No abstract

This paper is concerned with the application of reinforcement learning to the dynamic ride control of an active vehicle suspension system. The study makes key extensions to earlier simulation work to enable on-line implementation of the learning automaton methodology using an actual vehicle. Extensions to the methodology allow safe and continuous learning to take place on the road, using a limited instrumentation set. An important new feature is the use of a moderator to set physical limits on the vehicle states. It is shown that the addition of the moderator has little direct effect on the system's ability to learn, and allows learning to take place continuously even when there are unstable controllers present. The study concludes with the results of an experimental trial using vehicle hardware, where the successful synthesis of a semi-active ride controller is demonstrated.

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