Stochastic Robust Team Formation Tracking Design of Multi-VTOL-UAV Networked Control System in Smart City under Time-varying Delay and Random Fluctuation
In this study, the robust H∞ event-triggered team formation tracking control design of multi-VTOL-UAVs in networked system is investigated. To describe the realistic networked system and UAV model, the intrinsic continuous Wiener random fluctuation, discontinuous Poisson random fluctuations, externa...
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description | In this study, the robust H∞ event-triggered team formation tracking control design of multi-VTOL-UAVs in networked system is investigated. To describe the realistic networked system and UAV model, the intrinsic continuous Wiener random fluctuation, discontinuous Poisson random fluctuations, external disturbances and time-varying delays of wireless network are formulated in the proposed nonlinear stochastic jump diffusion system structure. By combining the event-triggered multi-UAV dynamic models and reference model into an augmented system, the robust H∞ event-triggered multi-UAV networked team tracking problem can be transformed to a Hamilton-Jacobi inequality(HJI)-constraint optimization problem. Due to the difficulties in solving HJI-constraint optimization problem, for practical application, the T-S fuzzy techniques are adopted to efficiently approximate the nonlinear multi-UAVs system by a set of local linearized networked systems. Thus, the HJI-constraint optimization problem for the H∞ event-triggered robust formation team tracking control can be transformed to a linear matrix inequality(LMI)-constraint optimization problem and can be easily solved by the convex optimization techniques. Finally, a simulation example is given to validate the effectiveness of the proposed event-triggered robust H∞ team formation tracking control for the multi-VTOL-UAV system. |
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To describe the realistic networked system and UAV model, the intrinsic continuous Wiener random fluctuation, discontinuous Poisson random fluctuations, external disturbances and time-varying delays of wireless network are formulated in the proposed nonlinear stochastic jump diffusion system structure. By combining the event-triggered multi-UAV dynamic models and reference model into an augmented system, the robust H∞ event-triggered multi-UAV networked team tracking problem can be transformed to a Hamilton-Jacobi inequality(HJI)-constraint optimization problem. Due to the difficulties in solving HJI-constraint optimization problem, for practical application, the T-S fuzzy techniques are adopted to efficiently approximate the nonlinear multi-UAVs system by a set of local linearized networked systems. Thus, the HJI-constraint optimization problem for the H∞ event-triggered robust formation team tracking control can be transformed to a linear matrix inequality(LMI)-constraint optimization problem and can be easily solved by the convex optimization techniques. Finally, a simulation example is given to validate the effectiveness of the proposed event-triggered robust H∞ team formation tracking control for the multi-VTOL-UAV system.</description><identifier>ISSN: 2169-3536</identifier><identifier>EISSN: 2169-3536</identifier><identifier>DOI: 10.1109/ACCESS.2020.3009751</identifier><identifier>CODEN: IAECCG</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Control design ; Control systems design ; Convexity ; Dynamic models ; event-triggered control ; H-infinity control ; Linear matrix inequalities ; Mathematical analysis ; Networked control systems ; Optimization ; Optimization techniques ; robust <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">H ∞ fuzzy control ; Robust control ; robust H∞ fuzzy control ; Robustness ; Smart cities ; stochastic control ; Stochastic processes ; Time varying control ; Tracking control ; Tracking problem ; Unmanned aerial vehicle networked system ; Unmanned aerial vehicles ; Vertical takeoff aircraft ; virtual structure formation control ; Wireless networks</subject><ispartof>IEEE access, 2020-01, Vol.8, p.1-1</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2020</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c408t-57af97dba3a405ec99d49ebbc747368fd3d45d728cae438d1a78d82d405c623d3</citedby><cites>FETCH-LOGICAL-c408t-57af97dba3a405ec99d49ebbc747368fd3d45d728cae438d1a78d82d405c623d3</cites><orcidid>0000-0002-0391-257X ; 0000-0003-0200-8674 ; 0000-0002-7448-8972 ; 0000-0003-1644-6106</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9142225$$EHTML$$P50$$Gieee$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,864,2102,27633,27924,27925,54933</link.rule.ids></links><search><creatorcontrib>Lee, Min-Yen</creatorcontrib><creatorcontrib>Chen, Bor-Sen</creatorcontrib><creatorcontrib>Chang, Yi</creatorcontrib><creatorcontrib>Hwang, Chih-Lyang</creatorcontrib><title>Stochastic Robust Team Formation Tracking Design of Multi-VTOL-UAV Networked Control System in Smart City under Time-varying Delay and Random Fluctuation</title><title>IEEE access</title><addtitle>Access</addtitle><description>In this study, the robust H∞ event-triggered team formation tracking control design of multi-VTOL-UAVs in networked system is investigated. To describe the realistic networked system and UAV model, the intrinsic continuous Wiener random fluctuation, discontinuous Poisson random fluctuations, external disturbances and time-varying delays of wireless network