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研究生:吳佳諦
研究生(外文):Chia-Ti Wu
論文名稱:基於粒子群最佳化演算法的混合控制器用於FIRA足球機器人以及半主動式汽車懸吊模擬
論文名稱(外文):A hybrid controller for FIRA and semi-active suspension simulation with Particle Swarm Optimization (PSO) Algorithm
指導教授:王啟州
指導教授(外文):Chi-Jo Wang
學位類別:碩士
校院名稱:南台科技大學
系所名稱:電機工程系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2009
畢業學年度:97
語文別:英文
論文頁數:74
中文關鍵詞:粒子群最佳化演算法FIRA足球機器人半主動式汽車懸吊
外文關鍵詞:FIRA soccer robotsemi-active suspensionParticle Swarm Optimization (PSO) Algorithm
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這一篇論文主要提出了一個基於粒子群優化理論的混合控制的理論與策略,而且透過MATLAB 7.0的模擬將其應用在汽車的主動式懸吊模型上。在此模擬當中,這個混合式的控制理論,在懸吊彈簧的收斂時間以及抑制震動的結果呈現出比一般常見或者是廣泛使用的單一控制理論所生產出來的懸吊有更好的表現。因此,這個理論在提升駕駛人信心以及駕駛穩定度上的方面提出改善,提供開車者更加的舒服以及穩定。除此之外,一個經過改進的控制策略也被提出,來證明其在其他學習領域裡應用的可能性,比如說足球機器人比賽的模擬上。在足球機器人比賽的模擬中,其提供了機器人一個控制策略,讓它比起其他應用舊式策略控制的機器人有著更好的表現。那也希望藉著此一研究,可以為人類在未來對於自動化機器人的要求有小小的貢獻。
This thesis proposes a hybrid control method based on particle swarm optimization algorithm. We applied this method in the simulation of an active suspension system with MATLAB 7.0 to prove its function. In the simulation, this hybrid control strategy shows improvements on both optimization and convergence time; it also performs well when compared with other existing methods. As a result, this theory improves the confidence and stability of drivers in certain level, and it also provides drivers a better handling and comfort to drive their cars. Moreover, a revised control strategy is also proposed for showing the possibility that it can work and function in different field of study, like on the robot soccer game simulation. In this robot soccer game simulation, the proposed controlling strategy enables the robot to operate in a more effective way than other existing methods with only one optimization method. It is our hope that this controlling strategy could lead us one small step further to what human being is always trying to achieve- an automatically moving and behaving robot in the future.
Catalog

Abstract…………….……………………………………………………iii
Outline……………………………………………………………………v
Catalog of Figures..………………………….…………………………viii
Catalog of Tables………….……………….……………………………..x
Chapter 1 Introduction……………………………………………………1
1.1 Preface……………………………………………………..3
1.2 Motivation and purpose…………...………………………4
1.2.1 Optimization methods: Genetic algorithm………….4
1.2.2 Support vector machine……………………………..7
1.2.3 Neural network……...………………………………8
1.2.4 Particle swarm optimization algorithm…..………..10
1.3 Research of references………………...…………………12
1.3.1 PSO algorithm research in foreign countries……..12
1.3.2 PSO algorithm research inland……………………13
1.4 Structure of the thesis…………………………………….14
Chapter 2 Basic structure of suspension and construction………….…..17
2.1 Fundamental ideas of construction of car suspension…….17
2.2 Different types of car suspension……………………...20
2.2.1 Active car suspension……………..………..……...20
2.2.2 Semi-active car suspension……….……………….23
2.2.3 Passive car suspension…………………………….25
2.3 Semi-active car suspension model……………………..…..26
2.4 Suspension-building strategy………………………………29
Chapter 3 Robot soccer games…………….…………………….……...35
3.1 Introduction of Robot soccer games…….……………..35
3.2 Generally used models…………………………………….38
3.2.1 Simulation league……………………….……….39
3.2.2 Small size league..…………………….….……..39
3.2.3 Middle size league……………………...……….40
3.2.4 Standard platform league………………………..40
3.2.5 Humanoid league………………………………40
3.3 Software requirement……………………………………...41
3.4 Proposed methods………………………………………….44
Chapter 4 Computer simulation…………………………………………60
4.1 Semi-active car suspension without PSO algorithm….........61
4.2 Semi-active car suspension with PSO algorithm…………..63
4.3 Soccer robot without PSO algorithm……….………………65
4.4 Soccer robot with PSO algorithm……………..……………66
Chapter 5 Conclusion and forecast…………….…………………..……70
5.1 Conclusion………………..…………………………..…….70
5.2 Forecast…………..……….………………………….……..71
References……………………………….……………………….……..72


Catalog of pictures
Figure 1.1……….………………………..……………………………….5
Figure 1.2……………….………………..……………………………….7
Figure 1.3……….………………………..……………………………….8
Figure 1.4.……………………………….………………………………..9
Figure 1.5……………………………….…………………….…………10
Figure 2.1……………………………….……………………...………..21
Figure 2.2……………………….……………………………………….22
Figure 2.3……………….…………………………………...…………..23
Figure 2.4……….……………………………………………………….24
Figure 2.5………………………………………………………………..25
Figure 2.6……………………………………………………….……….26
Figure 2.7……………………………………………….……………….29
Figure 2.8……………………………………….…………………….....30
Figure 2.9……………………………….…………………………….…31
Figure 3.1……………………….………….……..……………………..38
Figure 3.2……………….…………………………...…………………..47
Figure 3.3……….………………………….……………..……………..47
Figure 3.4.………………………………….……………..……………..48
Figure 3.5……………………………………………….……………….49
Figure 3.6……………………………………….……………………….50
Figure 3.7……………………………….……………………………….53
Figure 3.8……………………….……………………………………….54
Figure 3.9……………….………………………….……………………57
Figure 4.1……….……………………………………………………….60
Figure 4.2…………………………………………………………….….61
Figure 4.3…………………………………………………….………….62
Figure 4.4…………………………………………….………………….63
Figure 4.5…………………………………….………………………….64
Figure 4.6…………………………….………………………………….65
Figure 4.7…………………….………………………………………….66
Figure 4.8………………………………………………………………..67


















Catalog of Tables
Table 1.1………………………………………………………………….7
Table 3.1…………………………..…………………………………….53
References
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