Adaptive Backstepping Control For Trajectory Tracking Of Fully Actuated Surface Vessel With Input Saturation

Fully actuated trajectory tracking surface vessels are often employed in specific engineering operations,such as marine cargo transportation,offshore oil exploration,subsea pipeline construction and supply owing to their flexible operation and high safety performance.However,the surface vessel actuators are very vulnerable to physical constraints in the actual marine operation.And too large controller signal will lead to the damage of the vessel actuator even destabilize the system,so it is of great practical significance to study the full actuated surface vessel trajectory tracking control problem with input saturation.In this paper,a fully actuated surface vessel is taken as the simulation object,combining the nonlinear control theory,.dynamic surface control technology,barrier Lyapunov function,prescribed performance function,command filtering technology,neural network,adaptive technology,the Nussbaum function and other theoretical tools.Considering the problems of system uncertainties,input saturation,output saturation,the prescribed performance and the environmental disturbances.The main research work includes the following aspects:Firstly,concentrated on the problem of the surface vessels with uncertainties and input saturation in the presence of external disturbances,a minimum learning parameter method(MLP)-based neural network adaptive recursive sliding mode dynamic surface control strategy is proposed.In this strategy,the minimum learning parameter method is combined with the neural network technology to.approximate the system uncertainties and reduce the calculation of neural network effectively.The dynamic surface control combined with recursive concept is used to avoid the differential explosion and reduce the.parameter perturbation caused by the dynamic surface control.Then the piecewise smooth function is used to estimate saturation function,the auxiliary system and Nussbaum function are used to compensate the control law.By the Lyapunov stability theory,we can get that the proposed control method can guarantee the uniform boundedness of all the closed loop signals.The simulation results show the effectiveness of the proposed controller which improves the tracking accuracy.The limited force and torque are smooth and reasonable.Secondly,the surface vessel inevitably encounters narrow water channel or navigation congestion in the actual marine engineering.In this chapter,a barrier Lyapunov function-based adaptive recursive sliding mode control scheme is designed.A time-varying asymmetric barrer Lyapunov function is utilized to deal with the problem of output saturation and relax the initial constraint condition.The command filter control combined with recursive concept is used to limit the amplitude of control signal,avoid the differential explosion and enhance the robust performance of the system.The minimum learning parameter technology combined with neural network is employed to approximate the system uncertainties,the adaptive law is used to estimate the error of neural network and external disturbs.By the Lyapunov stability theory,we can get that the proposed control method can guarantee the uniform boundedness of all the closed loop signals.The simulation results show the effectiveness of the proposed controller which improves the tracking accuracy and limits the trajectory of the surface vessel in the time-varying range.Thirdly,the transient performance cannot be ignored when the steady-state performance be studied.In order to constrain the transient performance of the system,a predictor-based neural network command filter recursive sliding mode backstepping control strategy is designed,which combines the prescribed performance function with the barrier Lyapunov function to constrain the transient and steady state of the system,and use the predictor combined with the neural network to approximate the system unknown parameters and external disturbances,improve the system transient performance,and employ adaptive technology to estimate the neural network errors in order to elevate the system accuracy.By the Lyapunov stability theory,we can get that the proposed control method can guarantee the uniform boundedness of all the closed loop signals.The simulation results show the effectiveness of the proposed controller which constrains the steady and transient performance of the system effectively.Finally,a 76.2m supply surface vessel is taken as the research simulation object and the simulation results show that the proposed controllers in this dissertation can maintain certain control accuracy and satisfy control objective in case of the actual problems of control input saturation,output saturation,the transient performance and the steady-state performance,the system uncertainties or parameter uncertainties,and unknown external disturbances.