are formulated in the proposed nonlinear stochastic jump diffusion system structure. By combining the event-triggered multi-UAV dynamic models and reference model into an augmented system, the robust H∞ event-triggered multi-UAV networked team tracking problem can be transformed to a Hamilton-Jacobi inequality(HJI)-constraint optimization problem. Due to the difficulties in solving HJI-constraint optimization problem, for practical application, the T-S fuzzy techniques are adopted to efficiently approximate the nonlinear multi-UAVs system by a set of local linearized networked systems. Thus, the HJI-constraint optimization problem for the H∞ event-triggered robust formation team tracking control can be transformed to a linear matrix inequality(LMI)-constraint optimization problem and can be easily solved by the convex optimization techniques. Finally, a simulation example is given to validate the effectiveness of the proposed event-triggered robust H∞ team formation tracking control for the multi-VTOL-UAV system.</description><subject>Control design</subject><subject>Control systems design</subject><subject>Convexity</subject><subject>Dynamic models</subject><subject>event-triggered control</subject><subject>H-infinity control</subject><subject>Linear matrix inequalities</subject><subject>Mathematical analysis</subject><subject>Networked control systems</subject><subject>Optimization</subject><subject>Optimization techniques</subject><subject>robust <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">H ∞ fuzzy control</subject><subject>Robust control</subject><subject>robust H∞ fuzzy control</subject><subject>Robustness</subject><subject>Smart cities</subject><subject>stochastic control</subject><subject>Stochastic processes</subject><subject>Time varying control</subject><subject>Tracking control</subject><subject>Tracking problem</subject><subject>Unmanned aerial vehicle networked system</subject><subject>Unmanned aerial vehicles</subject><subject>Vertical takeoff aircraft</subject><subject>virtual structure formation control</subject><subject>Wireless networks</subject><issn>2169-3536</issn><issn>2169-3536</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>RIE</sourceid><sourceid>DOA</sourceid><recordid>eNpNkcFu3CAURa2qlRol-YJskLr2FAM2ZjlykzbSpJFiJ1uE4XnKxDYp4FTzKf3bkjiKygLQ033nPrhZdlHgTVFg8XXbNJdtuyGY4A3FWPCy-JCdkKISOS1p9fG_--fsPIQDTqtOpZKfZH_b6PQvFaLV6M71S4ioAzWhK-cnFa2bUeeVfrTzHn2DYPczcgO6WcZo84fudpffbx_QT4h_nH8Egxo3R-9G1B5DhAnZGbWT8hE1Nh7RMhvwqLMT5M_KH1fkqI5IzQbdpc0l23HRcXk1Pss-DWoMcP52nmb3V5dd8yPf3X6_bra7XDNcx7zkahDc9IoqhkvQQhgmoO81Z5xW9WCoYaXhpNYKGK1NoXhtamKSWFeEGnqaXa9c49RBPnmbJj5Kp6x8LTi_l-kJVo8gGYOeAuE95nWiM9FXlCYgpcyQ9KeJ9WVlPXn3e4EQ5cEtfk7jS8JKVjFSC5JUdFVp70LwMLy7Fli-RCrXSOVLpPIt0tR1sXZZAHjvEAUjhJT0H_RCnU0</recordid><startdate>20200101</startdate><enddate>20200101</enddate><creator>Lee, Min-Yen</creator><creator>Chen, Bor-Sen</creator><creator>Chang, Yi</creator><creator>Hwang, Chih-Lyang</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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To describe the realistic networked system and UAV model, the intrinsic continuous Wiener random fluctuation, discontinuous Poisson random fluctuations, external disturbances and time-varying delays of wireless network are formulated in the proposed nonlinear stochastic jump diffusion system structure. By combining the event-triggered multi-UAV dynamic models and reference model into an augmented system, the robust H∞ event-triggered multi-UAV networked team tracking problem can be transformed to a Hamilton-Jacobi inequality(HJI)-constraint optimization problem. Due to the difficulties in solving HJI-constraint optimization problem, for practical application, the T-S fuzzy techniques are adopted to efficiently approximate the nonlinear multi-UAVs system by a set of local linearized networked systems. Thus, the HJI-constraint optimization problem for the H∞ event-triggered robust formation team tracking control can be transformed to a linear matrix inequality(LMI)-constraint optimization problem and can be easily solved by the convex optimization techniques. Finally, a simulation example is given to validate the effectiveness of the proposed event-triggered robust H∞ team formation tracking control for the multi-VTOL-UAV system.</abstract><cop>Piscataway</cop><pub>IEEE</pub><doi>10.1109/ACCESS.2020.3009751</doi><tpages>1</tpages><orcidid>https://orcid.org/0000-0002-0391-257X</orcidid><orcidid>https://orcid.org/0000-0003-0200-8674</orcidid><orcidid>https://orcid.org/0000-0002-7448-8972</orcidid><orcidid>https://orcid.org/0000-0003-1644-6106</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Control design Control systems design Convexity Dynamic models event-triggered control H-infinity control Linear matrix inequalities Mathematical analysis Networked control systems Optimization Optimization techniques robust <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">H ∞ fuzzy control Robust control robust H∞ fuzzy control Robustness Smart cities stochastic control Stochastic processes Time varying control Tracking control Tracking problem Unmanned aerial vehicle networked system Unmanned aerial vehicles Vertical takeoff aircraft virtual structure formation control Wireless networks |
title | Stochastic Robust Team Formation Tracking Design of Multi-VTOL-UAV Networked Control System in Smart City under Time-varying Delay and Random Fluctuation |
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