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研究生:王興夫
研究生(外文):Xing-Fu Wang
論文名稱:網路式異質全向多機器人之智慧型適應分散式協同編隊控制
論文名稱(外文):Intelligent Adaptive Distributed Consensus Formation Control for Uncertain Networked Heterogeneous Swedish-Wheeled Omnidirectional Multi-Robots
指導教授:蔡清池
口試委員:黃旭志黃國勝余國瑞林惠勇
口試日期:2017-07-31
學位類別:碩士
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:2017
畢業學年度:105
語文別:中文
論文頁數:95
中文關鍵詞:分散式協同編隊控制四輪全向輪型機器人三輪全向輪型機器人輸出遞迴模糊小波類神經網路倒逆步終端滑膜控制
外文關鍵詞:Distributed consensus formation controlSwedish four-wheeled omnidirectional robotSwedish three-wheeled omnidirectional robotOutput recurrent fuzzy wavelet neural networkBacksteepingTerminal sliding mode control
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本篇論文提出一組包括全向四輪(SFWORs)和全向三輪(STWORs)異質多機器人系統的分散式協同型編隊控制方法學與技術,簡單的描述全向三輪與四輪的動力學和運動學模型,異質多機器人編隊控制系統及其機台間所使用的通訊技術。為了達成異質多機器人系統的隊形維持跟協同追蹤,本篇提出了兩種分散式協同編隊控制方法,其中一個方法使用自適應型倒逆步(adaptive backstepping)技術,另一個則使用適應型終端滑模控制(adaptive terminal sliding mode)技術。此外上述的兩種技術都使用即時學習的輸出遞迴類神經模糊小波類神經 (ORFWNNs)。一種防碰撞避障策略被提出,用以達成機器人間與障礙物的避障。最後透過數值分析與異質平台的實驗結果證明所提出方法的有效性與實用性。
This thesis presents methodologies and techniques for distributed consensus-based formation control of a group of networked heterogeneous multiple Swedish wheeled omnidirectional robots(SWORs) system including Swedish three-wheeled omnidirectional robots(STWORs) and Swedish four-wheeled omnidirectional robots (SFWORs) with uncertainties. After describing the kinematic and dynamic models of STWORs and SFWORs, the heterogeneous formation control system with the communication principle is designed and implemented. To achieve formation keeping and consensus tracking, this thesis proposes two distributed consensus formation control methods. One is an intelligent adaptive backstepping technique, and the other is an adaptive terminal sliding mode (TSMC) technique. In addition, both controllers use online learning of output recurrent fuzzy wavelet neural networks (ORFWNNs) which are employed to learn the system uncertainties. Moreover, a collision and obstacle avoidance strategy is proposed to avoid obstacle and collision among robots. Through numerical simulations and the experiment results of platform including SFWORs and STWORs on the built heterogeneous formation control system, the proposed methods have been shown effective in achieving the formation control goals.
誌謝辭..........i
摘 要..........ii
Abstract..........iii
Contents..........iv
List of Figures..........vii
List of Tables..........xi
Nomenclature..........xii
List of Abbreviations..........xiii
Chapter 1 Introduction..........1
1.1 Research Background..........1
1.2 Literature Review..........2
1.2.1 Related Work on Cooperation Consensus Formation Control..........2
1.2.2 Related Work on Heterogeneous Swedish Omnidirectional Robots..........3
1.2.3 Related Work on ORFWNN..........3
1.2.4 Related Work on Backstepping Control....................4
1.2.5 Related Work on Terminal Sliding Mode Control....................4
1.3 Motivation and Objectives....................5
1.4 Main Contributions....................5
1.5 Thesis Organization....................6
Chapter 2 Formation System Design and Implementation 8
2.1 Introduction..........8
2.2 Distributed Consensus Formation System Structure..........9
2.2.1 Diagram of Heterogeneous Multi-SWOR Formation System..........9
2.2.2 Diagram of each Heterogeneous SWOR in Formation..........9
2.3 Description of the Experimental SFWOR and STWOR in Formation..........11
2.3.1 Basic Structure of SFWOR and STWOR..........11
2.3.2 ARM-based Controller..........13
2.3.3 Single-Board Computer..........15
2.3.4 Dynamixel MX-64AR Robot Actuator..........16
2.3.5. Odometry..........18
2.3.6 Omnidirectional Wheels..........20
2.3.7 The Wi-Fi communication program..........20
2.4 Modeling a Heterogeneous Multi-SWOR System..........21
2.5 Problem Formulations..........21
2.6 Kinematic Formation Control..........23
2.6.1 Kinematic model and control of the ith SWOR..........23
2.7 Experimental Results and Discussion..........26
2.7.1 Experimental Results of Odometry..........26
2.7.2 Experimental Results of the Kinematic Formation Control..........33
2.8 Concluding Remarks..........38
Chapter 3 Intelligent Adaptive Backstepping Distributed Consensus Formation Control Using ORFWNN..........39
3.1 Introduction..........39
3.2 Unified Dynamic Model of the Heterogeneous SWOR..........39
3.2.1 Modeling a SFWOR in the World Frame..........39
3.2.2 Modeling a STWOR in the World Frame..........41
3.2.3 Modeling any SWOR in the World Frame..........42
3.3 ORFWNN Function Approximation..........43
3.4 Intelligent Adaptive Backstepping Distributed Consensus Formation Control..........45
3.5 The Strategy of Collision and Obstacle Avoidance..........49
3.5.1 Collision and Obstacle Avoidance..........50
3.5.2 Stability Analysis of the Formation Control Law with Collision and Obstacle Avoidance..........51
3.6 Simulation Results and Discussion..........52
3.7 Experimental Results and Discussion..........57
3.8 Concluding Remarks..........66
Chapter 4 Intelligent Terminal Sliding-Mode Formation Control Using ORFWNN..........68
4.1 Introduction..........68
4.2 Unified Dynamic Model of the Heterogeneous SWOR..........68
4.3 Intelligent TSMC Formation Control Using ORFWNN..........69
4.4 Simulation Results and Discussion..........74
4.5 Experimental Results and Discussion..........80
4.6 Concluding Remarks..........89
Chapter 5 Conclusions and Recommendations..........90
5.1 Conclusions..........90
5.2 Future Work..........91
References..........93
[1]R. Murray, “Recent research in cooperative control of multivehicle systems,” Journal of Dynamic Systems, Measurement, and Control, Vol. 129, pp. 571-583, 2007.
