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

Hunmin Kim; Pinyao Guo; Minghui Zhu; Peng Liu

doi:10.1016/j.automatica.2019.108588

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 journal › Article › peer-review

14 Scopus citations

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 language	English (US)
Article number	108588
Journal	Automatica
Volume	111
DOIs	https://doi.org/10.1016/j.automatica.2019.108588
State	Published - Jan 2020

All Science Journal Classification (ASJC) codes

Control and Systems Engineering
Electrical and Electronic Engineering

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10.1016/j.automatica.2019.108588

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title = "Simultaneous input and state estimation for stochastic nonlinear systems with additive unknown inputs",

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.",

author = "Hunmin Kim and Pinyao Guo and Minghui Zhu and Peng Liu",

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year = "2020",

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AU - Guo, Pinyao

AU - Zhu, Minghui

AU - Liu, Peng

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N2 - 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.

AB - 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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VL - 111

JO - Automatica

JF - Automatica

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

Abstract

All Science Journal Classification (ASJC) codes

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