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研究生:張嘉崴
研究生(外文):Chang, Chia-Wei
論文名稱:基於動態模型之控制系統實現
論文名稱(外文):Control System Implementation Based on Dynamic Models
指導教授:邱智煇
指導教授(外文):Chiu, Chih-Hui
口試委員:魏榮宗彭椏富
口試委員(外文):Wai, Rong-JongPeng, Ya-Fu
口試日期:2019-07-26
學位類別:碩士
校院名稱:國立臺灣海洋大學
系所名稱:通訊與導航工程學系
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:2019
畢業學年度:108
語文別:英文
論文頁數:197
中文關鍵詞:電動平衡載具動態模型模糊控制器小腦模型控制器類神經網路
外文關鍵詞:Vehicle systemModel basedFuzzy controllerCMACneural network
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本論文將基於各個系統模型,並實現於電動獨輪車、電動雙輪載具以及改良型電動獨輪車系統中。其三種載具系統藉由直流馬達的動力使載具平台維持於平衡點,並依照騎乘者之重心移動,使車身移動,達到車體移動的目的。而系統中皆是利用牛頓運動定律使系統保持平衡。這樣的平衡載具系統實現能達到成為代步工具之目標,並利用電池做為能源,降低交通工具所造成之空氣汙染。
為了完成平衡載具的平衡控制,針對各個系統使用不同的平衡控制器,以載具平台之傾斜之角度以及角速度做為控制變數,經過控制器運算後,傳送命令至直流馬達,並在馬達出力旋轉後達到載具維持平衡不倒之目標。本論文中各個系統有各自推導出的動態模型,並使用不同的控制器。在此先使用MSC.ADAMS與MATLAB/SIMLINK的聯合模擬對動態模型進行驗證,再設計出根據動態模型的各個控制器。
對於電動獨輪車,使用強健適應性輸出遞迴仿第二型模糊控制器做為平衡控制器;在電動雙輪載具方面,使用了強健適應性輸出遞迴仿第二型小腦膜性控制器做為平衡控制器;最後在改良型電動獨輪車中,使用了強健適應型輸出遞迴粒子群優派翠Elman類神經網路控制器做為平衡控制器。以上三種控制器皆使用高斯函數做為歸屬函數,其中輸出遞迴改善了控制器為靜態的缺點,而強健控制器包含了所推導控制系統的動態模型,使得控制系統在面對外擾以及不確定因素時能夠有更佳的反應。再者,藉由李亞普諾夫穩定性(Lyapunov Stability)分析推導以達到誤差收斂之目的。最後,透過模擬以及實驗結果,證明了基於動態模型之控制器能夠實現於各系統中,並在平衡控制上有不錯的表現。
The purpose of this thesis is to implement the control system based on each dynamic models. The co-simulation of MSC.ADAMS and MATLAB/SIMULINK is used here to verify the derived dynamic model. Then design each controller to each control system based on the dynamic models. In this study, a robust adaptive output recurrent imitate type-2 fuzzy controller is proposed for the electric unicycle, robust adaptive output recurrent imitate type-2 cerebellar model articulation controller is applied to electric two-wheel vehicle, and robust adaptive output recurrent particle swarm optimization (PSO) Petri Elman neural network controller is proposed for modified electric unicycle vehicle. The output recurrent technique make the static controller to dynamic. The robust controller contains the derived dynamic model of each control system, which make the control system has better performance when facing the external disturbance and uncertainties. When a person riding on the vehicle system, the control strategy is proposed for real-world moving control. The main object of this thesis is to implement a self-dynamic balancing control system. Owing to the nonlinear and time varying characteristic of the vehicle system, an adaptive control method based on the dynamic models of control system is designed. The Lynapunov stability analysis is applied here to guarantee the convergence of tracking error. In the end, the results of simulation and experiment verify the performance of the balance controller to each vehicle system.
