Research On Target Tracking And Formation Control Of Underactuated Unmanned Surface Vessels

Unmanned vessels have a major role to play in many marine operations,such as resource exploration,maritime rescue,automatic sea cruising and unknown marine environment sampling and detection,etc.The key technologies of USV control include trajectory tracking,target tracking and formation control,etc.In this paper,we take the underactuated USV as the research object to study the target tracking and the formation control in the case of unknown external environmental disturbance.The main contents are as follows:To address the problem that the model uncertainties and unknown external environmental disturbances seriously affect the performance and stability of the control system during the navigation,an adaptive self-structuring neural network-based target tracking control strategy is proposed.Firstly,an extended state observer is adopted to solve the problem of unpredictable velocity of the tracking target,secondly,a selfstructuring neural network(SNN)is designed for the model uncertainties and unknown environmental disturbance,which can approximate the model uncertainties and unknown environmental disturbance and improve the robustness of the control system,and SNN saves a large amount of computational power by optimizing the neural network structure.Finally,a robust target tracking controller is designed by combining the backstepping method.The main result analysis shows that the whole closed-loop error system is input-state stable(ISS).The simulation experiments verify the effectiveness of the control algorithm.Based on the above single-vessel target tracking,the underactuated USV formation control is further investigated.For the underactuated USV formation control problem with non-diagonal inertia matrix and compound unknown dynamics,an adaptive neural network(NN)finite-time formation control strategy based on prescribed performance is proposed.Firstly,a coordinate transformation method is adopted to solve the non-diagonal inertia matrix problem,a high-gain observer is designed to estimate the leader’s velocity,a tan-type Barrier Lyapunov Function(BLF)is introduced to ensure formation distance and angle constraints,while ensuring collision avoidance and maintaining effective communication distance.And finally,a self-structuring neural network is combined to compensate for the uncertainties consisting of the model and unknown environmental disturbances,which can effectively reduce the NNs computation and save computational resources.The stability analysis results show that all signals of the closed-loop system are bounded and the closed-loop system is practical finite-time stable(PFS).Simulation experiments verify the effectiveness of the control method and the designed controller has high accuracy and strong robustness.For the problem of insufficient communication bandwidth and limited communication resources during formation motion control,a time-varying BLF-based finite-time event-triggered control strategy is proposed.Firstly,since the tan-type BLF is too complicated for the controller design of underactuated USVs,a relatively simpler design of time-varying BLF is adopted to improve the system performance,and secondly,the dynamic surface control(DSC)is designed in combination with the backstepping method to avoid differential explosion.Finally,an underactuated USV finite-time formation controller is designed in combination with an event-triggered strategy to solve the problem of insufficient communication bandwidth in the formation control process.The result analysis shows that all signals of the closed-loop system are bounded and the closed-loop system is practical finite-time stable(PFS).Finally,the simulation experiment proves the effectiveness of the control method.To address the problem that finite-time control in the formation control process is too dependent on the initial state,a fixed-time underactuated USV formation control strategy based on the minimum learning parameters(MLP)and relative threshold event-triggered is proposed.Firstly,a neural network-based fixed-time adaptive MLP is designed to solve the problem of the influence of external disturbance and model uncertainties on the control system,and a fixed-time adaptive compensation law is designed to compensate for the MLP and improve the accuracy of the control.The relative threshold event-triggered strategy is proposed to solve the problem of insufficient communication bandwidth in formation control.Finally,the neural network-based fixed-time event-triggered formation controller is designed by combining the MLP,backstepping and relative threshold event-triggered strategy.The stability analysis shows that all signals of the closed-loop system are bounded and the closed-loop system is practical fixed-time stable(PFTS),and the numerical simulation results verify the effectiveness and superior performance of the algorithm.