Adaptive Discrete-Time Flight Control Using Disturbance Observer and Neural Networks
This paper studies the adaptive neural control (ANC)-based tracking problem for discrete-time nonlinear dynamics of an unmanned aerial vehicle subject to system uncertainties, bounded time-varying disturbances, and input saturation by using a discrete-time disturbance observer (DTDO). Based on the a...
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| Veröffentlicht in: | IEEE transaction on neural networks and learning systems 2019-12, Vol.30 (12), p.3708-3721 |
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| container_title | IEEE transaction on neural networks and learning systems |
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| creator | Shao, Shuyi Chen, Mou Zhang, Youmin |
| description | This paper studies the adaptive neural control (ANC)-based tracking problem for discrete-time nonlinear dynamics of an unmanned aerial vehicle subject to system uncertainties, bounded time-varying disturbances, and input saturation by using a discrete-time disturbance observer (DTDO). Based on the approximation approach of neural network, system uncertainties are tackled approximately. To restrain the negative effects of bounded disturbances, a nonlinear DTDO is designed. Then, a backstepping technique-based ANC strategy is proposed by utilizing a constructed auxiliary system and a discrete-time tracking differentiator. The boundness of all signals is proven in the closed-loop system under the discrete-time Lyapunov analysis. Finally, the feasibility of the proposed ANC technique is further specified based on numerical simulation results. |
| doi_str_mv | 10.1109/TNNLS.2019.2893643 |
| format | Article |
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Based on the approximation approach of neural network, system uncertainties are tackled approximately. To restrain the negative effects of bounded disturbances, a nonlinear DTDO is designed. Then, a backstepping technique-based ANC strategy is proposed by utilizing a constructed auxiliary system and a discrete-time tracking differentiator. The boundness of all signals is proven in the closed-loop system under the discrete-time Lyapunov analysis. 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(IEEE) 2019</rights><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c417t-f2f22458a5119bd415c14826a4cf286e586fa89a24eb7fa8744ec7692ed901e83</citedby><cites>FETCH-LOGICAL-c417t-f2f22458a5119bd415c14826a4cf286e586fa89a24eb7fa8744ec7692ed901e83</cites><orcidid>0000-0001-7158-8575 ; 0000-0002-9731-5943 ; 0000-0001-9458-661X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/8637961$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,27901,27902,54733</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/8637961$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/30763247$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Shao, Shuyi</creatorcontrib><creatorcontrib>Chen, Mou</creatorcontrib><creatorcontrib>Zhang, Youmin</creatorcontrib><title>Adaptive Discrete-Time Flight Control Using Disturbance Observer and Neural Networks</title><title>IEEE transaction on neural networks and learning systems</title><addtitle>TNNLS</addtitle><addtitle>IEEE Trans Neural Netw Learn Syst</addtitle><description>This paper studies the adaptive neural control (ANC)-based tracking problem for discrete-time nonlinear dynamics of an unmanned aerial vehicle subject to system uncertainties, bounded time-varying disturbances, and input saturation by using a discrete-time disturbance observer (DTDO). Based on the approximation approach of neural network, system uncertainties are tackled approximately. To restrain the negative effects of bounded disturbances, a nonlinear DTDO is designed. Then, a backstepping technique-based ANC strategy is proposed by utilizing a constructed auxiliary system and a discrete-time tracking differentiator. The boundness of all signals is proven in the closed-loop system under the discrete-time Lyapunov analysis. Finally, the feasibility of the proposed ANC technique is further specified based on numerical simulation results.