Centralized moving‐horizon estimation for a class of nonlinear dynamical complex networks under event‐triggered transmission scheme

Abstract

AbstractThis article is concerned with the problem of event‐triggered centralized moving‐horizon state estimation for a class of nonlinear dynamical complex networks. An event‐triggered scheme is employed to reduce unnecessary data transmissions between sensors and estimators, where the signal is transmitted only when certain condition is violated. By treating sector‐bounded nonlinearities as certain sector‐bounded uncertainties, the addressed centralized moving‐horizon estimation problem is transformed into a regularized robust least‐squares problem that can be effectively solved via existing convex optimization algorithms. Moreover, a sufficient condition is derived to guarantee the exponentially ultimate boundedness of the estimation error, and an upper bound of the estimation error is also presented. Finally, a numerical example is provided to demonstrate the feasibility and efficiency of the proposed estimator design method.