Modeling and adaptive torque computed control of industrial robot based on lie algebra

Abstract

Abstract Computed Torque Control (CTC) is the most direct and effective way to improve the motion control performance of robot. But the computation of the joint torque is quite difficult, and because of the uncertainty of the parameters, an accurate inverse robot dynamic model for torque generation is difficult to obtain. An efficient inverse dynamic model of the industrial robot based on lie algebra is proposed and applied to the computed torque control. In order to overcome the uncertainty of parameters, the inverse robot dynamic model is linearized and an adaptive computed torque control is proposed. In order to validate the adaptive torque computed control method, a multi-domain integrated system model of 6-DOF industrial robot is established and the simulation results show that the adaptive computed torque control system has the function of parameter self-learning, the inaccurate parameters converge to the true value finally. The adaptive control shows better control performance than the traditional computed torque control.