臺灣博碩士論文加值系統

人工智慧是一種最普遍也是最受歡迎的理論之一。本論文基於最佳化以及最佳控制理論為基礎進而發展一套最佳化控制方法，給設計者，針對不同型態的最佳化問題提供一種既快速又有效能的方法來改善其問題，而且能夠證明有能力在所有解答中找出最佳的解，並且將此種方法應用於機電系統上。
最佳化粒子群演算法 (PSO)，是一種具有高性能、強靭性，以及運算簡易的最佳化演算法。大部分對於 PSO 之研究，主要是以經驗為依據來加以設計，只有少部分是以粒子群的軌跡為主軸，作理論上的分析。並且利用改良式最佳化粒子群演算法(MPSO) 來鑑別系統上不易量出的參數，此 MPSO 新的方法是在傳統的 PSO 中加入「距離」之觀念以避免我們所找出的解不是系統的最佳解。藉由 MPSO 與 PSO 兩方法針對此系統之收斂特性分析，我們可以很清楚的發現，此 MPSO 的方法所找出來的解具有高效率、高品質的最佳解，而且又能夠改善系統響應的準確性。
本論文主要是探討過去有關最佳化粒子群演算法的文獻，並且將這些探討所得的結果應用在機構的系統鑑別，應用的系統有曲柄滑塊機構、LCD單軸機器手臂及Scott-Russell 機構等等。實驗結果的證明，我們可以發現這個理論是可以滿足我們機構設計上的需求。

Artificial intelligence is once of the most popular optimization algorithms. This dissertation focuses on developing a optimization and optimal control theory. Several solution searching strategies and mechanisms are proposed to improve solution searching capability and efficiency of population-based optimizers for dealing with different types of optimization problems. To verify the solution searching ability in the solution space, the proposed algorithm will be applied to mechanism systems.
This study mainly proposes an efficient modified particle swarm optimization (MPSO) method, to identify mechanism system with driven by a field-oriented PM synchronous motor. The parameters of many industrial machines are difficult to obtain if these machines cannot be taken apart. In system identification, we adopt the MPSO method to find parameters of the mechanism systems. This new algorithm is added with “distance” term in the traditional PSO’s fitness function to avoid converging to a local optimum. It is found that the MPSO method can obtain optimal high-quality solution, high calculation efficiency, and its feasibility and effectiveness.
All the proposed control schemes are applied to Slider-Crank mechanism system, LCD glass-handling robot system and Scott-Russell (SR) magnification mechanism system by simulation and experiment to demonstrate the effectiveness and advantages.

Abstract in English...... 1
Abstract in Chinese...... 2
Acknowledgements in Chinese...... 4
Contents .........................5
List of Figures...................7

Chapter 1 Introduction
1.1 Motivation and literature survey....9
1.2 Organization of thesis..............11

Chapter 2 Artificial Intelligence Algorithm
2.1 The particle swarm optimization theorem.........................12
2.1.1 The characteristic of the particle swarm optimization.......15
2.2 The modified particle swarm optimization theorem................16
2.3 The characteristic of the modified particle swarm optimization theorem....18

Chapter 3 The mechanism systems
3.1 The LCD Glass-handing Robot...........20
3.2 The Scott-Russell mechanism systems...23
3.3 The Slider-crank mechanism systems....26
3.3.1 Dynamic Modeling..................26
3.3.2 Governing equations...............29
3.3.3 Decouple the differential equations...30
3.3.4 Alternative dynamic modeling......33

Chapter 4 Application in the mechanism systems
4.1 The identify of an LCD Glass-handing Robot...........34
4.2 The identify of a Scott-Russell mechanism system.....35
4.3 The identify of a Slider-crank mechanism system......37

Chapter 5 Experimental Setup & Experimental Results
5.1 The LCD Glass-handing Robot...............38
5.1.1 Experimental setup....................38
5.1.2 Experimental results..................41
5.2 The identify of a Scott-Russell mechanism system.....46
5.2.1 Experimental setup....................46
5.2.2 Experimental results..................47
5.3 The identify of a Slider-crank mechanism system......50
5.3.1 Experimental setup....................50
5.3.2 Experimental results..................52

Chapter 6 Conclusions and Future work.........54
6.1 Conclusions...............................54
6.2 Future work...............................56

Appendix A: Dynamic formulation................57
Appendix B: Alternative dynamic modeling of a slider-crank mechanism.............60

References....................................62

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