Self-tuning PID control: a multivariable derivation and application
Automatica (Journal of IFAC)
Intelligent multi-controller assessment using fuzzy logic
Fuzzy Sets and Systems - Special issue on neuro-fuzzy techniques and applications
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Fuzzy Control
Using Fuzzy Logic in Automated Vehicle Control
IEEE Intelligent Systems
EMS-Vision: a perceptual system for autonomous vehicles
IEEE Transactions on Intelligent Transportation Systems
Emergency vehicle maneuvers and control laws for automated highway systems
IEEE Transactions on Intelligent Transportation Systems
Adaptive fuzzy control for inter-vehicle gap keeping
IEEE Transactions on Intelligent Transportation Systems
Modeling of traffic flow of automated vehicles
IEEE Transactions on Intelligent Transportation Systems
On the stability of fuzzy systems
IEEE Transactions on Fuzzy Systems
Model-based iterative learning control with a quadratic criterion for time-varying linear systems
Automatica (Journal of IFAC)
Brief A common framework for anti-windup, bumpless transfer and reliable designs
Automatica (Journal of IFAC)
Effects of moving the center's in an RBF network
IEEE Transactions on Neural Networks
Robust and adaptive backstepping control for nonlinear systems using RBF neural networks
IEEE Transactions on Neural Networks
Gaussian networks for direct adaptive control
IEEE Transactions on Neural Networks
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part II
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
Combined quasi-static backward modeling and look-ahead fuzzy control of vehicles
Expert Systems with Applications: An International Journal
Review: Adaptive cruise control look-ahead system for energy management of vehicles
Expert Systems with Applications: An International Journal
BICS'13 Proceedings of the 6th international conference on Advances in Brain Inspired Cognitive Systems
Development of a target recognition and following system for a field robot
Computers and Electronics in Agriculture
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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.