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

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.

Keywords:

• self-tuning control
• neural networks
• optimal control
• nonlinear discrete-time systems

Acknowledgements

This study was supported by the US Army Research Office under grant no. W911NF-06-1-0507 and the National Science Foundation under grant no. NSF 0717860.