Adaptive control of discrete-time systems using multiple models

Author

Narendra, Kumpati S. ; Xiang, Cheng

Author_Institution

Center for Syst. Sci., Yale Univ., New Haven, CT, USA

Volume

45

Issue

9

fYear

2000

fDate

9/1/2000 12:00:00 AM

Firstpage

1669

Lastpage

1686

Abstract

The adaptive control of a linear time-invariant discrete-time system using multiple models is considered in this paper. Both the deterministic (noise free) case and the stochastic case when random disturbances are present are discussed. Based on the prediction errors of a finite number of fixed and adaptive identification models, a procedure is outlined for switching between a finite number of controllers to improve performance. The principal contributions of the paper are the proof of global stability of the overall system and the convergence of the tracking error signal to zero in the deterministic case and the proof of convergence of the minimum variance control problem. Computer simulation results are included to complement the theoretical results

Keywords

adaptive control; convergence; discrete time systems; linear systems; stability; tracking; adaptive control; adaptive identification models; deterministic system; fixed identification models; global stability; linear time-invariant discrete-time system; minimum variance control problem convergence; multiple models; noise-free system; prediction errors; random disturbances; stochastic system; tracking error signal convergence; Adaptive control; Computer errors; Computer simulation; Control systems; Convergence; Error correction; Predictive models; Programmable control; Stability; Stochastic resonance;

fLanguage

English

Journal_Title

Automatic Control, IEEE Transactions on

Publisher

ieee

ISSN

0018-9286

Type

jour

DOI

10.1109/9.880617

Filename

880617