Robust Repetitive Learning Control For Trajectory Tracking Of Robotic Manipulators

Robotic manipulator is a highly complex and a strongly coupling nonlinear system.High-accuracy trajectory tracking control of robotic manipulators has always been a topic that continuous challenges researcher in robotics and automation control field.Given the myriad of industrial applications that require a robot to move in repetitive manner,repetitive learning control offers an easy-going scheme for high-accuracy tracking of robotic manipulators.Repetitive learning control is based on internal model principle,it uses the error overlay of the current cycle and the previous cycle as a new control input to the actuator for precise trajectory control.By virtue of the periodic nature of the robot dynamics,two robust repetitive learning control schemes are proposed in this thesis.The main work is as follows.1?A new terminal sliding mode repetitive learning control(FTSRC)method is proposed for tracking of robotic manipulators.A class of non-linear functions is employed to design an approximate fast terminal sliding mode and thus the robot system trajectory tracking FTSRC is formed.Using the Lyapunov stability theory and Barbalat lemma,it is proved that the global asymptotic stability of the robotic closed-loop system can be guaranteed and the trajectory tracking error of the robotic system can converge to an arbitrary small domain centered on zero in a finite time globally and finally go to zero asymptotically.The proposed control is synthesized without reference to modeling parameter and thus it features easy implementation with strong robustness.Compared with the conventional sliding-mode repetitive learning control(RC)and the terminal sliding-mode repetitive learning control(TSRC),the proposed FTSRC has faster transient response and higher steady-state tracking accuracy.This favorable result is obtained without an excessive control input.2?A fast non-singular integral terminal sliding mode repetitive learning control method(FITSRC)is proposed for trajectory tracking of robotic manipulators.A class of non-linear functions is employed to design an integral terminal sliding mode and thus the robot system trajectory tracking FITSRC is formed.Using the Lyapunov stability theory,it is proved that the global asymptotic stability of the closed-loop system of the robot system is guaranteed and the trajectory tracking error of the system convergence to zero in a finite time globally.Compared with the TSRC,the proposed control(FITSRC)has faster dynamic response and higher steady-state tracking accuracy.While compared with FTSRC,the FITSRC has a faster dynamic response.This is also obtained without an excessive control input.The proposed schemes combine the sliding mode control with the repetitive learning control to achieve a good tracking performance of the system.The hybrid control schemes can effectively track the periodic reference signal and suppress the periodic or non-periodic dynamic disturbance without accurately knowing the dynamic model of the system.Lyapunov's direct method is employed to prove global asymptotic tracking.Numerical results on a three degree-of-freedom(DOF)robot are presented to demonstrate the effectiveness and improved performance of the proposed control scheme.