TY - GEN
T1 - An intelligent multiple-controller framework for the integrated control of autonomous vehicles
AU - Hussain, Amir
AU - Abdullah, Rudwan
AU - Yang, Erfu
AU - Gurney, Kevin
PY - 2012/8/27
Y1 - 2012/8/27
N2 - 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.
AB - 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.
KW - autonomous vehicle control
KW - fuzzy logic switching and tuning
KW - PID controller
KW - pole-zero placement controller
UR - http://www.scopus.com/inward/record.url?scp=84865203792&partnerID=8YFLogxK
UR - http://link.springer.com/
UR - http://www.conference123.org/bics2012/
U2 - 10.1007/978-3-642-31561-9_10
DO - 10.1007/978-3-642-31561-9_10
M3 - Conference contribution book
AN - SCOPUS:84865203792
SN - 9783642315602
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 92
EP - 101
BT - Advances in Brain Inspired Cognitive Systems
A2 - Zhang, Huaguang
A2 - Hussain, Amir
A2 - Liu, Derong
A2 - Wang, Zhanshan
CY - Berlin
T2 - 5th International Conference on Advances in Brain Inspired Cognitive Systems, BICS 2012
Y2 - 11 July 2012 through 14 July 2012
ER -