Robust Tube-Based TS-MPC for Safe Coordination of Autonomous Vehicle

Abstract

In this work, a robust vehicle control scheme is proposed, which is capable of coordinating with nearby vehicles in order to optimally compute control actions that achieve collision-free overtaking maneuvers. The control actions are computed online by a global model predictive control (MPC) controller, which assumes a nominal disturbance-free vehicle model. To reduce the computational burden of the MPCs optimization problem, the vehicle model is reformulated into a pseudo-linear Takagi-Sugeno (TS) representation. Furthermore, the mismatch error between the real and the nominal model is corrected by a local TS H∞-optimal state-feedback controller. Moreover, the robust feasibility of the MPCs optimization problem is guaranteed by implementing a tube-based architecture. Finally, the proposed control scheme is tested and validated in a high-fidelity simulation, in which the controlled vehicle was capable of overtaking multiple vehicles while rejecting disturbances.