Title :
Decentralized stochastic adaptive control of dynamic non Gaussian systems
Author :
Filipovic, Vojislav Z.
Author_Institution :
CNTS, Loznica, Serbia
Abstract :
In this paper, the problem of stochastic adaptive control for a class of large-scale systems is addresed. As the locaal controllers is used a minimum variance controllers. The subsystems are described with single-input single-output ARMAX models. The stochastic disturbance is non-Gaussian and interconnections of subsystems are non linear functions. For estimation of unknown parameters of the local controllers projections stochastic approximation type algorithm is used. Nonlinear transformation of prediction error in algorithm is consequence of non-Gaussianity of disturbance. It is shown that the closed-loop system is globally stable and that the overall mean-square tracking error is bounded.
Keywords :
adaptive control; autoregressive moving average processes; closed loop systems; decentralised control; interconnected systems; mean square error methods; nonlinear functions; parameter estimation; stability; stochastic systems; closed-loop system; decentralized stochastic adaptive control; dynamic nonGaussian system; globally stable; large-scale system; local controller; mean-square tracking error; minimum variance controller; nonGaussian subsystem; nonlinear function; nonlinear transformation; prediction error; single-input single-output ARMAX model; stochastic approximation type algorithm; stochastic disturbance; subsystem interconnection; unknown parameter estimation; Adaptation models; Adaptive control; Approximation algorithms; Estimation; Large-scale systems; Prediction algorithms; Stochastic processes; Decentralized adaptive control; Global stability; Large-scale systems; Stochastic systems; non-Gaussian disturbance;
Conference_Titel :
Control Conference (ECC), 2001 European
Conference_Location :
Porto
Print_ISBN :
978-3-9524173-6-2