Set-Membership Filter for Discrete-Time Nonlinear Systems Using State-Dependent Coefficient Parameterization

Abstract

In this article, a recursive set-membership filtering algorithm for discrete-time nonlinear dynamical systems subject to unknown but bounded process and measurement noises is proposed. The nonlinear dynamics are represented in a pseudolinear form using the state-dependent coefficient (SDC) parameterization. Matrix Taylor expansions are utilized to expand the state-dependent matrices about the state estimates. Upper bounds on the norms of remainders in the matrix Taylor expansions are calculated online using a nonadaptive random search algorithm at each time step. Utilizing these upper bounds and the ellipsoidal set description of the uncertainties, a two-step filter is derived that utilizes the “correction–prediction” structure of the standard Kalman filter variants. At each time step, correction and prediction ellipsoids are constructed that contain the true state of the system by solving the corresponding semidefinite programs. Finally, a simulation example is included to illustrate the effectiveness of the proposed approach.