Neural Network-Based Command Filtered Backstepping Control for USV with Input Saturation
A command filtered backstepping control method is proposed for the trajectory tracking problem of unmanned surface vehicle(USV)system with input saturation and unknown nonlinearity.The radial basis function neural network(RBFNN)is used for the approximation of the unknown dynamics,which can reduce the amount of adaptive laws and the complexity of the system.The control law is designed by the backstepping method.The command filter with error compensation mechanism is introduced into the system and the effect of filtering error is eliminated so that the control accuracy is improved.Finally,the effectiveness of the method is shown by the simulation experiment.
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