Robust model predictive control for robotic manipulators with fully actuated system approach based on learning modelling
Yi Yang, Kai Zhang, Zhi Hua Chen, Bin Li
Abstract
This article puts forward a robust model predictive control (MPC) strategy fortified by a learning model within the framework of fully actuated system (FAS) approach. The proposed strategy is crafted to tackle the control difficulties of robotic manipulators under the limitations of joint angles and angular velocities. By leveraging a radial basis function neural network (RBFNN) to offset the inaccuracies in the manipulator’s dynamic modelling, a FAS model with RBFNN is constructed. Based on this, the FAS approach is utilised to formulate the nominal controller, which can efficiently manage the nonlinear and coupled dynamics of the system. To deal with the disparities between the learning model and the actual system dynamics, the robust MPC is incorporated. This also ensures compliance with operating limits and improves the accuracy of the tracking control. Moreover, the iterative feasibility and stability analysis are completed. The effectiveness of the proposed method is validated through simulations with the Franka robotic numerical model.