Robust MPC for Nonholonomic Robots with Moving Obstacle Avoidance

Abstract

In this paper, we propose a robust model predictive control algorithm for discrete-time nonholonomic robot systems with additive disturbances. To achieve moving obstacle avoidance, the related polyhedral over-approximations are utilized to realize the reformulation of obstacle avoidance constraint. Thus, the resulting model predictive control optimization problem can be solved effectively by standard nonlinear programming solvers. Moreover, the theoretical guarantees for recursive feasibility and input-to-state stability are provided. Finally, the efficiency of the proposed algorithm is verified by the simulation results.