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Designing active vehicle suspension system using critic-based control strategy

Designing active vehicle suspension system using critic-based control strategy Abstract In this paper, an adaptive critic-based neurofuzzy controller is presented for a 2 DOF active vehicle suspension system with a servo hydraulic actuator. Fuzzy critic-based learning is a reinforcement learning method based on dynamic programming. The only information available for the critic agent is the system feedback, which is interpreted as the last action performed by the controller in the previous state. The signal produced by the critic agent is used alongside the algorithm of error back propagation to tune online conclusion parts of the fuzzy inference rules of the adaptive controller. Simulation results demonstrate the superior performance of this control method in terms of well disturbance rejection, improved ride comfort, robustness to model uncertainty and lower controller cost. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Nonlinear Engineering de Gruyter

Designing active vehicle suspension system using critic-based control strategy

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Publisher
de Gruyter
Copyright
Copyright © 2015 by the
ISSN
2192-8010
eISSN
2192-8029
DOI
10.1515/nleng-2015-0004
Publisher site
See Article on Publisher Site

Abstract

Abstract In this paper, an adaptive critic-based neurofuzzy controller is presented for a 2 DOF active vehicle suspension system with a servo hydraulic actuator. Fuzzy critic-based learning is a reinforcement learning method based on dynamic programming. The only information available for the critic agent is the system feedback, which is interpreted as the last action performed by the controller in the previous state. The signal produced by the critic agent is used alongside the algorithm of error back propagation to tune online conclusion parts of the fuzzy inference rules of the adaptive controller. Simulation results demonstrate the superior performance of this control method in terms of well disturbance rejection, improved ride comfort, robustness to model uncertainty and lower controller cost.

Journal

Nonlinear Engineeringde Gruyter

Published: Sep 1, 2015

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