Optimal control of intelligent vehicle longitudinal dynamics via hybrid model predictive control

This paper presents an innovative application of the hybrid model predictive control (HMPC) scheme to optimally regulate the intelligent vehicle longitudinal velocity. For autonomous velocity regulation, the intelligent vehicle needs to be operated in two distinct modes (drive and brake) and because of the mode-dependent constraints on accelerations and decelerations by considering the comfort of passengers, the intelligent vehicle longitudinal dynamics control process can be regarded as a constrained hybrid dynamical system. Thus, in this study, the intelligent vehicle longitudinal dynamics is approximated as a two-mode discrete-time mixed logical dynamical (MLD) system. Using this approximation, a hybrid model predictive controller, which allows us to optimize the switching sequences of the operation modes (binary control inputs) and the torques acted on the wheels (continuous control inputs), is tuned based on online mixed-integer quadratic programming. Numerical simulation analysis is conducted for a sport utility vehicle to demonstrate the effectiveness of the proposed control method. Finally, the explicit representation of the HMPC is computed to control the intelligent vehicle in real-time, and the experimental results are presented to show the applicability of the proposed controller. (C) 2018 Elsevier B.V. All rights reserved.