Tracking Control Strategies Research Of Underactuated Autonomous Surface Vehicle Subject To Unknown Dynamics

Autonomous Surface Vehicle(ASV)stood out in a variety of marine equipment due to good maneuverability and strong renewable capacity.Currently,the ASVs play an important role in military and civilian.The underactuated ASVs have the advantages of lighter and lower cost due to lack of horizontal control inputs.However,there exit more unknown dynamics in complex ocean environment,included unknown model parameters、unmodeled dynamics、wave disturbances,and so on.Therefore,the underactuated ASV is as a research object in this thesis.Many nonlinear control methods are used in this thesis,such as backstepping method,adaptive method,sliding mode control.The position tracking,trajectory tracking and formation tracking of underactuated ASV are mainly studied.Main content and results of the thesis are gathered as:Based on coordinate conversion of geodetic fixed frame and body fixed frame,the kinematic equations are built.The overall forces and nonlinear damping items are taken into consideration,and the wave disturbance equations are built.The propeller model and rudder model are introduced to ASV.The underactuated property and symmetry are taken into consideration,the motion control model is established.Aiming at position tracking control and trajectory tracking control problem of ASV subject to unknown dynamics,robust adaptive position tracking strategy and trajectory tracking strategy are proposed,respectively.The position tracking controller and trajectory tracking controller are designed.The neural network are adopted to approximate and estimate the unknown dynamics of underactuated ASV.The predictor errors are combined with tracking errors to construct the adaptive laws such that the approximation abilities are improved.The finite time tracking differentiator is employed instead of taking derivative,the calculations are reduced.The sliding model controls are introduced to compensate wave disturbances,and the robustness are improved.The proposed controllers do not completely depend on the hydrodynamic parameters and mass parameters of the ASV,and they are easy in engineering.The Lyapunov stability thesis is employed to verify that all errors are uniformly ultimately bounded.Comparative simulations prove that designed strategies can realize position tracking and trajectory tracking highly effectively.Aim at trajectory tracking control problem of ASV subject to unavailable velocity,two neural network adaptive control strategies are proposed based on trajectory information,and two trajectory tracking controllers are designed considering the unavailable velocities.Firstly,a nonlinear velocity observer is designed to obtain velocity information,and the unknown hydrodynamic parameters are considered.The unknown dynamics are effectively approximated by neural network techniques.The bioinspired model is used instead of derivation.Moreover,a neural network velocity observer is designed to obtain velocity information.A filter compensation loop is designed to reduce the control error caused by integral process of second-order filter.The sliding mode control method is employed to compensate the system disturbances.The stability is improved.The Lyapunov stability theory are used to verify overall errors of two methods are uniformly ultimately bounded.The superiority and robustness of proposed methods are verified by comparison simulations.Aiming at formation control problem of ASV subject to unknown dynamics and unavailable velocity,two neural sliding mode robust control strategies are proposed,and two adaptive formation controller are designed.Firstly,the derivatives of virtual variable is obtained by finite time tracking differentiator.Combing with low-frequency learning technology,the neural networks are used to approximate unknown dynamics of underactuated ASV.The external disturbances are compensated by employing sliding mode adaptive method.Moreover,considering communication between the ASVs,the stability of formation controller are improved by sending message between ASVs.The control input auxiliary variable is designed to overcome the actuator saturation problem.The neural network is employed to solve the unknown dynamics problem of ASV.The differential compensation loop is established to reduce the differential error such that improved the system stability.Lyapunov theories are used to verify that all errors of closed loop are uniformly ultimately bounded.Comparative simulation experiments prove that designed control strategies are better than the traditional method.