</description><subject>Adaptive control</subject><subject>Aerodynamics</subject><subject>Artificial neural networks</subject><subject>Backstepping</subject><subject>Backstepping control</subject><subject>Computer simulation</subject><subject>Discrete time systems</subject><subject>Disturbance</subject><subject>disturbance observer (DO)</subject><subject>Disturbance observers</subject><subject>Dynamical systems</subject><subject>Feedback control</subject><subject>Flight control</subject><subject>Flight control systems</subject><subject>Mathematical models</subject><subject>MIMO communication</subject><subject>neural network (NN)</subject><subject>Neural networks</subject><subject>nonlinear discrete-time systems (NDTSs)</subject><subject>Nonlinear dynamics</subject><subject>Nonlinear systems</subject><subject>Tracking control</subject><subject>Tracking problem</subject><subject>Uncertainty</subject><subject>unmanned aerial vehicle (UAV)</subject><subject>Unmanned aerial vehicles</subject><issn>2162-237X</issn><issn>2162-2388</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpdkEtPwzAMgCMEYgj4AyChSly4dDROmsdxGk9pGgeGxC1KW3d0dO1IWhD_noyNHfDFlvzZsj9CzmgypDTR17PpdPI8hITqISjNBGd75AiogBiYUvu7Wr4OyKn3iySESFLB9SEZsEQKBlwekdmosKuu-sTopvK5ww7jWbXE6K6u5m9dNG6bzrV19OKrZr5Gut5ltskxeso8uk90kW2KaIq9s3VI3Vfr3v0JOSht7fF0m4_Jy93tbPwQT57uH8ejSZxzKru4hBKAp8qmlOqs4DTNKVcgLM9LUAJTJUqrtAWOmQyV5BxzKTRgoROKih2Tq83elWs_evSdWYYnsK5tg23vDQDo4IoxGtDLf-ii7V0TrjPAIKWBAxko2FC5a713WJqVq5bWfRuamLV286vdrLWbrfYwdLFd3WdLLHYjf5IDcL4BKkTctZVgUgvKfgAS9oU-</recordid><startdate>20191201</startdate><enddate>20191201</enddate><creator>Shao, Shuyi</creator><creator>Chen, Mou</creator><creator>Zhang, Youmin</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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Academic</collection><jtitle>IEEE transaction on neural networks and learning systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Shao, Shuyi</au><au>Chen, Mou</au><au>Zhang, Youmin</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Adaptive Discrete-Time Flight Control Using Disturbance Observer and Neural Networks</atitle><jtitle>IEEE transaction on neural networks and learning systems</jtitle><stitle>TNNLS</stitle><addtitle>IEEE Trans Neural Netw Learn Syst</addtitle><date>2019-12-01</date><risdate>2019</risdate><volume>30</volume><issue>12</issue><spage>3708</spage><epage>3721</epage><pages>3708-3721</pages><issn>2162-237X</issn><eissn>2162-2388</eissn><coden>ITNNAL</coden><abstract>This paper studies the adaptive neural control (ANC)-based tracking problem for discrete-time nonlinear dynamics of an unmanned aerial vehicle subject to system uncertainties, bounded time-varying disturbances, and input saturation by using a discrete-time disturbance observer (DTDO). Based on the approximation approach of neural network, system uncertainties are tackled approximately. To restrain the negative effects of bounded disturbances, a nonlinear DTDO is designed. Then, a backstepping technique-based ANC strategy is proposed by utilizing a constructed auxiliary system and a discrete-time tracking differentiator. The boundness of all signals is proven in the closed-loop system under the discrete-time Lyapunov analysis. Finally, the feasibility of the proposed ANC technique is further specified based on numerical simulation results.</abstract><cop>United States</cop><pub>IEEE</pub><pmid>30763247</pmid><doi>10.1109/TNNLS.2019.2893643</doi><tpages>14</tpages><orcidid>https://orcid.org/0000-0001-7158-8575</orcidid><orcidid>https://orcid.org/0000-0002-9731-5943</orcidid><orcidid>https://orcid.org/0000-0001-9458-661X</orcidid></addata></record> |
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| subjects | Adaptive control Aerodynamics Artificial neural networks Backstepping Backstepping control Computer simulation Discrete time systems Disturbance disturbance observer (DO) Disturbance observers Dynamical systems Feedback control Flight control Flight control systems Mathematical models MIMO communication neural network (NN) Neural networks nonlinear discrete-time systems (NDTSs) Nonlinear dynamics Nonlinear systems Tracking control Tracking problem Uncertainty unmanned aerial vehicle (UAV) Unmanned aerial vehicles |
| title | Adaptive Discrete-Time Flight Control Using Disturbance Observer and Neural Networks |
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