Model Predictive Tracking Control for Rigid Manipulators with Disturbance Rejection

A model predictive tracking control algorithm for rigid manipulators considering the existence of external time-varying disturbances is studied in this paper. In the field of manipulators, disturbances are inevitable. Ignoring the influence of disturbances will lead to poor control accuracy and even non-convergence. For a two-degree-of-freedom (2-DoF) rigid manipulator, a composite algorithm combing model predictive control (MPC) and a generalized proportional integral observer (GPIO) is presented. In the proposed method, feedback linearization technology is used to decouple the system. In order to improve the ability of disturbance rejection, a GPIO is used to estimate the lumped disturbances and the estimated values are added to the control law by feedforward compensation. Simulation is conducted to validate the proposed algorithm.