Event-triggered Trajectory Tracking Control of Marine Surface Vessels With Time-varying Output Constraints Using Barrier Functions
Abstract In this paper, we investigate the trajectory tracking control problem of marine surface vessels (MSVs) subject to time-varying output constraints, unknown external disturbances, and model uncertainties. To this end, a novel log-type universal barrier Lyapunov function (BLF) with prescribed...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Dong, Sheng [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
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| Anmerkung: |
© ICROS, KIEE and Springer 2023 |
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| Übergeordnetes Werk: |
Enthalten in: International Journal of Control, Automation and Systems - Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers, 2009, 21(2023), 8 vom: 23. Juni, Seite 2708-2717 |
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| Übergeordnetes Werk: |
volume:21 ; year:2023 ; number:8 ; day:23 ; month:06 ; pages:2708-2717 |
| Links: |
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| DOI / URN: |
10.1007/s12555-022-0072-1 |
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| Katalog-ID: |
SPR052643034 |
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| 520 | |a Abstract In this paper, we investigate the trajectory tracking control problem of marine surface vessels (MSVs) subject to time-varying output constraints, unknown external disturbances, and model uncertainties. To this end, a novel log-type universal barrier Lyapunov function (BLF) with prescribed performance is proposed to ensure the predefined dynamic performance of output tracking errors and good tracking accuracy. Then, a neural network considering approximation errors is designed to handle complex uncertainties. Furthermore, a relative threshold event-triggered mechanism is introduced in the controller design to reduce the actuator execution rate. With the aid of the BLF and the event-triggered mechanism in combination with the adaptive neural network technique and backstepping design tool, a novel adaptive neural network event-triggered trajectory tracking control scheme is presented. A rigorous stability analysis verifies that all signals in the closed-loop system are semi-globally uniformly ultimately bounded, and the prescribed time-varying constraints on the tracking errors would not be violated. At the same time, the Zeno behavior can be avoided. The numerical comparative simulation experiments validate the effectiveness and superiority of the proposed trajectory tracking control scheme. | ||
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| 700 | 1 | |a Zhou, Lu |4 aut | |
| 700 | 1 | |a Yu, Haomiao |4 aut | |
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10.1007/s12555-022-0072-1 doi (DE-627)SPR052643034 (SPR)s12555-022-0072-1-e DE-627 ger DE-627 rakwb eng Dong, Sheng verfasserin aut Event-triggered Trajectory Tracking Control of Marine Surface Vessels With Time-varying Output Constraints Using Barrier Functions 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © ICROS, KIEE and Springer 2023 Abstract In this paper, we investigate the trajectory tracking control problem of marine surface vessels (MSVs) subject to time-varying output constraints, unknown external disturbances, and model uncertainties. To this end, a novel log-type universal barrier Lyapunov function (BLF) with prescribed performance is proposed to ensure the predefined dynamic performance of output tracking errors and good tracking accuracy. Then, a neural network considering approximation errors is designed to handle complex uncertainties. Furthermore, a relative threshold event-triggered mechanism is introduced in the controller design to reduce the actuator execution rate. With the aid of the BLF and the event-triggered mechanism in combination with the adaptive neural network technique and backstepping design tool, a novel adaptive neural network event-triggered trajectory tracking control scheme is presented. A rigorous stability analysis verifies that all signals in the closed-loop system are semi-globally uniformly ultimately bounded, and the prescribed time-varying constraints on the tracking errors would not be violated. At the same time, the Zeno behavior can be avoided. The numerical comparative simulation experiments validate the effectiveness and superiority of the proposed trajectory tracking control scheme. Event-triggered control (dpeaa)DE-He213 marine surface vessel (dpeaa)DE-He213 trajectory tracking (dpeaa)DE-He213 universal barrier Lyapunov function (dpeaa)DE-He213 Shen, Zhipeng (orcid)0000-0003-0210-2046 aut Zhou, Lu aut Yu, Haomiao aut Enthalten in International Journal of Control, Automation and Systems Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers, 2009 21(2023), 8 vom: 23. Juni, Seite 2708-2717 (DE-627)SPR026303256 nnns volume:21 year:2023 number:8 day:23 month:06 pages:2708-2717 https://dx.doi.org/10.1007/s12555-022-0072-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_21 GBV_ILN_24 GBV_ILN_72 GBV_ILN_181 GBV_ILN_496 GBV_ILN_2002 GBV_ILN_2003 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2060 GBV_ILN_2470 AR 21 2023 8 23 06 2708-2717 |
