|Appears in Collections:||Computing Science and Mathematics Book Chapters and Sections|
|Title:||An intelligent multiple-controller framework for the integrated control of autonomous vehicles|
|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. http://link.springer.com/chapter/10.1007/978-3-642-31561-9_10#|
|Keywords:||Autonomous vehicle control|
pole-zero placement controller
fuzzy logic switching and tuning
|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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