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Autonomous intelligent cruise control using a novel multiple-controller framework incorporating fuzzy-logic-based switching and tuning

Abdullah, R.; Hussain, A.; Warwick, K.; Zayed, A.


R. Abdullah

K. Warwick

A. Zayed


This paper presents a novel intelligent multiple-controller framework incorporating a fuzzy-logic-based switching and tuning supervisor along with a generalised learning model (GLM) for an autonomous cruise control application. The proposed methodology combines the benefits of a conventional proportional-integral-derivative (PID) controller, and a PID structure-based (simultaneous) zero and pole placement controller. The switching decision between the two nonlinear fixed structure controllers is made on the basis of the required performance measure using a fuzzy-logic-based supervisor, operating at the highest level of the system. The supervisor is also employed to adaptively tune the parameters of the multiple controllers in order to achieve the desired closed-loop system performance. The intelligent multiple-controller framework is applied to the autonomous cruise control problem in order to maintain a desired vehicle speed by controlling the throttle plate angle in an electronic throttle control (ETC) system. Sample simulation results using a validated nonlinear vehicle model are used to demonstrate the effectiveness of the multiple-controller with respect to adaptively tracking the desired vehicle speed changes and achieving the desired speed of response, whilst penalising excessive control action.

Journal Article Type Article
Online Publication Date May 16, 2008
Publication Date 2008-08
Deposit Date Oct 17, 2019
Print ISSN 0925-2312
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 71
Issue 13-15
Pages 2727-2741
Keywords Intelligent adaptive control, Autonomous cruise control, Neural networks, PID control, Zero–pole placement control, Fuzzy switching, Fuzzy tuning, ETC system
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