Input and state estimation for linear systems: A least squares estimation approach

Shuwen Pan; Hongye Su; Hong Wang; Jian Chu; Renquan Lu

Input and state estimation for linear systems: A least squares estimation approach

Shuwen Pan, Hongye Su, Hong Wang, Jian Chu, Renquan Lu

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

Abstract

The problem of joint input and state estimation is addressed in this paper for linear discrete-time stochastic systems without direct feedthrough from unknown inputs to outputs. With the weighted least squares estimation for an extended state vector including unknown inputs and states, a recursive filter approach referred to as Kalman filter with unknown inputs without direct feedthrough (KF-UI-WDF) is derived. It is shown that the proposed KF-UI-WDF approach is uniquely optimal in sense of both least-squares (LS) and minimum-variances unbiased (MVU) over a category of MVU filters (e.g., [4], [5], [10]). The global optimality of the proposed KF-UI-WDF approach is also discussed. Due to the limited space, an illustrative example is omitted. ©2009 ACA.

Original language	English
Title of host publication	Proceedings of 2009 7th Asian Control Conference, ASCC 2009\|Proc. Asian Control Conf., ASCC
Pages	378-383
Number of pages	5
Publication status	Published - 2009
Event	2009 7th Asian Control Conference, ASCC 2009 - Hong Kong Duration: 1 Jul 2009 → … http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5276313&isnumber=5276086

Conference

Conference	2009 7th Asian Control Conference, ASCC 2009
City	Hong Kong
Period	1/07/09 → …
Internet address	http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5276313&isnumber=5276086

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@inproceedings{fcefdb3e012e474f915f008a33612595,

title = "Input and state estimation for linear systems: A least squares estimation approach",

abstract = "The problem of joint input and state estimation is addressed in this paper for linear discrete-time stochastic systems without direct feedthrough from unknown inputs to outputs. With the weighted least squares estimation for an extended state vector including unknown inputs and states, a recursive filter approach referred to as Kalman filter with unknown inputs without direct feedthrough (KF-UI-WDF) is derived. It is shown that the proposed KF-UI-WDF approach is uniquely optimal in sense of both least-squares (LS) and minimum-variances unbiased (MVU) over a category of MVU filters (e.g., [4], [5], [10]). The global optimality of the proposed KF-UI-WDF approach is also discussed. Due to the limited space, an illustrative example is omitted. {\textcopyright}2009 ACA.",

author = "Shuwen Pan and Hongye Su and Hong Wang and Jian Chu and Renquan Lu",

year = "2009",

language = "English",

isbn = "9788995605691",

pages = "378--383",

booktitle = "Proceedings of 2009 7th Asian Control Conference, ASCC 2009|Proc. Asian Control Conf., ASCC",

note = "2009 7th Asian Control Conference, ASCC 2009 ; Conference date: 01-07-2009",

url = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5276313&isnumber=5276086",

}

Pan, S, Su, H, Wang, H, Chu, J & Lu, R 2009, Input and state estimation for linear systems: A least squares estimation approach. in Proceedings of 2009 7th Asian Control Conference, ASCC 2009|Proc. Asian Control Conf., ASCC. pp. 378-383, 2009 7th Asian Control Conference, ASCC 2009, Hong Kong, 1/07/09. <http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5276313&isnumber=5276086>

TY - GEN

T1 - Input and state estimation for linear systems: A least squares estimation approach

AU - Pan, Shuwen

AU - Su, Hongye

AU - Wang, Hong

AU - Chu, Jian

AU - Lu, Renquan

PY - 2009

Y1 - 2009

N2 - The problem of joint input and state estimation is addressed in this paper for linear discrete-time stochastic systems without direct feedthrough from unknown inputs to outputs. With the weighted least squares estimation for an extended state vector including unknown inputs and states, a recursive filter approach referred to as Kalman filter with unknown inputs without direct feedthrough (KF-UI-WDF) is derived. It is shown that the proposed KF-UI-WDF approach is uniquely optimal in sense of both least-squares (LS) and minimum-variances unbiased (MVU) over a category of MVU filters (e.g., [4], [5], [10]). The global optimality of the proposed KF-UI-WDF approach is also discussed. Due to the limited space, an illustrative example is omitted. ©2009 ACA.

AB - The problem of joint input and state estimation is addressed in this paper for linear discrete-time stochastic systems without direct feedthrough from unknown inputs to outputs. With the weighted least squares estimation for an extended state vector including unknown inputs and states, a recursive filter approach referred to as Kalman filter with unknown inputs without direct feedthrough (KF-UI-WDF) is derived. It is shown that the proposed KF-UI-WDF approach is uniquely optimal in sense of both least-squares (LS) and minimum-variances unbiased (MVU) over a category of MVU filters (e.g., [4], [5], [10]). The global optimality of the proposed KF-UI-WDF approach is also discussed. Due to the limited space, an illustrative example is omitted. ©2009 ACA.

M3 - Conference contribution

SN - 9788995605691

SP - 378

EP - 383

BT - Proceedings of 2009 7th Asian Control Conference, ASCC 2009|Proc. Asian Control Conf., ASCC

T2 - 2009 7th Asian Control Conference, ASCC 2009

Y2 - 1 July 2009

ER -

Input and state estimation for linear systems: A least squares estimation approach

Abstract

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