[2]L. E. Parker, “Distributed intelligence: overview of the field and its application in multi-robot systems,” Journal of Physical Agents, Vol. 2, No. 1, pp.1-14, March 2008.
[3]Y. Kuriki and T. Namerikawa, “Formation control of UAVs with a fourth-order flight dynamics,” in Proc. of IEEE 52nd Annual Conference on Decision and Control (CDC 13), pp. 6706-6711, 2013.
[4]Y. Kuriki and T. Namerikawa, “Consensus-based cooperative formation control with collision avoidance for a multi-UAV system,” in Proc. of 2014 American Control Conference (ACC), Portland, Oregon, USA, pp. 2077-2082, June 4-6, 2014.
[5]W. Ren, R. W. Beard, and E. M. Atkins, “Information consensus in multivehicle cooperative control,” IEEE Control Systems Magazine, Vol. 27, No. 2, pp. 71-82, 2007.
[6]C. Ren and C. L. Philip Chen, “Decentralized control for second-order uncertain nonlinear multi-agent systems consensus problem based on fuzzy adaptive high-gain observer,” in Proc. of 2013 IEEE International Conference on Systems, Man and Cybernetics, Manchester, UK, pp. 4935-4940, 2013.
[7]J. Mei and W. Ren and J.Chen, “Distributed consensus of second-order multi-agent systems with heterogeneous unknown inertias and control gains under a directed graph,” IEEE Transactions on Automatic Control, Vol. 61, No. 8,pp. 2019-2034, 2015.
[8]W.W. Yu, G. Chen, M. Cao and J. Kurths, “Second-order consensus for multi-agent systems with directed topologies and nonlinear dynamics,” Systems Man and Cybernetics Part B-Cybernetics, IEEE Transactions, Vol. 40, No.3 pp. 881-891, 2009.
[9]C. Chen, C.-E Zen, and T. Du, “Fuzzy observed-based adaptive consensus tracking control for second-order multi-agent systems with heterogeneous nonlinear dynamics,” to appear in IEEE Transactions on Fuzzy Systems, 2016.
[10]G. Indiveri, “Swedish wheeled omnidirectional mobile robots: kinematics analysis and control,” IEEE Transactions on Robotics, Vol. 25, No. 1, pp. 164-171, 2009.
[11]C. C. Tsai, Y. S. Chen, “Intelligent distributed collision-free consensus formation control for uncertain networked heterogeneous omnidirectional multi-robots with three Swedish wheels
[12]C. C. Tsai, Y. S. Chen, and F. C. Tai, “Intelligent adaptive distributed consensus formation control for uncertain networked heterogeneous Swedish-wheeled omnidirectional multi-robots,” in Proc. of the SICE 2016, Tsukuba International Congress Center , Tsukuba, Japan, September 20-23, 2016.
[13]C.C. Tsai, H. L. Wu, Y. R. Lee, “Intelligent adaptive motion controller design for Mecanum wheeled omnidirectional robots with parameter variations,’’ Intern. Journal of Nonlinear Sciences and Nonlinear Simulation, vol. 11, supplement issue, pp. 091-95, 2010.
[14]D.W.C. Ho, P.A. Zhang, and J. Xu, “Fuzzy wavelet networks for function learning,” IEEE Trans. on Fuzzy Systems, vol. 9, no.1, pp.200-211, Feb. 2001.
[15]C. H. Lee and H. S. Chang, “Output recurrent wavelet neural network-based adaptive backstepping controller for a class of MIMO nonlinear non-affine uncertain systems,” Neural Computing and Applications, vol. 24, no. 5, pp. 1035-1045, April 2014
[16]C. C. Tsai, H. L. Wu, F. C, Tai, and Y. S. Chen, “Adaptive backstepping decentralized formation control using fuzzy wavelet neural networks for uncertain Mecanum-wheeled omnidirectional multi-vehicles,” in Proc. of the 2016 IEEE International Conference on Industrial Technology, Taipei, Taiwan, 15-17 March, 2016.
[17]C. C. Tsai, H. L. Wu, and K. H. Chuang, “Backstepping aggregated sliding-Mode motion control for automatic 3D overhead cranes”, IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), pp.849-854, 2012.
[18]H. K. Khalil, Nonlinear systems, 2nd ed., Prentice Hall, 1996.
[19]C. E. Ren and C. L. Philip Chen, “Sliding mode leader-following consensus controllers for second-order non-linear multi-agent systems,” IET Control Theory Appl., vol. 9, no. 10, pp. 1544–1552, 2015.
[20]X. Yut, Z. Man , and Y. Wut, “Terminal sliding modes with fast transient performance,” in Proc. of the 36th Conf. on Decision and control, San Diego, California, USA, pp. 952-953, December 1997.
[21]C. C. Tsai, and H. L. Wu, “Nonsingular terminal sliding control using fuzzy wavelet networks for Mecanum wheeled omnidirectional vehicles,” Proc. of the 2010 IEEE International Conference on Fuzzy Systems, Barcelona, Spain, 18-23 July 2010.
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