Title: Neural control for a semi-active suspension of a half-vehicle model

Authors: M.J.L. Boada, B.L. Boada, B. Munoz, V. Diaz

Addresses: Department of Mechanical Engineering, Carlos III University, Avd. De la Universidad 30, 28911 Leganes (Madrid), Spain. ' Department of Mechanical Engineering, Carlos III University, Avd. De la Universidad 30, 28911 Leganes (Madrid), Spain. ' Department of Mechanical Engineering, Carlos III University, Avd. De la Universidad 30, 28911 Leganes (Madrid), Spain. ' Department of Mechanical Engineering, Carlos III University, Avd. De la Universidad 30, 28911 Leganes (Madrid), Spain

Abstract: This paper presents a reinforcement learning algorithm using neural networks which allows a vehicle with semi-active suspension to improve continuously not only the ride comfort but also the tyre/ground contact. The proposed controller learns online, so that the system can adapt to changes produced in the environment. The neural controller has been studied using a half-vehicle model. Different road profiles have been tested to prove the robustness and reliability of the proposed semi-active suspension system. Simulation results show the effectiveness of our algorithm.

Keywords: continuous reinforcement learning; half-vehicle model; neural networks; semi-active suspension vehicle; ride comfort; tyre-ground contact; simulation; vehicle suspension; autonomous vehicle systems.

DOI: 10.1504/IJVAS.2005.008250

International Journal of Vehicle Autonomous Systems, 2005 Vol.3 No.2/3/4, pp.306 - 329

Available online: 25 Nov 2005 *

Full-text access for editors Access for subscribers Purchase this article Comment on this article