Nonlinear sliding mode control of manipulator based on iterative learning algorithm

Abstract

Manipulator is a complex nonlinear system. Due to its uncertainty and other factors, it is difficult to establish an accurate control model, which brings some difficulties to the high-precision estimation and tracking of manipulator. Aiming at the problems of traditional manipulator control trajectory control, joint angular displacement control and joint position tracking deviation control, a nonlinear sliding mode control method of manipulator based on iterative learning algorithm is proposed. Taking the 7-DOF manipulator as the research object, according to its structural characteristics, the adaptive control method is adopted to realize the adaptive control of different loads. Considering Stribeck friction and external interference, a seven degree of freedom mechanical manual model is established, and the ideal input of manipulator control is obtained by using the model. In order to ensure the high robustness of the nonlinear motion system of the manipulator, the reset circuit is designed. Finally, the output of the controller is applied to the controlled system by using the iterative learning algorithm to obtain the output of the controlled system, so as to realize the nonlinear sliding mode control of the manipulator. The experimental results show that the control trajectory of this method fits well with the actual trajectory, the tracking error accuracy of the manipulator is high, the tracking deviation of joint angular displacement and joint position is small, and it has strong experience learning ability and robustness, which has practical application value.