Neural network-based sliding mode control of electronic throttle

Authors:
Miroslav Barić;Ivan Petrović;Nedjeljko Perić
Affiliations:
Automatic Control Laboratory, Swiss Federal Institute of Technology, Zürich, Switzerland;Department of Control and Computer Engineering in Automation, Faculty of Electrical Engineering and Computing, University of Zagreb, Croatia;Department of Control and Computer Engineering in Automation, Faculty of Electrical Engineering and Computing, University of Zagreb, Croatia
Venue:
Engineering Applications of Artificial Intelligence
Year:
2005

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Abstract

A neural network-based sliding mode controller for an electronic throttle of an internal combustion engine is proposed. Electronic throttle is modeled as a linear system with uncertainties and affected by disturbances depending on the states of the system. The disturbances, consisting of an unknown friction and a torque caused by the dual spring mechanism inside the mechanical part of the throttle, are estimated by a neural network whose parameters are adapted on-line. The sliding mode controller and the parameters adaptation scheme are derived in order to achieve a tracking of a smooth reference signal, while preserving boundedness of all signals in the closed-loop system. Experimental results are presented which demonstrate the efficiency and robustness of the proposed control scheme.