Affiliation:
1. Navigation College, Dalian Maritime University, Dalian, China
2. Department of Automation, Shanghai Jiao Tong University, Shanghai, China
Abstract
This study investigates the course-tracking problem for the unmanned surface vehicle in the presence of constraints of the actuator faults, control gain uncertainties, and environmental disturbance. A novel event-triggered robust neural control algorithm is proposed by fusing the robust neural damping technique and the event-triggered input mechanism. In the algorithm, no prior information of the system model about the unknown yawing dynamic parameters and unknown external disturbances is required. The transmission burden between the controller and the actuator could be relieved. Moreover, the control gain-related uncertainties and the unknown actuator faults are compensated through two updated online adaptive parameters. Sufficient effort has been made to verify the semi-global uniform ultimate bounded stability for the closed-loop system based on Lyapunov stability theory. Finally, simulation results are presented to illustrate the effectiveness and superiority of the proposed algorithm.
Funder
the Science and Technology Innovation Foundation of Dalian City
national postdoctoral program for innovative talents
national natural science foundation of china
natural science foundation of liaoning province
fundamental research funds for the central universities
Subject
Mechanical Engineering,Control and Systems Engineering
Cited by
16 articles.
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