Study On Trajectory Tracking Control Of Unmanned Surface Vessels With State Constraints

As an unmanned vehicle with wide application,the unmanned surface vessel(USV)has high research value in the field of marine technology.It is necessary to realize the accurate and effective trajectory tracking control for USV so that it can navigate on schedule and fulfil the mission.However,there exist several factors in the tracking control,such as model uncertainty,input saturation,and random external disturbance,which have a great influence on the control performance.Especially for the problem of state constraints,it will cause equipment damage,control failure,and other serious consequences,when the USV violates the constraints on the tracking control.Therefore,it is necessary to consider these factors in the trajectory tracking control design for USV,ensuring the feasibility and effectiveness of the control strategy.Based on the barrier Lyapunov function,this study investigates the trajectory tracking control for USV with state constraints,including the problems of uncertainty and input saturation.The proposed control strategies are verified by numerical simulation.The main contributions of this study are shown as follows.Firstly,a backstepping-based barrier Lyapunov function in the logarithmic form is presented for USV,which realizes the state constraints by limiting the tracking errors to boundary conditions.Meanwhile,the upper bounds of external disturbance and input saturation are estimated by adaptive method,and the relevant adaptive law and control law are designed.Then,a tangent-type barrier Lyapunov function is utilized to design a trajectory tracking controller for USV with error constraints.The tracking errors are constrained in the prescribed ranges by converting the constrained variables to the Euclidean norm.Radial basis function neural network,which has the excellent ability to approximate any complex nonlinear term,is used to deal with the model uncertainty of USV.Meanwhile,the hyperbolic tangent function is employed to address the input saturation problem,which is converted into unknown input gains.Finally,a finite-time control scheme is proposed for USV.In addition,based on the control scheme with error constraint,an adaptive fault-tolerant trajectory tracking controller is proposed for USV.Considering the problem of actuator fault,the upper bound of fault uncertainty is estimated by adaptive method.Finally,the tracking control problem of USV under deferred and asymmetric constraints is investigated,which occur after a period of system operation rather than at the initial time.Based on the shift function and error transformation system,an asymmetric barrier Lyapunov function with time-varying boundary is proposed.It can deal with deferred and asymmetric full-state constraints under completely unknown initial tracking condition.The neural networks are employed to solve the uncertainty problem and the anti-windup compensator is used to address the influence of input saturation,which compensates for the saturation difference directly.This paper takes the state constraints for USV as the core problem and presents several trajectory tracking control strategies based on barrier Lyapunov function.The simulation results verify the effectiveness and superiority of the proposed methods,which can be used as a reference for further research.