Dynamic vehicle roll control using reinforcement learning

A reinforcement learning strategy is applied to the problem of the dynamic roll control of a full-body vehicle system fitted with semi-active suspension under digital control. The simulation model used in this study is based upon realistic vehicle hardware. Prior engineering knowledge of the non-linear actuation system is used to develop a control structure. Parameters in this structure are then obtained using Continuous Action Reinforcement Learning Automata (CARLA), an extension of the interconnected learning automata methodology. No model-based information is used in the controller synthesis.