Inverse optimal filtering of linear distributed parameter systems

A constructive method is developed to design inverse optimal filters to estimate the states of a class of linear distributed parameter systems (DPSs) based on the calculus of variation approach. Inverse optimality guarantees that the cost functional to be minimized is meaningful in the sense that th...

Full description

Bibliographic Details
Main Author: Do, Khac Duc
Format: Journal Article
Published: HIKARI Ltd 2013
Subjects:
Online Access:http://hdl.handle.net/20.500.11937/36433
_version_ 1848754769163714560
author Do, Khac Duc
author_facet Do, Khac Duc
author_sort Do, Khac Duc
building Curtin Institutional Repository
collection Online Access
description A constructive method is developed to design inverse optimal filters to estimate the states of a class of linear distributed parameter systems (DPSs) based on the calculus of variation approach. Inverse optimality guarantees that the cost functional to be minimized is meaningful in the sense that the symmetric and positive definite weighting kernel matrix on the states is chosen after the filter design instead of being specified at the start of the filter design. Inverse optimal design enables that the Riccati nonlinear partial differential equation (PDE) can be simplified to a Bernoulli PDE, which can be solved analytically. The filter design is based on a new Green matrix formula, a new unique and bounded solution of a linear PDE, and analytical solution of a Bernoulli PDE. The inverse optimal filter design is first developed for the case where the measurements are spatially available, then is extended to the practical case where only a finite number of measurements is available.
first_indexed 2025-11-14T08:45:40Z
format Journal Article
id curtin-20.500.11937-36433
institution Curtin University Malaysia
institution_category Local University
last_indexed 2025-11-14T08:45:40Z
publishDate 2013
publisher HIKARI Ltd
recordtype eprints
repository_type Digital Repository
spelling curtin-20.500.11937-364332017-10-02T02:28:10Z Inverse optimal filtering of linear distributed parameter systems Do, Khac Duc Distributed parameter systems Inverse optimal filter Riccati PDE Bernoulli PDE A constructive method is developed to design inverse optimal filters to estimate the states of a class of linear distributed parameter systems (DPSs) based on the calculus of variation approach. Inverse optimality guarantees that the cost functional to be minimized is meaningful in the sense that the symmetric and positive definite weighting kernel matrix on the states is chosen after the filter design instead of being specified at the start of the filter design. Inverse optimal design enables that the Riccati nonlinear partial differential equation (PDE) can be simplified to a Bernoulli PDE, which can be solved analytically. The filter design is based on a new Green matrix formula, a new unique and bounded solution of a linear PDE, and analytical solution of a Bernoulli PDE. The inverse optimal filter design is first developed for the case where the measurements are spatially available, then is extended to the practical case where only a finite number of measurements is available. 2013 Journal Article http://hdl.handle.net/20.500.11937/36433 10.12988/ams.2013.37361 HIKARI Ltd fulltext
spellingShingle Distributed parameter systems
Inverse optimal filter
Riccati PDE
Bernoulli PDE
Do, Khac Duc
Inverse optimal filtering of linear distributed parameter systems
title Inverse optimal filtering of linear distributed parameter systems
title_full Inverse optimal filtering of linear distributed parameter systems
title_fullStr Inverse optimal filtering of linear distributed parameter systems
title_full_unstemmed Inverse optimal filtering of linear distributed parameter systems
title_short Inverse optimal filtering of linear distributed parameter systems
title_sort inverse optimal filtering of linear distributed parameter systems
topic Distributed parameter systems
Inverse optimal filter
Riccati PDE
Bernoulli PDE
url http://hdl.handle.net/20.500.11937/36433