Nonlinear Set Membership Filter with State Estimation Constraints via Consensus-ADMM

TL;DR

This paper introduces a novel set membership filtering approach for nonlinear systems with constraints, using a consensus-ADMM method and semi-infinite programming to improve estimation accuracy without linearization.

Contribution

It proposes a new nonlinear set membership filter that employs a semi-infinite programming approach and consensus-ADMM to handle nonlinearities and constraints effectively.

Findings

The proposed filter accurately estimates states in nonlinear systems.

The ADMM-based algorithm efficiently solves the constrained estimation problem.

Numerical examples demonstrate the filter's effectiveness.

Abstract

This paper considers the state estimation problem for nonlinear dynamic systems with unknown but bounded noises. Set membership filter (SMF) is a popular algorithm to solve this problem. In the set membership setting, we investigate the filter problem where the state estimation requires to be constrained by a linear or nonlinear equality. We propose a consensus alternating direction method of multipliers (ADMM) based SMF algorithm for nonlinear dynamic systems. To deal with the difficulty of nonlinearity, instead of linearizing the nonlinear system, a semi-infinite programming (SIP) approach is used to transform the nonlinear system into a linear one, which allows us to obtain a more accurate estimation ellipsoid. For the solution of the SIP, an ADMM algorithm is proposed to handle the state estimation constraints, and each iteration of the algorithm can be solved efficiently. Finally, the proposed filter is applied to typical numerical examples to demonstrate its effectiveness.