Trajectory Tracking Of Robotic Manipulator Based On Terminal Sliding Mode Control

In this thesis,the Denso VP 6242 serial manipulator is taken as the controlled object,and the trajectory tracking control strategy of the manipulator is studied.Firstly,the Denso manipulator is modeled.Kinematics modeling includes forward kinematics modeling using standard D-H parameter method and inverse kinematics modeling using geometric method as well as analytical method.Simulation test proves the accuracy of kinematics modeling.According to Lagrange equation and the physical parameters of the manipulator,the dynamic model is established,which provides the simulation test object for the control algorithm designed later.A terminal sliding mode control strategy with adaptive fuzzy system(FTSM_Fuzzy)is proposed for trajectory tracking control of manipulators with difficult accurate modeling and environmental disturbances.The improved double exponential approaching law is adopted to improve the convergence speeds of the system states during the approaching stage and suppress the output chattering of the controller.Non-singular fast terminal sliding surface is adopted to ensure the global fast convergence.Adaptive MIMO fuzzy system is untilized to approximate the system model and external disturbance,replacing the system model parameters which can not be accurately obtained,thus improving the trajectory tracking accuracy and anti-jamming ability.The closed-loop stability and finite time convergence of the system are proved by constructing the Lyapunov functions.The simulation comparison experiments prove the superiority of the FTSM_Fuzzy controller.In order to solve the computational burden caused by a large number of fuzzy rules in FTSM_Fuzzy,an adaptive iterative learning control strategy based on second-order terminal sliding mode(STSM_ILC)is proposed for the manipulators which perform periodic repetitive motion in actual working conditions.The strategy adds an integral term to the non-singular fast terminal sliding mode surface to construct a second-order terminal sliding mode controller,which improves the trajectory tracking speed and suppresses the output chattering.The adaptive iterative learning method is used to learn the state information of each working cycle,thus the output of control variables is corrected to improve the control accuracy.The convergence of this control strategy with increasing iterations is proved by the Lyapunov like energy function.Simulation comparison experiments prove the superiority of the STSM_ILC controller.Finally,the two control strategies are verified on the Quanser experimental platform.A high-order chain differentiator is designed to solve the problem that FTSM_Suzzy can not accurately obtain the joint angular velocity and acceleration in the experiment.The theoretical analysis and convergence proof of the initial state change of STSM_ILC in the actual working conditions.Compared with the traditional linear sliding mode controller,the experimental results show that the two control strategies designed in this thesis can improve the tracking accuracy and anti-jamming ability of the manipulator,as well as alleviate the chattering of the control variables.