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Title: An intelligent multiple-controller framework for the integrated control of autonomous vehicles
Author(s): Hussain, Amir
Abdullah, Rudwan
Yang, Erfu
Gurney, Kevin
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Editor(s): Zhang, H
Hussain, A
Liu, D
Wang, Z
Sponsor: The Royal Society of Edinburgh
Citation: Hussain A, Abdullah R, Yang E & Gurney K (2012) An intelligent multiple-controller framework for the integrated control of autonomous vehicles. In: Zhang H, Hussain A, Liu D & Wang Z (eds.) Advances in Brain Inspired Cognitive Systems: 5th International Conference, BICS 2012, Shenyang, China, July 11-14, 2012. Proceedings. Lecture Notes in Computer Science, 7366. Berlin Heidelberg: Springer, pp. 92-101.
Keywords: Autonomous vehicle control
PID controller
pole-zero placement controller
fuzzy logic switching and tuning
Issue Date: 2012
Date Deposited: 8-Aug-2013
Series/Report no.: Lecture Notes in Computer Science, 7366
Abstract: This paper presents an intelligent multiple-controller framework for the integrated control of throttle, brake and steering subsystems of realistic validated nonlinear autonomous vehicles. In the developed multiple-controller framework, a fuzzy logic-based switching and tuning supervisor operates at the highest level of the system and makes a switching decision on the basis of the required performance measure, between an arbitrary number of adaptive controllers: in the current case, between a conventional Proportional-Integral-Derivative (PID) controller and a PID structure-based pole-zero placement controller. The fuzzy supervisor is also able to adaptively tune the parameters of the multiple controllers. Sample simulation results using a realistic autonomous vehicle model demonstrate the ability of the intelligent controller to both simultaneously track the desired throttle, braking force, and steering changes, whilst penalising excessive control actions - with significant potential implications for both fuel and emission economy. We conclude by demonstrating how this work has laid the foundation for ongoing neuro-biologically motivated algorithmic development of a more cognitively inspired multiple-controller framework.
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