Active roll control using reinforcement learning for a single unit heavy vehicle Online publication date: Wed, 22-Jul-2009
by Maria Jesus Lopez Boada, Beatriz Lopez Boada, Antonio Gauchia Babe, Jose Antonio Calvo Ramos, Vicente Diaz Lopez
International Journal of Heavy Vehicle Systems (IJHVS), Vol. 16, No. 4, 2009
Abstract: In this paper, a reinforcement learning algorithm using neural networks to improve the roll stability in a single unit heavy vehicle is proposed. The controller, consisting of active anti-roll bars, provides the adequate roll moments to the vehicle to improve its performance. The main advantages of the implemented neuro control are its good performance to control non-linear systems, it is a free-model control and it learns online, so that the system can adapt to changes produced in the environment. In this case, it is only necessary to measure a unique variable (the sprung mass roll angle) to control the vehicle so both the number of sensors and vehicle cost are reduced. Simulation results show the effectiveness of the proposed control system during different manoeuvres such as J-turn and lane-change.
Online publication date: Wed, 22-Jul-2009
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