Robust Distributed Consensus-Based Filtering for Uncertain Systems over Sensor Networks

Autor(es)

Rocha, Kaio D. T. ; Terra, Marco H.

• Tipo de documento: Artigo Científico
• Fonte: IFAC PAPERSONLINE; v. 53, n. 2, p. 6-pg., 2020-01-01.

Resumo

Distributed consensus-based estimation is one of the main applications of sensor networks. Most approaches are highly dependent on exact model knowledge. This limitation motivated the development of robust distributed filters that deal with model uncertainties. Many of these works, however, are not fully distributed filters or demand high communication and computational efforts. In this paper, we propose a robust distributed consensus-based filter for uncertain discrete-time linear systems. We assume norm-bounded parametric uncertainties in all matrices of both the target system and sensing models. The approach consists of adopting a purely deterministic interpretation of the robust distributed estimation problem, formulated by combining the penalty function method and the robust regularized least-squares estimation problem. The filter is presented in a fully distributed Kalman-like structure that is suitable for online applications, requiring acceptable computational and communication efforts. We evaluate the effectiveness of the proposed filter by comparing its performance with an existing robust distributed filter, as well as with a centralized strategy. Copyright (C) 2020 The Authors. (AU)

• Processo FAPESP: 17/16346-4 - Controle tolerante a falhas de rede de comunicação para o movimento coordenado de robôs heterogêneos
• Beneficiário: Kaio Douglas Teófilo Rocha
• Modalidade de apoio: Bolsas no Brasil - Doutorado Direto
• Processo FAPESP: 14/50851-0 - INCT 2014: Instituto Nacional de Ciência e Tecnologia para Sistemas Autônomos Cooperativos Aplicados em Segurança e Meio Ambiente
• Beneficiário: Marco Henrique Terra
• Modalidade de apoio: Auxílio à Pesquisa - Temático