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研究生:吳崇維
研究生(外文):Chung-Wei Wu
論文名稱:使用寬度學習系統的網路連結異質全向多台移動機器人模糊合作定位與分數階非奇異終端滑模合作編隊控制
論文名稱(外文):Fuzzy Cooperative Localization and Fractional-Order NTSM Formation Control Using Broad Learning System for Networking Heterogeneous Omnidirectional Multirobots with Dynamic Effects and Uncertainties
指導教授:蔡清池
口試委員:李祖聖黃國勝林惠勇余國瑞
口試日期:2019-01-28
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:2019
畢業學年度:107
語文別:英文
論文頁數:102
中文關鍵詞:模糊分佈分散式擴展信息濾波法分數階非奇異終端滑模控制寬度學習麥卡全向移動機器平台方向圖論
外文關鍵詞:FDDEIFFONTSMBLSHOMRdirected graph theoryLyapunov
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本論文針對具有動態效應與參數不確定性的三種麥卡全向移動機器所構成網路連結異質多機器系統,結合寬度學習系統,模糊分佈分散式擴展信息濾波法則和分數階非奇異終端滑模控制策略,提出合作定位與編隊控制方法。此網路連結異質多機器實驗系統由四輪麥卡全向輪移動機器、三輪全向輪移動機器及四輪全向輪移動機器所構成,每一移動機器安裝在多編碼器作方位推算,RGB-D感測器來辨識與量測已知的地標,並用雷射探測周圍環境與建圖。在合作定位部分,一種合作姿態初始化算法被提出來估計所有多機器人的初始位置和方向。在粗略確定所有多移動機器的初始姿態,一種結合寬度學習系統與模糊分佈分散式擴展信息濾波法則(BLS-FDDEIF)被提出來融合編碼器,RGB-D感測器以及雷射探測等感測器所測量的讀值,用以估測得到每個移動機器更準確的姿態。在合作編隊控制部分,每個移動機器不確定性的動態行為被以一含不確定項的簡化三輸入三輸出二階狀態方程來建模,並以方向圖論來建置異質多移動機器系統的模型,且利用基於Lyapunov的滑模控制理論以及配合寬度學習系統在不確定項的線上即時學習,提出一種智能寬度學習系統與分數階非奇異終端滑模編隊控制 (BLS-FONTSM)方法,進行合作編隊追蹤控制。具比較功能的電腦模擬被用以說明所提出BLS-FDDEIF與BLS-FONTSM等兩方法之有效性和優越性。經由網路連結異質全向移動多機器實驗系統所得的實驗數據,展示例證所提兩方法的可用與實用性。
This thesis presents novel fuzzy distributed decentralized extended information filtering (FDDEIF) method and fractional-order nonsingular terminal sliding-mode (FONTSM) control approach using broad learning system (BLS), in order to achieve cooperation localization and cooperative formation control for networking multiple heterogeneous omnidirectional mobile robots (HOMRs) with dynamic effects and uncertainties. The used multiple HOMRs are composed of three kinds of Mecanum-wheeled omnidirectional mobile robots, each of which uses a RGB-D sensor mounted on a robot to detect known landmarks and a laser sensor to detect the surroundings. In cooperative localization, a cooperative pose initialization algorithm is proposed to estimate the robot''s initial position and orientation. Once all the initial poses of the multirobots have been roughly determined, a FDDEIF method using the BLS, called BLS-FDDEIF, is presented to fuse the sensors’ measurements from the encoders, RGB-D sensor and laser scanner, in order to estimate more accurate poses of each HOMR. In formation control, the dynamic model of each uncertain HOMR is modelled by a reduced three-input, three-output second-order state equation with uncertainties, and the overall multirobot system is modeled by directed graph theory. By using the Lyapunov-based sliding-mode theory and online learning of the system uncertainties via BLS, an intelligent BLS-FONTSM formation control approach is proposed to carry out formation control in presence of uncertainties. Numerous computer simulations are conducted to show the effectiveness and superiority of the proposed BLS-FDDEIF and BLS-FONTSM methods. Experimental results are carried out to demonstrate the applicability and practicability of the two proposed approaches on the laboratory-built multirobot system.
