Nonparametric identification of controlled nonlinear time varying processes

Author

Hilgert, Nadine ; Senoussi, Rachid ; Vila, Jean-Pierre

Author_Institution

Lab. d´´Analyse des Systemes et de Biometrie, INRA- ENSAM, Montpellier, France

Volume

3

fYear

2001

fDate

2001

Firstpage

2544

Abstract

We are interested in the identification of an unknown time-varying additive component of a controlled nonlinear autoregressive stochastic model, which is a problem of interest in the modeling and control of uncertain systems, such as those met in biotechnological processes. A kernel-based nonparametric estimator is proposed whose almost sure convergence is studied by means of a Lyapunov stabilizability assumption and laws of large numbers for martingales. We then adapt the general result to several classes of deterministic or random functional model uncertainties

Keywords

Lyapunov methods; autoregressive processes; biotechnology; convergence; identification; nonlinear control systems; process control; random functions; stability; stochastic systems; uncertain systems; Lyapunov stabilizability; biotechnological processes; controlled nonlinear autoregressive stochastic model; controlled nonlinear time-varying processes; convergence; deterministic functional model uncertainties; kernel-based nonparametric estimator; large numbers; martingales; nonparametric identification; random functional model uncertainties; uncertain systems control; uncertain systems modeling; unknown time-varying additive component; Adaptive control; Biomass; Convergence; Filtration; Kernel; Noise measurement; Process control; Random variables; Stochastic processes; Uncertainty;

fLanguage

English

Publisher

ieee

Conference_Titel

Decision and Control, 2001. Proceedings of the 40th IEEE Conference on

Conference_Location

Orlando, FL

Print_ISBN

0-7803-7061-9

Type

conf

DOI

10.1109/.2001.980647

Filename

980647