A Robust Model Predictive Control Strategy for Trajectory Tracking of Omni-directional Mobile Robots

This paper proposes a robust model predictive control (MPC) strategy for the trajectory tracking control of a four-mecanum-wheeled omni-directional mobile robot (FM-OMR) under various constraints. The method proposed in this paper can solve various constraints while implementing trajectory tracking of the FM-OMR. Firstly, a kinematics model with constraint relationship of the FM-OMR is established. On the basis of the kinematics model, the kinematics trajectory tracking error model of the FM-OMR is further formulated. Then, it is transformed into a constrained quadratic programming(QP) problem by the method of MPC. In addition, aiming at the speed deficiencies of conventional neural networks in QP solving, a delayed neural network (DNN) is applied to solve the optimal solution of the QP problem, and compared with the Lagrange programming neural network (LPNN) to show the rapidity of the DNN. Finally, two simulation cases considering bounded random disturbance are provided to verify the robustness and effectiveness of the proposed method. Theoretical analysis and simulation results show that the control strategy is effective and feasible.