摘 要 i
Abstract ii
Contents iii
List of Figures vii
List of Tables xi
List of Nomenclature xiii
List of Acronyms xv
Chapter 1 Introduction 1
1.1 Research Background 1
1.2 Literature Review 4
1.2.1 Related Work on Cooperation Localization 4
1.2.2 Related Work on Consensus Formation Control 5
1.2.3 Related Work on Neural Networks and BLS for Dynamic Effects and Uncertainties 5
1.2.4 Related Work on Fractional Order NTSM (FONTSM) Control 6
1.3 Motivation and Objectives 7
1.4 Main Contributions and Novelty 8
1.5 Thesis Organization 9
Chapter 2 System Design and Implementation 10
2.1 Introduction 10
2.2 System Structure 11
2.3 Description of the Experimental HOMRs 12
2.3.1 Basic Structure 12
2.3.2 ARM-based Microcontroller 14
2.3.3 Single-Board Computer 16
2.3.4 Dynamixel Motors 17
2.3.5 RGB-D Sensor 18
2.3.6 Laser Scanner 20
2.4 Encoders and Odometry 22
2.4.1 Odometry Method of the COMR 22
2.4.2 Odometry Method of the TOMR 23
2.4.3 Odometry Method of the FOMR 23
2.5 Modeling HOMRs in a Unified Framework 24
2.5.1 Dynamic Model of the COMR 24
2.5.2 Dynamic Model of the TOMR 25
2.5.3 Dynamic Model of the FOMR 26
2.5.4 Unified Dynamic Model of the HOMRs 28
2.6 Broad Learning System (BLS) 28
2.6.1 Introduction to BLS 28
2.6.2 Vector Uncertain Function Approximation by BLS 29
2.7 Cooperative Localization System and Formation Control Architecture 30
2.8 Concluding Remarks 33
Chapter 3 Cooperative Localization Using Fuzzy DDEIF and Broad Learning System for Uncertain HOMRs Incorporated Dynamic Effects 34
3.1 Introduction 34
3.2 RGB-D Sensor Model 35
3.3 Laser Scanner 36
3.4 BLS Identifier for Dynamic Model of Each HOMR with Dynamic Effect and Uncertainties 38
3.4.1 Models of Multi-HOMRs with Dynamic Effects 38
3.4.2 BLS Approximation 39
3.5 Fuzzy Distributed and Decentralized EIF Algorithm for Mobile Multirobots 45
3.5.1 Distributed and Decentralized Extended Information Filtering Algorithm Using a Modified Graph Theory 45
3.5.2 Fuzzy DDEIF (FDDEIF) 47
3.5.3 Fuzzy Tuner 48
3.6 Cooperative Pose Initialization 49
3.6.1 Map-Based Pose Initialization 49
3.6.2 Cooperative Pose Initialization 50
3.7 BLS-FDDEIF for Dynamic Localization 51
3.8 Simulation Results and Discussion 53
3.9 Experimental Results and Discussion 59
3.10 Concluding Remarks 70
Chapter 4 Intelligent Fractional Order Nonsingular Terminal Sliding-Mode Formation Control Using BLS 71
4.1 Introduction 71
4.2 Graph Theory 72
4.3 Problem Statement 73
4.4 Preliminary of Fractional Order Calculus 74
4.4.1 Fractional Order Calculus Definition 74
4.4.2 Grunwald-Letnikov Definition 75
4.5 Intelligent BLS-FONTSM Formation Control 76
4.6 Simulation Results and Discussion 81
4.7 Experimental Results and Discussion 90
4.8 Concluding Remarks 93
Chapter 5 Conclusions and Future Work 95
5.1 Conclusions 95
5.2 Future Work 96
References 98
[1] 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.
[2] M. Robers, “Tracking the evolution of autonomous and unmanned aircraft,” 2018. [Online]. Available: https://www.createdigital.org.au/evolution-autonomous-unmanned-aircraft/ [Accessed: 20-September - 2018].
[3] E. Howell, “What is a satellite?” 2017. [Online]. Available: https://www.space.com/24839-satellites.html [Accessed:26-October-2017].
[4] K. Landry, “Keeping humans in the loop is necessary for service robots to function effectively”, 2016. [Online]. Available: https://aitrends.com/robotics/keeping-humans-loop-necessary-service-robots-function-effectively/ [Accessed: 10 - Dec - 2018].
