Trajectory Tracking Control Of Autonomous Underwater Vehicle-Manipulator Systems

In the last 20 years,to alleviate the energy crisis on the land,many efforts have been made to develop marine tools for ocean exploration and exploitation,such as unmanned underwater vehicles(UUVs).And,it becomes an effective way,which gradually attracts people’s attention,to utilize an autonomous underwater vehicle-manipulator system(AUVMS)to dock,capture or recover the target UUV.In particular,the AUVMS is subjected to dynamic natures like high nonlinearities,strong coupling,and time-varying characteristics,and suffers from the effects caused by complex hydrodynamics and unknown disturbances in the underwater environment.Thus,there are many difficulties in controlling the AUVMS to complete the target capture and docking.To solve the above problems,in this dissertation theoretical research has been carried out on the dynamic modeling and trajectory tracking of the AUVMS.In this dissertation,the kinematic and dynamic models of the autonomous underwater vehicle(AUV)and the underwater manipulator are established separately according to kinematic,dynamic,and hydrodynamic theories.The Quasi-Lagrange method is employed to establish the whole dynamic model of the AUVMS,which provides the theoretical basis for the design of trajectory tracking control algorithms.As for the trajectory tracking control problem of the AUV,it is necessary to handle the effects of the lumped disturbances exerted on the system generated by model uncertainties,ocean currents,limitations on the input forces,etc.Considering the available measurements of both positions/attitude angles and their velocities,and the bounded lumped disturbances,an adaptive radial-basis-function neural network method is designed based on the state observer and the backstepping technique to guarantee the boundness of estimation errors of the observer states and disturbances.Furthermore,considering the measurable positions/attitude angles without velocity measurements,and the bounded first-time derivative of the lumped disturbances,a fractional PID-type sliding mode control(SMC)with extended state observer(ESO)is proposed to enforce disturbance estimation errors converge to a bounded neighborhood of the origin in finite time.On this basis,a modified SMC method is designed by combining the unit quaternion theory and the ESO.A modified nonsingular terminal sliding mode control method with adaptive wave neural network is proposed for the trajectory tracking control problem of the underwater manipulator,with available joint position and velocity measurements,and bounded lumped disturbances.The method can guarantee the boundness of the disturbance estimation errors.Then,considering the measurable joint positions with its unmeasured velocities,and the bounded second-time derivative of the lumped disturbances,a modified fractional integral SMC strategy based on disturbance observer is designed to ensure the finite-time convergence of the disturbance estimation errors.The quaternion-based closed-loop inverse kinematic algorithm is designed for the trajectory tracking control problem of the AUVMS.Considering the available measurements of both the positions/attitude angles of the AUV and the joint positions of the underwater manipulator with their velocities,and the bounded first-time derivative of the lumped disturbances,an adaptive modified nonsingular fast terminal sliding mode control scheme with ESO is proposed on the basis of the whole control.Consider the case that the positions/attitude angles of the AUV and the joint positions of the underwater manipulator are measurable but without velocity measurements,meanwhile,both the first-time derivative of the lumped disturbances of the AUV and the second-time derivative of the lumped disturbances of the underwater manipulator are bounded.A hybrid control scheme is designed by expanding the whole control to the block processing mode.Both the proposed schemes can ensure that the disturbance estimation errors achieve convergence in finite time.All the proposed control methods can guarantee the system’s stability via the Lyapunov theory.Concurrently,satisfactory trajectory tracking performance and robustness against disturbances are verified by the simulation results.