Adaptive multi-mode control system for stability and tracking control of distributed drive automated electric vehicle

Abstract

Conventional tracking control algorithms of Distributed Drive Automated Electric Vehicles (DDAEVs) are typically designed for a unique control mode, which may lead to poor lateral stability under various driving conditions. Aimed this problem, an Adaptive Multi-mode Control System (AMCS) is proposed. Firstly, according to the adhesion utilization between tires and road surface, the driving condition is classified into three categories, and three control modes corresponding to them are proposed. Secondly, an Adaptive Neuro-fuzzy Inference-based Mode Matching Control System (ANFI-MMCS) is constructed to calculate the matching degree between vehicle states and control modes. Then, in order to achieve the ideal distribution of road wheel angle and yaw moment while avoiding approaching the limit speed corresponding to the reference trajectory, a model predictive control trajectory tracking controller and a speed controller with limitations were built. Finally, to achieve smooth switching between control modes, the outputs of AMCS are weighted according to the matching degree. In the experimental section, AMCS is evaluated on Carsim-Simulink co-simulation platform, and the results under different tests reveal that the proposed controller is more adaptable.