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

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4 Author(s)
Jose I. Canelon ; School of Electrical Engineering, Universidad del Zulia, Maracaibo, 4005, Venezuela ; Leang S. Shieh ; Yongpeng Zhang ; Cajetan M. Akujuobi

This paper presents a new neural network-based approach for self-tuning control of nonlinear MIMO 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 observable block companion form 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 estimated state, which is calculated without estimating the noise covariance properties. The effectiveness of the proposed control approach is illustrated using a simulation example.

Published in:

2009 American Control Conference

Date of Conference:

10-12 June 2009