Robust Formation Control for Cooperative Underactuated Quadrotors via Reinforcement Learning
In this article, the model-free robust formation control problem is addressed for cooperative underactuated quadrotors involving unknown nonlinear dynamics and disturbances. Based on the hierarchical control scheme and the reinforcement learning theory, a robust controller is proposed without knowle...
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| Veröffentlicht in: | IEEE transaction on neural networks and learning systems 2021-10, Vol.32 (10), p.4577-4587 |
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| container_title | IEEE transaction on neural networks and learning systems |
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| creator | Zhao, Wanbing Liu, Hao Lewis, Frank L. |
| description | In this article, the model-free robust formation control problem is addressed for cooperative underactuated quadrotors involving unknown nonlinear dynamics and disturbances. Based on the hierarchical control scheme and the reinforcement learning theory, a robust controller is proposed without knowledge of each quadrotor dynamics, consisting of a distributed observer to estimate the position state of the leader, a position controller to achieve the desired formation, and an attitude controller to control the rotational motion. Simulation results on the multiquadrotor system confirm the effectiveness of the proposed model-free robust formation control method. |
| doi_str_mv | 10.1109/TNNLS.2020.3023711 |
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Based on the hierarchical control scheme and the reinforcement learning theory, a robust controller is proposed without knowledge of each quadrotor dynamics, consisting of a distributed observer to estimate the position state of the leader, a position controller to achieve the desired formation, and an attitude controller to control the rotational motion. Simulation results on the multiquadrotor system confirm the effectiveness of the proposed model-free robust formation control method.</description><identifier>ISSN: 2162-237X</identifier><identifier>EISSN: 2162-2388</identifier><identifier>DOI: 10.1109/TNNLS.2020.3023711</identifier><identifier>PMID: 32970600</identifier><identifier>CODEN: ITNNAL</identifier><language>eng</language><publisher>PISCATAWAY: IEEE</publisher><subject>Attitude control ; Computer Science ; Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Control methods ; Control systems ; Cooperative control ; Dynamical systems ; Engineering ; Engineering, Electrical & Electronic ; Formation control ; Heuristic algorithms ; Learning (artificial intelligence) ; Learning theory ; Nonlinear dynamical systems ; Nonlinear dynamics ; Nonlinear systems ; quadrotor system ; Reinforcement ; reinforcement learning (RL) ; Robust control ; Robustness ; Rotary wing aircraft ; Science & Technology ; Technology ; underactuated system ; Vehicle dynamics</subject><ispartof>IEEE transaction on neural networks and learning systems, 2021-10, Vol.32 (10), p.4577-4587</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2021</rights><woscitedreferencessubscribed>true</woscitedreferencessubscribed><woscitedreferencescount>64</woscitedreferencescount><woscitedreferencesoriginalsourcerecordid>wos000704111000027</woscitedreferencesoriginalsourcerecordid><citedby>FETCH-LOGICAL-c328t-8b75805c58caea65b86f097f74cb6bf58274e58affa969b1b8b5be8f4be544243</citedby><cites>FETCH-LOGICAL-c328t-8b75805c58caea65b86f097f74cb6bf58274e58affa969b1b8b5be8f4be544243</cites><orcidid>0000-0003-4074-1615 ; 0000-0002-0564-9736 ; 0000-0001-8365-8008</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9205206$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>315,781,785,797,27929,27930,39263,54763</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/9205206$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Zhao, Wanbing</creatorcontrib><creatorcontrib>Liu, Hao</creatorcontrib><creatorcontrib>Lewis, Frank L.</creatorcontrib><title>Robust Formation Control for Cooperative Underactuated Quadrotors via Reinforcement Learning</title><title>IEEE transaction on neural networks and learning systems</title><addtitle>TNNLS</addtitle><addtitle>IEEE T NEUR NET LEAR</addtitle><description>In this article, the model-free robust formation control problem is addressed for cooperative underactuated quadrotors involving unknown nonlinear dynamics and disturbances. Based on the hierarchical control scheme and the reinforcement learning theory, a robust controller is proposed without knowledge of each quadrotor dynamics, consisting of a distributed observer to estimate the position state of the leader, a position controller to achieve the desired formation, and an attitude controller to control the rotational motion. 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| subjects | Attitude control Computer Science Computer Science, Artificial Intelligence Computer Science, Hardware & Architecture Computer Science, Theory & Methods Control methods Control systems Cooperative control Dynamical systems Engineering Engineering, Electrical & Electronic Formation control Heuristic algorithms Learning (artificial intelligence) Learning theory Nonlinear dynamical systems Nonlinear dynamics Nonlinear systems quadrotor system Reinforcement reinforcement learning (RL) Robust control Robustness Rotary wing aircraft Science & Technology Technology underactuated system Vehicle dynamics |
| title | Robust Formation Control for Cooperative Underactuated Quadrotors via Reinforcement Learning |
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