Block recursive parallelotopic bounding in set membership identification

Abstract

In this paper, a procedure for the recursive approximation of the feasible parameter set of a linear model with a set membership uncertainty description is provided. Approximating regions of parallelotopic shape are considered. The new contribution of this paper consists in devising a general procedure allowing for block processing of q > 1 measurements at each recursion step. Based on this, several approximation strategies for polytopes are presented. Simulation experiments are performed, showing the effectiveness of the algorithm as compared to the original algorithm processing one measurement at each step

Theoretical and experimental study of uncertain set based moving target localization using multiple robots

“…Of course, some other uncertain set can also be used, for example orthotopic and paralleotopic [10][11][12] , but in this paper, we will mainly discuss the ESMF algorithm, and in this sub-section, we will introduce the basic procedure of the ESMF algorithm.…”

Comparing different approaches to model error modeling in robust identification

“…For example, since for ∞ -bounded noise F ES is a polytope in Rn, it can be recursively approximated by simpler regions, like ellipsoids or parallelotopes [5,4], and the center of these approximating sets may be chosen as an estimate ofθ * . More sophisticated set approximation strategies, based on inner and outer bounding via polytopes, have been recently proposed in [22].…”

On Model Error Modeling in Set Membership Identification

“…These may include recursive approximations of the set F SS (employing different approximating regions like ellipsoids, orthotopes, parallelotopes, see e.g. [6,8,18]), and/or suboptimal pointwise estimators like projection algorithms, interpolatory algorithms, etc. The evaluation of bounds on the identification error of these algorithms has been tackled in different contexts and several results are now available (see e.g.…”