Fast zonotope‐tube‐based LPV‐MPC for autonomous vehicles
Résumé
In this study, the authors present an effective online tube-based model predictive control (T-MPC) solution for autonomous driving that aims at improving the computational load while ensuring robust stability and performance in fast and disturbed scenarios. They focus on reformulating the non-linear original problem into a pseudo-linear problem by transforming the non-linear vehicle equations to be expressed in a linear parameter varying (LPV) form. An scheme composed by a nominal controller and a corrective local controller is proposed. First, the local controller is designed as a polytopic LPV-H
controller able to reject external disturbances. Moreover, a finite number of accurate reachable sets, also called tube, are computed online using zonotopes taking into account the system dynamics, the local controller and the disturbance-uncertainty bounds considered. Second, the nominal controller is designed as an MPC where the LPV vehicle model is used to speed up the computational time while keeping accurate vehicle representation. They test the presented scheme and compared the local controller performance against the LQR design as state-of-the-art approach. They demonstrate its effectiveness in a disturbed fast driving scenario being able to reject strong exogenous disturbances and fulfilling imposed constraints at a very reduced computational cost.