審定書 I
授權書 II
摘 要 III
ABSTRACT IV
誌謝 V
Content VI
List of Figures IX
List of Tables XV
Chapter 1 Introduction 1
1.1 Foreword 1
1.2 Research background 2
1.3 Research object 6
Chapter 2 Electric Unicycle 8
2.1 Electric unicycle system 8
2.1.1 Mechanism 8
2.1.2 Main controller 9
2.1.3 Electric circuit 10
2.1.4 Inertia measurement unit 10
2.1.5 Power system 11
2.2 Mathematical model 13
2.3 Robust adaptive output recurrent imitate interval type-2 fuzzy controller 17
2.3.1 Overview 17
2.3.2 Controller design of different variance value of output recurrent imitate interval type-2 fuzzy controller 18
2.3.3 Controller design of different mean value of output recurrent imitate interval type-2 fuzzy controller 20
2.3.4 Robust adaptive output recurrent imitate interval type-2 fuzzy controller 21
2.4 Simulation on MATLAB 25
2.4.1 Simulation of the different variance value type 25
2.4.2 Simulation of the different mean value type 28
2.5 Simulation on ADAMS 31
2.5.1 Simulation of the different variance value type 33
2.5.2 Simulation of the different mean value type 35
2.6 Experiment 38
2.6.1 Experiment of the different variance value type 38
2.6.2 Experiment of the different mean value type 53
Chapter 3 Electric Two-wheeled Vehicle 69
3.1 Electric two-wheeled vehicle system 69
3.1.1 Mechanism 69
3.1.2 Main controller 70
3.1.3 Electric circuit 71
3.1.4 Inertia measurement unit 71
3.1.5 Power system 72
3.2 Mathematical model 74
3.3 Robust adaptive output recurrent imitate type-2 cerebellar model articulation controller 78
3.3.1 Overview 78
3.3.2 Controller design of different variance value of output recurrent imitate type-2 CMAC 79
3.3.3 Controller design of different mean value of output recurrent imitate type-2 CMAC 80
3.3.4 Robust output recurrent imitate type-2 CMAC 82
3.3.5 Convergence analysis 86
3.4 Simulation on MATLAB 87
3.4.1 Simulation of the different variance value type 87
3.4.2 Simulation of the different mean value type 91
3.5 Simulation on ADAMS 95
3.5.1 Simulation of the different variance value type 97
3.5.2 Simulation of the different mean value type 99
3.6 Experiment 102
3.6.1 Experiment of different variance value type 102
3.6.2 Experiment of different variance value type 122
Chapter 4 Modified Electric Unicycle Vehicle 143
4.1 Modified Electric Unicycle vehicle system 143
4.1.1 Mechanism 143
4.1.2 Main controller 144
4.1.3 Electric circuit 145
4.1.4 Inertia measurement unit 145
4.1.5 Power system 145
4.2 Mathematical model 147
4.3 Robust adaptive output recurrent PSO Petri Elman neural network controller 151
4.3.1 Overview 151
4.3.2 Controller design of output recurrent PSO Petri Elman neural network 154
4.3.3 Robust output recurrent PSO Petri Elman neural network controller 156
4.3.4 Convergence analysis 160
4.4 Simulation on MATLAB 161
4.4.1 Balance simulation 161
4.4.2 External disturbance simulation 162
4.5 Simulation on ADAMS 164
4.5.1 Balance simulation 165
4.5.2 External disturbance simulation 166
4.6 Experiment 168
4.6.1 Forward balance experiment 168
4.6.2 Backward balance experiment 172
4.6.3 External disturbance experiment 176
4.6.4 Left turn balance experiment 180
4.6.5 Right turn balance experiment 185
Chapter 5 Conclusions and future researches 190
5.1 Conclusions 190
5.2 Future Researches 193
I. Mechanism improvement 193
II. Motor improvement 193
III. Micro controller improvement 193
Reference 194
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