Decentralized event-triggered adaptive control of a class of uncertain interconnected nonlinear systems using local state feedback corrupted by unknown injection data

Abstract

This paper investigates a decentralized event-triggered adaptive control problem of uncertain interconnected lower-triangular nonlinear systems using corrupted local state feedback. The unknown injection data such as sensor faults or cyber attacks are assumed to be added to full state feedback measurements of each subsystem with unknown nonlinearities. Under this assumption, exactly measured local state variables are unknown during Lyapunov-stability-based recursive control design steps. The primary contributions of this study are (i) to develop a recursive adaptive control design strategy for achieving decentralization of unmatched interconnected nonlinearities using corrupted local state feedback information and (ii) to design a decentralized asynchronous event-triggering mechanism using the corrupted local state variables. To this end, local injection data compensators using neural networks and adaptive tuning laws are designed to compensate for unknown injection data and nonlinear interaction effects. It is shown that the proposed decentralized control system via the corrupted local state feedback ensures the practical stability of the total closed-loop system and the exclusion of Zeno behavior.