Model Predictive Control with Incremental Input Constraints for Trajectory Tracking of Wheeled Mobile Robots

In this paper, we propose a model predictive control algorithm with incremental input constraints for trajectory tracking of wheeled mobile robot (WMR). This algorithm ensures that the rate of change of control inputs remains within a certain range, avoiding deterioration in the control performance or mechanical damage caused by excessive changes in control inputs. Firstly, based on the kinematics equations of WMR, we establish a trajectory tracking error model. Then, combining this model, we present a model predictive control algorithm with control increment constraints. Furthermore, by utilizing the properties of cost function monotonically decreasing along predicted trajectories and system states converging to equilibrium points, together with Lyapunov Theorem, we prove the asymptotic stability of the system. Finally, simulation results demonstrate that under this algorithm’s control, the trajectory tracking error model can stably converge to equilibrium point while satisfying incremental input constraints.