications, rise time, maximum overshoot and steady state error of the step response are considered. A generalized two-parameter PID control framework is developed via genetic searching and evolutionary searching approaches to ensure the specifications imposed. The proposed design method is intuitive and practical that offers an effective way to implement simple but robust solutions covering a wide range of plant perturbation and, in addition, if provides excellent tracking performance without resorting to excessive control.
A multiple objective controller design specifications directly related to the mixed / control are also imposed which directly related to the stability robustness and optimal control on the time and frequency domains. An advanced evolution strategy is developed to solve the constrained multi-objective problem; it is a global search algorithm thus avoids trapping into the local minimum. The new design paradigm offers a way to implement simple but robust solutions that covers a wide range of plant perturbations and provides excellent tracking performance.
For the proposed design methods, complete simulation and experimental studies are performed to verify the performance and applicability of our proposed design.

感謝ii
摘要iii
Abstractiv
Contentsv
List of Figuresvi
List of Tablesviii
Chapter 1 Introduction1
Chapter 2 Preliminary5
2.1 Genetic Algorithms5
2.2 Evolutionary Algorithms9
2.3 Linear DC Brushless motor12
Chapter 3 GA-Based and EA-Based Multiobjective PID Control17
3.1 Two-parameter PID Control Scheme17
3.2 Mixed Time/Frequency Domain Specifications20
3.3 GA-Based PID Parameter Design23
3.4 Genetic Algorithms v.s. Evolutionary Algorithms28
3.5 EA-Based PID Parameter Design29
Chapter 4 EA-Based Mixed / Multiobjective PID Control33
4.1 Mixed / Control Specifications Design33
4.2 Multiobjective Optimization Design36
Chapter 5 Numerical and Experimental Results40
Chapter 6 Conclusions48
References49

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