[5] Multiple Robot Systems - Formation Control. Retrieved Dec. 18, 2018 from http://www.ra.cs.unituebingen.de/forschung/terminiert/formation/welcome_e.html
[6] Wikipedia, “Mecanum wheel,” 2011. [Online]. Available: https://en.wikipedia.org/wiki/Mecanum_wheel#cite_note-1 [Accessed: 10-Dec – 2018].
[7] Wikipedia, “Omni wheel,” 2011. [Online]. Available: https://en.wikipedia.org/wiki/Omni_wheel [Accessed: 10-Dec - 2011].
[8] G. Indiveri, “Swedish wheeled omnidirectional mobile robots: kinematics analysis and control,” IEEE Transactions on Robotics, vol. 25, no. 1, pp. 164-171, 2009.
[9] X. F. Wang, C. C. Tsai, and F. C. Tai, “Intelligent adaptive backstepping distributed consensus formation control for uncertain networked four Swedish-wheeled omnidirectional multi- robots,” in Proc. of 2016 Intern. Conf. on Advanced Robotics and Intelligent Systems (ARIS 2016), Taipei Nangang Exhibition Center, Taipei, Taiwan, August 31-Sptember 2, 2016.
[10] H. Kwon, Y. Yoon, J. B. Park, A.C. Kak, “Person tracking with a mobile robot using two uncalibrated independently moving cameras,” IEEE International Conference on Robotics and Automation, Barcelona, Spain, Spain, April 2005.
[11] Y. M. Tan, and C. C. Tsai, Fuzzy decentralized cooperative global localization for a multi-ballbot system, MS Thesis, Department of Electrical Engineering, National Chung Hsing University, June 2016.
[12] Y. Wang and C. W. de Silva, “Sequential Q -learning with Kalman filtering for multirobot cooperative transportation,” IEEE/ASME Trans. on Mechatronics, vol. 15, no.2, pp. 261-268, 2010.
[13] C. C. Tsai, C. C. Chan, F. C. Tai, and K. H. Yang, “Fuzzy distributed and decentralized extended information filtering for an omnidirectional mobile multi-robot system,” in Proc. of 2016 Intern. Conf. on Advanced Robotics and Intelligent Systems, Taipei Nangang Exhibition Center, Taipei, Taiwan, Aug. 31-Sep. 2, 2016.
[14] W. Ren., “Information consensus in multivehicle cooperative Control,” IEEE Contr. Syst. magazine, vol. 27, no. 2, pp. 71-82, 2007.
[15] C. L. P. Chen, C. E. Zen, and T. Du, “Fuzzy observed-based adaptive consensus tracking control for second-order multi-agent systems with heterogeneous nonlinear dynamics,” IEEE Transactions on Fuzzy Systems, vol. 24, no. 4, pp. 906-915. doi: 10.1109/TFUZZ.2015.2486817, Oct. 2015.
[16] C. C. Tsai, H. L. Wu, and F. C. Tai, “Intelligent sliding-mode formation control for uncertain networked heterogeneous Mecanum-wheeled omnidirectional platforms,” in Proc. of 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Budapest, Hungary, 9-12 October, 2016.
[17] T. Y. Lin, Intelligent formation control of uncertain networked heterogeneous omnidirectional wheel multi-robots, MS Thesis, Department of Electrical Engineering, National Chung Hsing University, July 2017.
[18] H. L. Wu, Intelligent sliding-mode leader-following consensus tracking control of uncertain second-order nonlinear multi-agent systems, Doctoral Dissertation, Department of Electrical Engineering, National Chung Hsing University, July 2018.
[19] C. L. P. Chen and Z. Liu, “Broad learning system: An effective and efficient incremental learning system without the need for deep architecture,” IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 1, pp. 10–24, Jan. 2018. doi:10.1109/TNNLS.2017.2716952.
[20] C. L. P. Chen, Z. L. Liu, and S. Feng, “A new paradigmatic broad learning system: Structural variations and universal approximation capability,” To appear in IEEE Transactions on Neural Networks and Learning Systems, 2018.
[21] D. Zhu, L. Liu, and C. Liu, “Optimal fractional-order PID control of chaos in the fractional-order BUCK converter,” in Proc. 2014 9th IEEE Conference on Industrial Electronics and Applications, Hangzhou,, China, June 9-11, 2014. doi: 10.1109/ICIEA.2014.6931269.
