Set-Membership Estimation for Nonlinear 2-D Systems With Missing Measurements

Abstract

This brief addresses the set-membership estimation issue for two-dimensional nonlinear systems with missing measurements and unknown-but-bounded disturbances. The phenomenon of missing measurements is modeled by a deterministic approach with values of 0 and 1. The considered nonlinearities are treated by Taylor series expansion, in which the truncation errors are transformed subtly into norm-bounded parameter uncertainties. A set-membership estimator is established, which ensures that the real system state is included in a certain ellipsoidal region of the estimation state at each step. Then, to identify the local best estimation performance, an optimization algorithm is devised aiming at ellipsoidal minimization (in the sense of matrix trace). The numerical results further confirm the efficacy of the proposed algorithm.