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

Abdullah, R., Hussain, A., Warwick, K. and Zayed, A. (2008) Autonomous intelligent cruise control using a novel multiple-controller framework incorporating fuzzy-logic-based switching and tuning. Neurocomputing, 71 (13-15). pp. 2727-2741. ISSN 0925-2312 doi: 10.1016/j.neucom.2007.05.016

Abstract/Summary

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. Crown Copyright (C) 2008 Published by Elsevier B.V. All rights reserved.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/15092
Item Type	Article
Refereed	Yes
Divisions	Science
Uncontrolled Keywords	intelligent adaptive control, autonomous cruise control, neural, networks, PID control, zero-pole placement control, fuzzy switching, fuzzy tuning, ETC system, AUTOMATED VEHICLE CONTROL, DISCRETE-TIME-SYSTEMS, ADAPTIVE-CONTROL, PID, CONTROL, NETWORKS, MODEL, STABILITY, MANEUVERS
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