Robust adaptive neural trajectory tracking control of unmanned surface vessels under input saturation

Author(s):  
Guibing Zhu ◽  
Jialu Du
Author(s):  
Haoping Wang ◽  
Shuyu Zhang

This article considers the trajectory tracking control for unmanned surface vessels with unknown time-variant external disturbances and input saturation. The strategy mainly consists of event-triggered reset sub-controller and nonlinear disturbance observer–based compensation sub-controller. To reduce network transmissions, and in the meanwhile, guarantee the desirable closed-loop behavior, the event-triggered reset control is proposed where the reset law and the event-triggered mechanism are designed separately. Both of static and dynamic event-triggered reset controllers are designed. Their corresponding stability is demonstrated using Lyapunov stability theory. Finally, numerical simulation results are presented to demonstrate the effectiveness and robustness of the proposed trajectory tracking control strategy.


2018 ◽  
Vol 15 (5) ◽  
pp. 172988141880811 ◽  
Author(s):  
Hongde Qin ◽  
Chengpeng Li ◽  
Yanchao Sun ◽  
Zhongchao Deng ◽  
Yuhan Liu

This article investigates the trajectory tracking control problem for unmanned surface vessels with input saturation and full-state constraints. The barrier Lyapunov function is used to solve the problem of state constraints, and the adaptive method is employed to handle the unknown random disturbances and saturation problems. The proposed control approach can guarantee that the control law and signals of closed-loop system are uniformly bounded and achieve the asymptotic tracking. Finally, simulation studies are provided to show the effectiveness of the proposed method.


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