Authors: Janaína R. Amaral; Thiago A. Fiorentin; Harald Göllinger
Addresses: Pós-Graduação em Engenharia e Ciências Mecânicas – Universidade Federal de Santa Catarina (UFSC), Joinville, SC, 89219-600, Brazil ' Pós-Graduação em Engenharia e Ciências Mecânicas – Universidade Federal de Santa Catarina (UFSC), Joinville, SC, 89219-600, Brazil ' Department of Mechanical Engineering – Technische Hochschule Ingolstadt, Ingolstadt, 85049, Germany
Abstract: This paper presents a study on the use of reinforcement learning to control the torque vectoring of a small electric racecar aiming to improve vehicle handling and vehicle stability. The reinforcement-learning algorithm used is Neural Fitted Q Iteration, and the sampling of experiences is based on simulations using the software CarMaker. The cost function is based on the position of the states on the phase-plane of sideslip angle and angular velocity. To investigate the maximum ratio of torque distribution that should be set to guarantee stability as well as the effectiveness of the controller inputs in the learning process, two experiments were done (A and B) with different states and possibilities of torque distribution. The controller A is able to improve the vehicle handling and stability with a significant reduction in vehicle sideslip angle. And it also showed that 70% of torque distribution is enough to keep the vehicle stable.
Keywords: torque vectoring; neural fitted Q iteration; vehicle stability; vehicle handling; CarMaker; sideslip angle.
International Journal of Vehicle Systems Modelling and Testing, 2020 Vol.14 No.2/3, pp.97 - 112
Received: 03 Sep 2019
Accepted: 04 Nov 2019
Published online: 06 Dec 2020 *