Nonlinear model predictive control for industrial robot arms based on feedforward compensation

Abstract

To address the problems of poor dynamic performance and tracking overshoot of existing control strategies for industrial robot arms, this paper proposed a feedforward compensation-based nonlinear model predictive control (FNMPC) framework to achieve better tracking performance. First, the nonlinear frictional force of a six-degree-of-freedom robot manipulator is obtained by utilizing the least squares parameter identification method, and a more accurate dynamic model of the manipulator is established via the Newton-Euler method. Second, an FNMPC strategy is designed to achieve accurate tracking control of the robot manipulator. Finally, the performance of the proposed FNMPC strategy is verified by conducting a semi-physical simulation comparison experiment. Semi-physical simulation results show that the proposed control strategy exhibits superior performance in terms of dynamic response stationarity and cumulative tracking error.