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10.1007/s12555-022-0072-1 doi (DE-627)SPR052643034 (SPR)s12555-022-0072-1-e DE-627 ger DE-627 rakwb eng Dong, Sheng verfasserin aut Event-triggered Trajectory Tracking Control of Marine Surface Vessels With Time-varying Output Constraints Using Barrier Functions 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © ICROS, KIEE and Springer 2023 Abstract In this paper, we investigate the trajectory tracking control problem of marine surface vessels (MSVs) subject to time-varying output constraints, unknown external disturbances, and model uncertainties. To this end, a novel log-type universal barrier Lyapunov function (BLF) with prescribed performance is proposed to ensure the predefined dynamic performance of output tracking errors and good tracking accuracy. Then, a neural network considering approximation errors is designed to handle complex uncertainties. Furthermore, a relative threshold event-triggered mechanism is introduced in the controller design to reduce the actuator execution rate. With the aid of the BLF and the event-triggered mechanism in combination with the adaptive neural network technique and backstepping design tool, a novel adaptive neural network event-triggered trajectory tracking control scheme is presented. A rigorous stability analysis verifies that all signals in the closed-loop system are semi-globally uniformly ultimately bounded, and the prescribed time-varying constraints on the tracking errors would not be violated. At the same time, the Zeno behavior can be avoided. The numerical comparative simulation experiments validate the effectiveness and superiority of the proposed trajectory tracking control scheme. Event-triggered control (dpeaa)DE-He213 marine surface vessel (dpeaa)DE-He213 trajectory tracking (dpeaa)DE-He213 universal barrier Lyapunov function (dpeaa)DE-He213 Shen, Zhipeng (orcid)0000-0003-0210-2046 aut Zhou, Lu aut Yu, Haomiao aut Enthalten in International Journal of Control, Automation and Systems Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers, 2009 21(2023), 8 vom: 23. Juni, Seite 2708-2717 (DE-627)SPR026303256 nnns volume:21 year:2023 number:8 day:23 month:06 pages:2708-2717 https://dx.doi.org/10.1007/s12555-022-0072-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_21 GBV_ILN_24 GBV_ILN_72 GBV_ILN_181 GBV_ILN_496 GBV_ILN_2002 GBV_ILN_2003 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2060 GBV_ILN_2470 AR 21 2023 8 23 06 2708-2717 |
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10.1007/s12555-022-0072-1 doi (DE-627)SPR052643034 (SPR)s12555-022-0072-1-e DE-627 ger DE-627 rakwb eng Dong, Sheng verfasserin aut Event-triggered Trajectory Tracking Control of Marine Surface Vessels With Time-varying Output Constraints Using Barrier Functions 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © ICROS, KIEE and Springer 2023 Abstract In this paper, we investigate the trajectory tracking control problem of marine surface vessels (MSVs) subject to time-varying output constraints, unknown external disturbances, and model uncertainties. To this end, a novel log-type universal barrier Lyapunov function (BLF) with prescribed performance is proposed to ensure the predefined dynamic performance of output tracking errors and good tracking accuracy. Then, a neural network considering approximation errors is designed to handle complex uncertainties. Furthermore, a relative threshold event-triggered mechanism is introduced in the controller design to reduce the actuator execution rate. With the aid of the BLF and the event-triggered mechanism in combination with the adaptive neural network technique and backstepping design tool, a novel adaptive neural network event-triggered trajectory tracking control scheme is presented. A rigorous stability analysis verifies that all signals in the closed-loop system are semi-globally uniformly ultimately bounded, and the prescribed time-varying constraints on the tracking errors would not be violated. At the same time, the Zeno behavior can be avoided. The numerical comparative simulation experiments validate the effectiveness and superiority of the proposed trajectory tracking control scheme. Event-triggered control (dpeaa)DE-He213 marine surface vessel (dpeaa)DE-He213 trajectory tracking (dpeaa)DE-He213 universal barrier Lyapunov function (dpeaa)DE-He213 Shen, Zhipeng (orcid)0000-0003-0210-2046 aut Zhou, Lu aut Yu, Haomiao aut Enthalten in International Journal of Control, Automation and Systems Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers, 2009 21(2023), 8 vom: 23. Juni, Seite 2708-2717 (DE-627)SPR026303256 nnns volume:21 year:2023 number:8 day:23 month:06 pages:2708-2717 https://dx.doi.org/10.1007/s12555-022-0072-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_21 GBV_ILN_24 GBV_ILN_72 GBV_ILN_181 GBV_ILN_496 GBV_ILN_2002 GBV_ILN_2003 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2060 GBV_ILN_2470 AR 21 2023 8 23 06 2708-2717 |
