Decentralized Adaptive Robust Tracking and Model Following for Uncertain Large Scale Systems
Abstract
The problem of decentralized robust tracking and model following is considered for a class of uncertain large scale interconnected systems. In this paper, it is assumed that the upper bounds of the uncertainties and external disturbances are unknown. A modified adaptation law is introduced to estimate such unknown bounds, and on the basis of the updated values of these unknown bounds, a class of decentralized local state feedback controllers is constructed for robust tracking of dynamical signals. It is shown that the proposed decentralized local adaptive robust tracking controllers can guarantee that the tracking errors between each subsystem and the corresponding local reference model decrease uniformly asymptotically to zero.
