Car-following stability improvement of cooperative adaptive cruise control based on distributed model predictive control

Abstract

To solve the problem of large fluctuation of vehicle following distance in cooperative adaptive cruise control (CACC), a distributed model predictive control (DMPC) strategy is proposed. The idea of hierarchical control is performed to control the CACC system. The controller is divided into an upper controller and a lower controller. The upper controller calculates the expected acceleration of the vehicle according to the platooning state, and the lower controller controls the throttle and braking system pressure of the vehicle according to the expected acceleration. Firstly, the longitudinal dynamic model of vehicle platooning is established. Secondly, the objective function is designed according to the control objectives, so that the platooning can obtain the optimal control quantity at the current time. Meanwhile, the robust design is used to improve the controller performance, and the optimization of reference trajectory and the extension of feasible domain are used to improve the stability of the controller. Car-following Stability therefore can be improved. Then the lower controller is designed based on a reverse engine model and a reverse braking model. Finally, the effectiveness of the designed control strategy is verified by the co-simulation of Carsim and MATLAB/Simulink. The results show that DMPC can reduce the peak value, the standard deviation, and the root mean square of vehicle following distance error and improve the following stability.