Equality-constrained state estimation for hybrid systems
Carregando...
Data
Título da Revista
ISSN da Revista
Título de Volume
Editor
Universidade Federal de Minas Gerais
Descrição
Tipo
Artigo de periódico
Título alternativo
Primeiro orientador
Membros da banca
Resumo
The authors investigate the problem of state estimation for stochastic hybrid dynamical systems with state equality constraints. They divided this problem into three categories depending on the linearity or non-linearity of equality constraints as well as on the dependence of constraints on the operating mode. For the mode-independent equality-constrained linear case, they present sufficient conditions on process dynamics and filter initial conditions so that the classical interacting multiple model (IMM) algorithm yields state estimates satisfying the linear equality constraint for all subsequent times. For both linear and non-linear systems, the mode-dependent equality constraints must be enforced over time by the filter. They present a modified version of the IMM filter to enforce the equality constraints in such cases. Their numerical results show that the proposed methods provide more accurate estimates than the IMM unconstrained estimates.
Abstract
Assunto
Kalman, Filtragem de, Sistemas dinâmicos diferenciais
Palavras-chave
The Kalman filter (KF) provides optimal state estimates for linear and Gaussian systems. However, additional information about the system in the form of state constraints may be useful for improving the state estimates. Many applications, dynamical systems satisfy constraints that arise from physical laws, mathematical properties or geometric considerations. One example is the task of tracking a ground vehicle whose velocity is constrained by road orientation.
Citação
Departamento
Curso
Endereço externo
https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/iet-cta.2018.6374