Set-membership filtering for automatic guided vehicles with unknown-but-bounded noises

Abstract

This paper is concerned with the state estimation problem for an automatic guided vehicle (AGV). A novel set-membership filtering (SMF) scheme is presented to solve the state estimation problem in the trajectory tracking process of the AGV under the unknown-but-bounded (UBB) process and measurement noises. Different from some existing traditional filtering methods, such as Kalman filtering method and [Formula: see text] filtering method, the proposed SMF scheme is developed to provide state estimation sets rather than state estimation points for the system states to effectively deal with UBB noises and reduce the requirement of the sensor precision. Then, in order to obtain the state estimation ellipsoids containing the true states, a set-membership estimation algorithm is designed based on the AGV physical model and S-procedure technique. Finally, comparison examples are presented to illustrate the effectiveness of the proposed SMF scheme for an AGV state estimation problem in the present of the UBB noises.

Distributed State Fusion Estimation of Multi-Source Localization Nonlinear Systems

“…An existing filtering method for UAD noise disturbances in the system is the set-membership filtering (SMF) strategy [ 16 ]. As the noise characteristics are characterized by a bounded range, the SMF works by finding a minimum estimated ellipsoid to contain the possible range of the target’s state [ 17 ]. However, it is difficult to apply the SMF to nonlinear systems with UAD noise.…”

Invariant Ellipsoids Method for Chaos Synchronization in a Class of Chaotic Systems

“…This paper proposes a static state-feedback control for master-slave synchronization designed by invariant ellipsoids techniques. The idea initially established in [23,24,25] and then further investigated over the last decades (see [26], [27] and the references therein) is based on the analysis of reachable and feasible sets for uncertain dynamic models or on the search for their approximations by simple convex domains like boxes, polyhedra or ellipsoids [28].…”