Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/22073
Title: An approach to tune fuzzy controllers based on reinforcement learning for autonomous vehicle control
Authors: Dai, X
Li, CK
Rad, AB
Keywords: Autonomous vehicles
Fuzzy controllers
Longitudinal control
Reinforcement learning
Issue Date: 2005
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on intelligent transportation systems, 2005, v. 6, no. 3, p. 285-293 How to cite?
Journal: IEEE transactions on intelligent transportation systems 
Abstract: In this paper, we suggest a new approach for tuning parameters of fuzzy controllers based on reinforcement learning. The architecture of the proposed approach is comprised of a Q estimator network (QEN) and a Takagi-Sugeno-type fuzzy inference system (TSK-FIS). Unlike other fuzzy Q-learning approaches that select an optimal action based on finite discrete actions, the proposed controller obtains the control output directly from TSK-FIS. With the proposed architecture, the learning algorithms for all the parameters of the QEN and the FIS are developed based on the temporal-difference (TD) methods as well as the gradient-descent algorithm. The performance of the proposed design technique is illustrated by simulation studies of a vehicle longitudinal-control system.
URI: http://hdl.handle.net/10397/22073
ISSN: 1524-9050
EISSN: 1558-0016
DOI: 10.1109/TITS.2005.853698
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