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10.1007/s12555-022-0072-1 doi (DE-627)SPR052643034 (SPR)s12555-022-0072-1-e DE-627 ger DE-627 rakwb eng Dong, Sheng verfasserin aut Event-triggered Trajectory Tracking Control of Marine Surface Vessels With Time-varying Output Constraints Using Barrier Functions 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © ICROS, KIEE and Springer 2023 Abstract In this paper, we investigate the trajectory tracking control problem of marine surface vessels (MSVs) subject to time-varying output constraints, unknown external disturbances, and model uncertainties. To this end, a novel log-type universal barrier Lyapunov function (BLF) with prescribed performance is proposed to ensure the predefined dynamic performance of output tracking errors and good tracking accuracy. Then, a neural network considering approximation errors is designed to handle complex uncertainties. Furthermore, a relative threshold event-triggered mechanism is introduced in the controller design to reduce the actuator execution rate. With the aid of the BLF and the event-triggered mechanism in combination with the adaptive neural network technique and backstepping design tool, a novel adaptive neural network event-triggered trajectory tracking control scheme is presented. A rigorous stability analysis verifies that all signals in the closed-loop system are semi-globally uniformly ultimately bounded, and the prescribed time-varying constraints on the tracking errors would not be violated. At the same time, the Zeno behavior can be avoided. The numerical comparative simulation experiments validate the effectiveness and superiority of the proposed trajectory tracking control scheme. Event-triggered control (dpeaa)DE-He213 marine surface vessel (dpeaa)DE-He213 trajectory tracking (dpeaa)DE-He213 universal barrier Lyapunov function (dpeaa)DE-He213 Shen, Zhipeng (orcid)0000-0003-0210-2046 aut Zhou, Lu aut Yu, Haomiao aut Enthalten in International Journal of Control, Automation and Systems Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers, 2009 21(2023), 8 vom: 23. Juni, Seite 2708-2717 (DE-627)SPR026303256 nnns volume:21 year:2023 number:8 day:23 month:06 pages:2708-2717 https://dx.doi.org/10.1007/s12555-022-0072-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_21 GBV_ILN_24 GBV_ILN_72 GBV_ILN_181 GBV_ILN_496 GBV_ILN_2002 GBV_ILN_2003 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2060 GBV_ILN_2470 AR 21 2023 8 23 06 2708-2717 |
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10.1007/s12555-022-0072-1 doi (DE-627)SPR052643034 (SPR)s12555-022-0072-1-e DE-627 ger DE-627 rakwb eng Dong, Sheng verfasserin aut Event-triggered Trajectory Tracking Control of Marine Surface Vessels With Time-varying Output Constraints Using Barrier Functions 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © ICROS, KIEE and Springer 2023 Abstract In this paper, we investigate the trajectory tracking control problem of marine surface vessels (MSVs) subject to time-varying output constraints, unknown external disturbances, and model uncertainties. To this end, a novel log-type universal barrier Lyapunov function (BLF) with prescribed performance is proposed to ensure the predefined dynamic performance of output tracking errors and good tracking accuracy. Then, a neural network considering approximation errors is designed to handle complex uncertainties. Furthermore, a relative threshold event-triggered mechanism is introduced in the controller design to reduce the actuator execution rate. With the aid of the BLF and the event-triggered mechanism in combination with the adaptive neural network technique and backstepping design tool, a novel adaptive neural network event-triggered trajectory tracking control scheme is presented. A rigorous stability analysis verifies that all signals in the closed-loop system are semi-globally uniformly ultimately bounded, and the prescribed time-varying constraints on the tracking errors would not be violated. At the same time, the Zeno behavior can be avoided. The numerical comparative simulation experiments validate the effectiveness and superiority of the proposed trajectory tracking control scheme. Event-triggered control (dpeaa)DE-He213 marine surface vessel (dpeaa)DE-He213 trajectory tracking (dpeaa)DE-He213 universal barrier Lyapunov function (dpeaa)DE-He213 Shen, Zhipeng (orcid)0000-0003-0210-2046 aut Zhou, Lu aut Yu, Haomiao aut Enthalten in International Journal of Control, Automation and Systems Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers, 2009 21(2023), 8 vom: 23. Juni, Seite 2708-2717 (DE-627)SPR026303256 nnns volume:21 year:2023 number:8 day:23 month:06 pages:2708-2717 https://dx.doi.org/10.1007/s12555-022-0072-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_21 GBV_ILN_24 GBV_ILN_72 GBV_ILN_181 GBV_ILN_496 GBV_ILN_2002 GBV_ILN_2003 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2060 GBV_ILN_2470 AR 21 2023 8 23 06 2708-2717 |
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Event-triggered Trajectory Tracking Control of Marine Surface Vessels With Time-varying Output Constraints Using Barrier Functions Event-triggered control (dpeaa)DE-He213 marine surface vessel (dpeaa)DE-He213 trajectory tracking (dpeaa)DE-He213 universal barrier Lyapunov function (dpeaa)DE-He213 |
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misc Event-triggered control misc marine surface vessel misc trajectory tracking misc universal barrier Lyapunov function |
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International Journal of Control, Automation and Systems |
