Simultaneous input and state estimation for stochastic nonlinear systems with additive unknown inputs

Hunmin Kim, Pinyao Guo, Minghui Zhu, Peng Liu

Research output: Contribution to journalArticle

Abstract

This paper investigates simultaneous input and state estimation for a class of nonlinear stochastic systems. We propose a recursive filter to concurrently estimate system states and unknown inputs. We show that the estimation errors of the proposed filter are Practically Exponentially Stable in probability, and the estimation error covariance matrices are uniformly bounded.

Original languageEnglish (US)
Article number108588
JournalAutomatica
Volume111
DOIs
StatePublished - Jan 1 2020

Fingerprint

State estimation
Error analysis
Nonlinear systems
Stochastic systems
Covariance matrix

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Cite this

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abstract = "This paper investigates simultaneous input and state estimation for a class of nonlinear stochastic systems. We propose a recursive filter to concurrently estimate system states and unknown inputs. We show that the estimation errors of the proposed filter are Practically Exponentially Stable in probability, and the estimation error covariance matrices are uniformly bounded.",
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Simultaneous input and state estimation for stochastic nonlinear systems with additive unknown inputs. / Kim, Hunmin; Guo, Pinyao; Zhu, Minghui; Liu, Peng.

In: Automatica, Vol. 111, 108588, 01.01.2020.

Research output: Contribution to journalArticle

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