The unmanned surface vessel (USV) plays an important role in smart ocean. This study proposes an adaptive fault-tolerant tracking control for USVs in the presence of input saturations and error constraints. A tan-type barrier Lyapunov function is utilised for the error constraints and the neural networks are employed to treat the model uncertainty. Moreover, the adaptive technique combined with the backstepping method not only enables the actuator fault-tolerant controller to address the fault effects but also handles the external disturbances and input saturations. The proposed control approach can track the desired trajectory with error constraints and the system is guaranteed to be uniformly bounded under certain actuator failure. Numerical simulation is carried out to verify the effectiveness of this control strategy.
Adaptive neural network-based fault-tolerant trajectory-tracking control of unmanned surface vessels with input saturation and error constraints
IET Intelligent Transport Systems ; 14 , 5 ; 356-363
2019-07-30
8 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
model uncertainty , remotely operated vehicles , input saturation , fault tolerant control , unmanned surface vessel , uncertain systems , Lyapunov methods , neural networks , fault effects , neurocontrollers , adaptive technique , adaptive fault-tolerant tracking control , marine vehicles , trajectory control , USV , smart ocean , control nonlinearities , external disturbances , error constraints , tan-type barrier Lyapunov function , backstepping method , adaptive neural network-based fault-tolerant trajectory-tracking control , adaptive control , actuator fault-tolerant controller
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Neural network tracking control of ocean surface vessels with input saturation
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