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Event-triggered Trajectory Tracking Control of Marine Surface Vessels With Time-varying Output Constraints Using Barrier Functions |
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Event-triggered Trajectory Tracking Control of Marine Surface Vessels With Time-varying Output Constraints Using Barrier Functions |
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Dong, Sheng |
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Dong, Sheng Shen, Zhipeng Zhou, Lu Yu, Haomiao |
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10.1007/s12555-022-0072-1 |
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event-triggered trajectory tracking control of marine surface vessels with time-varying output constraints using barrier functions |
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Event-triggered Trajectory Tracking Control of Marine Surface Vessels With Time-varying Output Constraints Using Barrier Functions |
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Abstract In this paper, we investigate the trajectory tracking control problem of marine surface vessels (MSVs) subject to time-varying output constraints, unknown external disturbances, and model uncertainties. To this end, a novel log-type universal barrier Lyapunov function (BLF) with prescribed performance is proposed to ensure the predefined dynamic performance of output tracking errors and good tracking accuracy. Then, a neural network considering approximation errors is designed to handle complex uncertainties. Furthermore, a relative threshold event-triggered mechanism is introduced in the controller design to reduce the actuator execution rate. With the aid of the BLF and the event-triggered mechanism in combination with the adaptive neural network technique and backstepping design tool, a novel adaptive neural network event-triggered trajectory tracking control scheme is presented. A rigorous stability analysis verifies that all signals in the closed-loop system are semi-globally uniformly ultimately bounded, and the prescribed time-varying constraints on the tracking errors would not be violated. At the same time, the Zeno behavior can be avoided. The numerical comparative simulation experiments validate the effectiveness and superiority of the proposed trajectory tracking control scheme. © ICROS, KIEE and Springer 2023 |
| abstractGer |
Abstract In this paper, we investigate the trajectory tracking control problem of marine surface vessels (MSVs) subject to time-varying output constraints, unknown external disturbances, and model uncertainties. To this end, a novel log-type universal barrier Lyapunov function (BLF) with prescribed performance is proposed to ensure the predefined dynamic performance of output tracking errors and good tracking accuracy. Then, a neural network considering approximation errors is designed to handle complex uncertainties. Furthermore, a relative threshold event-triggered mechanism is introduced in the controller design to reduce the actuator execution rate. With the aid of the BLF and the event-triggered mechanism in combination with the adaptive neural network technique and backstepping design tool, a novel adaptive neural network event-triggered trajectory tracking control scheme is presented. A rigorous stability analysis verifies that all signals in the closed-loop system are semi-globally uniformly ultimately bounded, and the prescribed time-varying constraints on the tracking errors would not be violated. At the same time, the Zeno behavior can be avoided. The numerical comparative simulation experiments validate the effectiveness and superiority of the proposed trajectory tracking control scheme. © ICROS, KIEE and Springer 2023 |
| abstract_unstemmed |
Abstract In this paper, we investigate the trajectory tracking control problem of marine surface vessels (MSVs) subject to time-varying output constraints, unknown external disturbances, and model uncertainties. To this end, a novel log-type universal barrier Lyapunov function (BLF) with prescribed performance is proposed to ensure the predefined dynamic performance of output tracking errors and good tracking accuracy. Then, a neural network considering approximation errors is designed to handle complex uncertainties. Furthermore, a relative threshold event-triggered mechanism is introduced in the controller design to reduce the actuator execution rate. With the aid of the BLF and the event-triggered mechanism in combination with the adaptive neural network technique and backstepping design tool, a novel adaptive neural network event-triggered trajectory tracking control scheme is presented. A rigorous stability analysis verifies that all signals in the closed-loop system are semi-globally uniformly ultimately bounded, and the prescribed time-varying constraints on the tracking errors would not be violated. At the same time, the Zeno behavior can be avoided. The numerical comparative simulation experiments validate the effectiveness and superiority of the proposed trajectory tracking control scheme. © ICROS, KIEE and Springer 2023 |
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Event-triggered Trajectory Tracking Control of Marine Surface Vessels With Time-varying Output Constraints Using Barrier Functions |
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