Event-triggered robust neural control for unmanned sail-assisted vehicles subject to actuator failures
This note focus on the waypoints-based path-following control for the unmanned sail-assisted vehicles (USAV), aiming to release the constraints of the actuator failures and gain uncertainties. The proposed scheme is formulated as two components, i.e., the composite guidance part and the control part...
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| Vydáno v: | Ocean engineering Ročník 216; s. 107754 |
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| Hlavní autoři: | , , , |
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| Jazyk: | angličtina |
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Elsevier Ltd
15.11.2020
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| ISSN: | 0029-8018, 1873-5258 |
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| Abstract | This note focus on the waypoints-based path-following control for the unmanned sail-assisted vehicles (USAV), aiming to release the constraints of the actuator failures and gain uncertainties. The proposed scheme is formulated as two components, i.e., the composite guidance part and the control part. By utilizing the sign self-selection algorithm, the composite Logic Virtual Ship (LVS) guidance law is developed in the scheme to program the real-time heading angle for the USAV. The main superiorities of this design are to ensure the USAV navigating efficiently and choose the corresponding sailing mode: upwind mode, downwind mode or crosswind mode. Furthermore, to improve the effectiveness of the closed-loop control system, an event-triggered robust neural control algorithm is targetly designed for the rudder actuator and the sail actuator by fusing the robust neural damping technique and the input event-triggered mechanism. In this algorithm, the unknown terms of the system are tackled requiring no information of the system model and the external disturbances. The transmission burden from the controller to the actuator is reduced. And the unknown actuator failures and the gain uncertainties are compensated through four adaptive updated parameters. Based on the Lyapunov analysis, sufficient effort has been made to guarantee that all the signals of the closed-loop control system are the semi-global uniform ultimate bounded (SGUUB). Finally, the simulated results demonstrate the validity of the proposed control strategy.
•An event-triggered robust neural algorithm is proposed for the unmanned sail-assisted vehicles.•The composited LVS guidance law could generate a real-time reference for the upwind, crosswind and downwind conditions.•The energy consumption and transmission burden of the actuators are largely reduced by the input event-triggered mechanism.•The form of the control law becomes concise by employing robust neural damping techniques. |
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| AbstractList | This note focus on the waypoints-based path-following control for the unmanned sail-assisted vehicles (USAV), aiming to release the constraints of the actuator failures and gain uncertainties. The proposed scheme is formulated as two components, i.e., the composite guidance part and the control part. By utilizing the sign self-selection algorithm, the composite Logic Virtual Ship (LVS) guidance law is developed in the scheme to program the real-time heading angle for the USAV. The main superiorities of this design are to ensure the USAV navigating efficiently and choose the corresponding sailing mode: upwind mode, downwind mode or crosswind mode. Furthermore, to improve the effectiveness of the closed-loop control system, an event-triggered robust neural control algorithm is targetly designed for the rudder actuator and the sail actuator by fusing the robust neural damping technique and the input event-triggered mechanism. In this algorithm, the unknown terms of the system are tackled requiring no information of the system model and the external disturbances. The transmission burden from the controller to the actuator is reduced. And the unknown actuator failures and the gain uncertainties are compensated through four adaptive updated parameters. Based on the Lyapunov analysis, sufficient effort has been made to guarantee that all the signals of the closed-loop control system are the semi-global uniform ultimate bounded (SGUUB). Finally, the simulated results demonstrate the validity of the proposed control strategy.
•An event-triggered robust neural algorithm is proposed for the unmanned sail-assisted vehicles.•The composited LVS guidance law could generate a real-time reference for the upwind, crosswind and downwind conditions.•The energy consumption and transmission burden of the actuators are largely reduced by the input event-triggered mechanism.•The form of the control law becomes concise by employing robust neural damping techniques. |
| ArticleNumber | 107754 |
| Author | Li, Jiqiang Zhang, Weidong Zhang, Guoqing Yu, Wei |
| Author_xml | – sequence: 1 givenname: Guoqing surname: Zhang fullname: Zhang, Guoqing email: zhanggq87@dlmu.edu.cn organization: Navigation College, Dalian Maritime University, Dalian, 116026, Liaoning, People's Republic of China – sequence: 2 givenname: Jiqiang surname: Li fullname: Li, Jiqiang email: ljqdlmu@dlmu.edu.cn organization: Navigation College, Dalian Maritime University, Dalian, 116026, Liaoning, People's Republic of China – sequence: 3 givenname: Wei surname: Yu fullname: Yu, Wei organization: Navigation College, Dalian Maritime University, Dalian, 116026, Liaoning, People's Republic of China – sequence: 4 givenname: Weidong surname: Zhang fullname: Zhang, Weidong email: wdzhang@sjtu.edu.cn organization: Navigation College, Dalian Maritime University, Dalian, 116026, Liaoning, People's Republic of China |
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| Cites_doi | 10.1109/JOE.2017.2777638 10.1109/TAC.2013.2251795 10.1109/TCST.2006.883211 10.1016/j.oceaneng.2019.01.043 10.1109/TCYB.2017.2655053 10.1109/TITS.2015.2483022 10.1109/TNNLS.2017.2678922 10.1109/TIE.2018.2793207 10.1016/j.cor.2016.09.017 10.1016/j.automatica.2017.07.061 10.1017/S0373463319000353 10.1017/S0373463310000263 10.1109/TNNLS.2017.2650238 10.1016/j.ifacol.2016.10.490 10.1016/j.oceaneng.2010.04.007 10.1002/rnc.4473 10.1109/JOE.2013.2247276 10.1016/S0888-613X(96)00132-6 10.1109/JOE.2018.2809018 10.1016/j.robot.2012.08.004 10.1007/s11432-010-0031-y 10.1007/s11432-013-5043-y 10.1016/j.automatica.2016.01.064 |
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| Keywords | Event-triggered inputs LVS guidance Path-following control Adaptive neural control Sail-assisted vehicles |
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| SubjectTerms | Adaptive neural control Event-triggered inputs LVS guidance Path-following control Sail-assisted vehicles |
| Title | Event-triggered robust neural control for unmanned sail-assisted vehicles subject to actuator failures |
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