[22] W. A. Shutnan and T. Y. Abdalla, “Artificial immune system based optimal fractional order PID control scheme for path tracking of robot manipulator,” in Proc. 2018 IEEE International Conference on Advance of Sustainable Engineering and its Application (ICASEA), Wasit, Iraq, March 14-15, 2018. doi: 10.1109/ICASEA.2018.8370949.
[23] S. Y. Chen and C. Y. Lee, “Digital signal processor based intelligent fractional-order sliding-mode control for a linear voice coil actuator,” IET Control Theory & Applications, vol. 11, no. 8, pp. 1282-1292, 2017. doi: 10.1049/iet-cta.2016.1127.
[24] Z. Tianyi, R. Xuemei, and Z. Yao, “A fractional order sliding mode controller design for spacecraft attitude control system,” in Proc. 2015 IEEE International Conference on Chinese Control Conference (CCC), Hangzhou, China, September 28-30, 2015. doi:10.1109/ChiCC.2015.7260160.
[25] I. N’Doye and T. M. Laleg-Kirati, “Fractional-order adaptive fault estimation for a class of nonlinear fractional-order systems,” in Proc. of 2015 IEEE International Conference on American Control Conference (ACC), Chicago, IL, USA, July 30, 2015. doi: 10.1109/ACC.2015.7171923.
[26] C. Ren and C. L. P. Chen, “Sliding mode leader-following consensus controllers for second-order non-linear multi-agent systems,” IET Control Theory & Applications, vol. 9, no. 10, pp. 1544-1552, 2015. doi:10.1049/iet-cta.2014.0523.
[27] Y. Y. Wang, L. Gu, Y. Xu, and X. Cao, “Practical tracking control of robot manipulators with continuous fractional-order nonsingular terminal sliding mode,” IEEE Transactions on Industrial Electronics, vol. 63, no. 10, pp. 6194-6204, 2016. doi:10.1109/TIE.2016.2569454.
[28] Arm-MBED NUCLEO-F446RE. Retrieved Dec.15, 2018, from https://os.mbed.com/platforms/ST-Nucleo-F446RE/
[29] LATTEPANDA. Retrieved Dec.15, 2018, from https://www.lattepanda.com/products/3.html
[30] C. C. Tsai, H. L. Wu, F. C. Tai, and Y. S. Chen, “Distributed consensus formation control with collision and obstacle avoidance for uncertain networked omnidirectional multirobot systems using fuzzy wavelet neural networks,” International Journal of Fuzzy Systems, vol. 19, no. 5, pp. 1375-1391, 2017. doi:10.1007/s40815-016-0239-0.
[31] X. F. Wang, C. C. Tsai, and F. C. Tai, “Intelligent adaptive backstepping distributed consensus formation control for uncertain networked four Swedish-wheeled omnidirectional multi-robots,” in Proc. 2016 International conference on Advanced Robotics and Intelligent Systems (ARIS 2016), Taipei, Taiwan, Aug. 31 - Sept. 2, 2016.
[32] C. C. Tsai, C. C. Chan, F. C. Tai, and K. H. Yang, “Fuzzy distributed and decentralized extended information filtering for an omnidirectional mobile multi-robot system,” in Proc. 2016 International conference on Advanced Robotics and Intelligent Systems (ARIS 2016), Taipei, Taiwan, Aug. 31 - Sept. 2, 2016.
[33] C. C. Tsai, C. C. Chan, and F. C, Tai, “Cooperative localization using fuzzy decentralized extended information filtering for homogenous omnidirectional mobile multi-robot system,” in Proc. 2015 International Conference on System Science and Engineering, Morioka, Japan, July 6-8, 2015.
[34] H. K. Khalil, Nonlinear systems, 3rd ed., Prentice Hall, 2013.
[35] C. W. Wu and C. C. Tsai, “Intelligent nonsingular terminal sliding-mode formation control using broad learning system for uncertain networking heterogeneous omnidirectional mobile multirobots,” in Proc. 2018 International Conference on Advanced Robotics and Intelligent Systems (ARIS 2018), Taipei, Taiwan, August 28-31, 2018.
[36] H. L Wu, C. C. Tsai, and F. C. Tai, “Adaptive nonsingular terminal sliding-mode formation control using ORFWNN for uncertain networked heterogeneous Mecanum-wheeled omnidirectional robot,” in Proc. 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2017), Banff, Canada, October 5-8, 2017. doi:10.1109/SMC.2017.8123141.
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