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Authors: Simone Tognetti 1 ; Marcello Restelli 1 ; Sergio M. Savaresi 1 and Cristiano Spelta 2

Affiliations: 1 Politecnico di Milano, Italy ; 2 Universit degli Studi di Bergamo, Italy

ISBN: 978-989-8111-99-9

Keyword(s): Batch-reinforcement learning, Control theory, Non linear optimal control, Semi-active suspension.

Related Ontology Subjects/Areas/Topics: Informatics in Control, Automation and Robotics ; Intelligent Control Systems and Optimization ; Machine Learning in Control Applications ; Robotics and Automation ; Vehicle Control Applications

Abstract: The design problem of optimal comfort-oriented semi-active suspension has been addressed with different standard techniques which failed to come out with an optimal strategy because the system is hard non-linear and the solution is too complex to be found analytically. In this work, we aimed at solving such complex problem by applying Batch Reinforcement Learning (BRL), that is an artificial intelligence technique that approximates the solution of optimal control problems without knowing the system dynamics. Recently, a quasi optimal strategy for semi-active suspension has been designed and proposed: the Mixed SH-ADD algorithm, which the strategy designed in this paper is compared to. We show that an accurately tuned BRL provides a policy able to guarantee the overall best performance.

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Paper citation in several formats:
Tognetti S.; Restelli M.; M. Savaresi S.; Spelta C. and (2009). BATCH REINFORCEMENT LEARNING - An Application to a Controllable Semi-active Suspension System.In Proceedings of the 6th International Conference on Informatics in Control, Automation and Robotics - Volume 3: ICINCO, ISBN 978-989-8111-99-9, pages 228-233. DOI: 10.5220/0002210302280233

@conference{icinco09,
author={Simone Tognetti and Marcello Restelli and Sergio {M. Savaresi} and Cristiano Spelta},
title={BATCH REINFORCEMENT LEARNING - An Application to a Controllable Semi-active Suspension System},
booktitle={Proceedings of the 6th International Conference on Informatics in Control, Automation and Robotics - Volume 3: ICINCO,},
year={2009},
pages={228-233},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002210302280233},
isbn={978-989-8111-99-9},
}

TY - CONF

JO - Proceedings of the 6th International Conference on Informatics in Control, Automation and Robotics - Volume 3: ICINCO,
TI - BATCH REINFORCEMENT LEARNING - An Application to a Controllable Semi-active Suspension System
SN - 978-989-8111-99-9
AU - Tognetti, S.
AU - Restelli, M.
AU - M. Savaresi, S.
AU - Spelta, C.
PY - 2009
SP - 228
EP - 233
DO - 10.5220/0002210302280233

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