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An approach to tune fuzzy controllers based on reinforcement learning for autonomous vehicle control

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3 Author(s)
Xiaohui Dai ; Rockwell Autom. Res. Center, Shanghai, China ; Chi-Kwong Li ; Rad, A.B.

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.

Published in:

Intelligent Transportation Systems, IEEE Transactions on  (Volume:6 ,  Issue: 3 )

Date of Publication:

Sept. 2005

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