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研究生:王忍忠
研究生(外文):Jen-Chung Wang
論文名稱:小型無人直升機群之分散式智慧型編隊控制
論文名稱(外文):Intelligent Distributed Formation Control of Small-Size Unmanned Helicopters
指導教授:蔡清池
口試委員:李祖聖黃國勝林惠勇林志民蘇順豐莊家峰
口試日期:2016-07-20
學位類別:博士
校院名稱:國立中興大學
系所名稱:電機工程學系所
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:2016
畢業學年度:104
語文別:英文
論文頁數:126
中文關鍵詞:編隊控制系統小型無人直升機實數型編碼基因演算法彈性帶方勢能場粒子群演算法適應式軌跡追蹤控制基函數網路輪廓飛行
外文關鍵詞:formation control systemsmall-size unmanned helicopterreal-coded genetic algorithmelastic band techniquepotential fieldparticle swarm optimizationadaptive trajectory tracking controlfuzzy basis function networkscontour flight
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本論文探討多架小型無人直升機群在具有靜態與動態障礙物的複雜模擬環境中,提出一種分散式群組控制系統架構,不僅可達成多機編隊之隊形保持而且可完成無碰撞的軌跡追踪與輪廓飛行。為建置完整編隊控制系統,本文的主要研究課題可區分為三大核心技術。第一是針對單架小型無人直升機飛行,提出利用基函數網絡來發展智慧自適應軌跡追踪控制方法,用以實現低空飛行和輪廓飛行。第二是運用實數型編碼基因演算法與彈性帶技術來提出一整合型路徑規劃法,用以搜尋單機的全域近最佳,平滑與可行的飛行路徑。第三是使用協同演算法、勢能場、粒子群演算法,以及實數型編碼基因演算法,建立一個多架小型無人直升機之分散式領航-跟隨協同編隊控制系統架構,使小型無人直升機系統可成功地達成穿越複雜地形輪廓的編隊飛行。最後藉由一個可靠的小型無人直升機非線性模型,進行多個數值模擬與性能比較研究,用以驗證所提方法之有效性與優越性。

This dissertation presents a distributed formation control system frame for a team of multiple small-size unmanned helicopters (SSUHs) flying together over complicated environments with static and dynamic obstacles, in order to achieve not only for formation keeping but also for collision-free contour trajectory tracking and contour flight. To synthesize the overall formation control system, the main research topics of the dissertation are classified into three core techniques. First, an intelligent adaptive trajectory tracking control method using fuzzy basis function networks is proposed for a single SSUH to perform low-level and contour motion control. Second, an integrated global path planning method using real-coded genetic algorithm (RGA) and elastic band technique is presented to search for the near-optimum, smooth and feasible contour flight routes over a wide variety of terrain. Third, a distributed leader-follower consensus formation control system is established and examined by utilizing a formation trajectory generation method using consensus algorithm, potential field (PF), particle swarm optimization (PSO) and real-coded genetic algorithm for a multi-SSUH system successfully accomplishing cooperative contour flight over complex environments. The effectiveness and merits of all the proposed methods are well exemplified by conducting several simulations on a verified SSUH nonlinear model.

摘 要 i
Abstract ii
CONTENTS iii
LIST of TABLES vi
LIST of FIGURES vii
NOMENCLATURE xiii
ACRONYM xxi

Chapter 1 Introduction 1
1.1. Research Background 1
1.2. Literature Review 8
1.2.1. modeling and flight control 8
1.2.2. Global Path Planning 10
1.2.3. Formation Control Methodologies 12
1.3. Motivation and Objectives 13
1.4. Contributions of the Dissertation 15
1.5. Dissertation Organization 16
Chapter 2 SSUH Dynamics and FBFN controller design 19
2.1. SSUH dynamical model 20
2.2. FBFN Approximation to the Coupling Effect 23
2.3. FBFN-based flight control system 25
2.3.1. Adaptive Backstepping Control Augmented by FBFN 26
2.3.2. Closed-Loop Stability Analysis and Parameter Updating 31
2.4. Simulations and Discussion 34
2.4.1. Nonlinear Modeling and Simulation Setup for a SSUH 34
2.4.2. Performance Indices 36
2.4.3. Hovering with Airdrop 37
2.4.4. Trajectory Tracking with External Disturbances 39
2.5. Concluding Remarks 41
Chapter 3 Three-dimensional global path planning for Contour flight 43
3.1. Global navigation point planning based on RGA 44
3.1.1. Real-coded Chromosome 44
3.1.2. Evolutionary Operators 45
3.1.3. Evolution Cost Function 45
3.1.4. Global Navigation Point Planning Algorithm 46
3.2. Obstacle avoidance trajectory using elastic band technique 47
3.2.1. Initial Path Build-up 48
3.2.2. Elastic Band Deformation 49
3.2.3. Bubble Reorganization 51
3.2.4. Trajectory Smooth 52
3.2.5. Algorithm for an Elastic Band Trajectory Generation 55
3.2.6. Integrated global trajectory planning algorithm 56
3.3. Simulation Results and Discussion 57
3.3.1. Flight Planning with Idea Conditions 59
3.3.2. Contour Flight with Four Blocking Threats 65
3.3.3. Contour Flight with One Moving Threat 69
3.4. Concluding Remarks 71
Chapter 4 Potential-Field-Based Distributed Formation Control Using Consensus Algorithms and PSO-RGA 73
4.1. Modeling a Multi-SSUH System 74
4.2. Problem Statement 76
4.3. Improved 3D Potential Field and Consensus Algorithm 77
4.3.1. Virtual Leading Force 78
4.3.2. Mutual Influencing Force 80
4.3.3. Collision-Avoidance Force 80
4.3.4. Obstacle-Avoidance Force 81
4.3.5. Total MPF Forces 83
4.4. Exponential Stability and Consensus Tracking 84
4.5. Parameter Tuning of the MPF Formation Controller using PSO-RGA 90
4.6. Simulations and Discussion 93
4.7. Concluding Remarks 111
Chapter 5 Conclusions and Recommendations 113
5.1. Conclusions 113
5.2. Recommendations 114
Bibliography 117



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