Moderated Reinforcement Learning of Active and Semi-Active Vehicle Suspension Control Laws

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.

Online Reinforcement Learning-Based Control of an Active Suspension System Using the Actor Critic Approach

“…Despite the rare application of reinforcement learning in the active suspension problem, one of the earliest attempts to the best of our knowledge was conducted by [13][14][15]. Although there is a significant difference between their learning algorithms and the currently used algorithms, the core idea of learning by interactions and experience is the same.…”

“…The learning algorithm implemented by [13] was able to achieve near optimal results compared with the Linear Quadratic Gaussian (LQG) under idealized conditions. Reference [15] had the same approach, but they introduced a new learning scheme, which allowed the controller after learning to work in certain conditions where the traditional LQG controller resulted in an unstable system. They also tested the learning method in a real vehicle, but with a semi-active suspension system installed, and they showed promising experimental results.…”

Development of a Fuzzy Sky-hook Algorithm for a Semi-active ER Vehicle Suspension Using Inverse Model

“…In this paper, we obtained inverse model of ER damper using equation (2). The inverse model of ER damper can be expressed by …”