Stabilizing predictive control of NARX models

Author

De Nicoiao, G. ; Magni, L. ; Scattolini, R.

Author_Institution

Dipt. di Inf. e Sist., Univ. di Pavia, Pavia, Italy

fYear

1997

fDate

1-7 July 1997

Firstpage

81

Lastpage

86

Abstract

In this paper a predictive control algorithm for nonlinear discrete-time systems is presented. Starting from a state-space model, conditions for the asymptotic tracking of constant reference signals in a neighbourhood of a given equilibrium are first derived. Then it is shown that the system under control can be locally described in terms of a suitable NARX (Nonlinear ARX) model, which can be identified by means of well established techniques. For the NARX model, a receding-horizon predictive control algorithm is proposed which guarantees local stability and robust asymptotic tracking in the neighbourhood of the equilibrium.

Keywords

autoregressive processes; discrete time systems; nonlinear control systems; predictive control; robust control; NARX models; constant reference signals; local stability; nonlinear ARX model; nonlinear discrete-time systems; predictive control stabilization; receding-horizon predictive control algorithm; robust asymptotic tracking; state-space model; Asymptotic stability; Closed loop systems; Nonlinear systems; Optimization; Predictive control; Stability analysis; integral control; nonlinear control; nonlinear systems; optimal control; predictive control;

fLanguage

English

Publisher

ieee

Conference_Titel

Control Conference (ECC), 1997 European

Conference_Location

Brussels

Print_ISBN

978-3-9524269-0-6

Type

conf

Filename

7081912