A new neural network-based approach for self-tuning control of nonlinear SISO discrete-time systems

Authors:
Jose I. Canelon;Leang S. Shieh;Gangbing Song
Affiliations:
School of Electrical Engineering, Universidad del Zulia, Maracaibo 4005, Venezuela;Department of Electrical and Computer Engineering, University of Houston, Houston 77204-4005, USA;Department of Mechanical Engineering, University of Houston, Houston 77204-4005, USA
Venue:
International Journal of Systems Science
Year:
2010

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A new neural network-based approach for self-tuning control of nonlinear multi-input multi-output dynamic systems

ACC'09 Proceedings of the 2009 conference on American Control Conference

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Abstract

This article presents a new neural network-based approach for self-tuning control of nonlinear single-input single-output (SISO) discrete-time dynamic systems. According to the approach, a neural network ARMAX (NN-ARMAX) model of the system is identified and continuously updated, using an online training algorithm. Control design is accomplished by solving an optimal discrete-time linear quadratic tracking problem using an observer-type linear state-space Kalman innovation model, which is built from the parameters of a local linear version of the NN-ARMAX model. The state-feedback control law is implemented using the Kalman state, which is calculated without estimating the noise covariance properties. The proposed control approach is shown to be very effective and outperforms the self-tuning control approach based on a linear ARMAX model on two simulation examples.