Nonlinear Model Predictive Control for Wheeled Mobile Robot with Applications to Dynamic Environment

This paper tackles the regulation problem of wheeled mobile robot (WMR) by using the nonlinear model predictive control (NMPC) in polar coordination framework. The terminal state penalty, terminal state constraints, and the input saturation constraints are taken into consideration in this optimization problem to guarantee the closed-loop regulation performance and stability. Furthermore, we implemented NMPC algorithm to address the set-point regulation problem with obstacle avoidance strategies in a dynamic environment. The regulation performance under static obstacle and dynamic objective environment are been scrutinized. Some simulation results are used for illustrating the effectiveness of the control algorithm.