Neual state space model based approximation pole assignment control for a class of unknown nonlinear systems

Q. Wu; Y. J. Wang; H. Wang

doi:10.23919/ecc.2003.7085257

Neual state space model based approximation pole assignment control for a class of unknown nonlinear systems

Q. Wu, Y. J. Wang, H. Wang

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

Abstract

In this paper, an extended linearized neural state space (ELNSS) model is proposed and used to design an approximate pole assignment control strategy for a class of nonlinear systems. At first, the applicability of the ELNSS model to approximate affine nonlinear systems is studied, where the extended Kalman filter (EKF) algorithm is employed to train the weights of the ELNSS model. It has been shown that such a training algorithm can guarantee the convergence of the network weights. Using the trained weights in the ELNSS model, the design of an approximate pole assignment controller is performed using a state feedback framework. The convergence of the approximate pole assignment adaptive control algorithm is also analyzed.

Original language	English
Title of host publication	European Control Conference, ECC 2003
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1984-1989
Number of pages	6
ISBN (Electronic)	9783952417379
DOIs	https://doi.org/10.23919/ecc.2003.7085257
State	Published - Apr 13 2003
Externally published	Yes
Event	2003 European Control Conference, ECC 2003 - Cambridge, United Kingdom Duration: Sep 1 2003 → Sep 4 2003

Publication series

Name	European Control Conference, ECC 2003

Conference

Conference	2003 European Control Conference, ECC 2003
Country/Territory	United Kingdom
City	Cambridge
Period	09/1/03 → 09/4/03

Keywords

Approximate pole assignment
Neural state space
Nonlinear systems

Access to Document

10.23919/ecc.2003.7085257

Cite this

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title = "Neual state space model based approximation pole assignment control for a class of unknown nonlinear systems",

abstract = "In this paper, an extended linearized neural state space (ELNSS) model is proposed and used to design an approximate pole assignment control strategy for a class of nonlinear systems. At first, the applicability of the ELNSS model to approximate affine nonlinear systems is studied, where the extended Kalman filter (EKF) algorithm is employed to train the weights of the ELNSS model. It has been shown that such a training algorithm can guarantee the convergence of the network weights. Using the trained weights in the ELNSS model, the design of an approximate pole assignment controller is performed using a state feedback framework. The convergence of the approximate pole assignment adaptive control algorithm is also analyzed.",

keywords = "Approximate pole assignment, Neural state space, Nonlinear systems",

author = "Q. Wu and Wang, {Y. J.} and H. Wang",

note = "Publisher Copyright: {\textcopyright} 2003 EUCA.; 2003 European Control Conference, ECC 2003 ; Conference date: 01-09-2003 Through 04-09-2003",

year = "2003",

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day = "13",

doi = "10.23919/ecc.2003.7085257",

language = "English",

series = "European Control Conference, ECC 2003",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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booktitle = "European Control Conference, ECC 2003",

}

Wu, Q, Wang, YJ & Wang, H 2003, Neual state space model based approximation pole assignment control for a class of unknown nonlinear systems. in European Control Conference, ECC 2003., 7085257, European Control Conference, ECC 2003, Institute of Electrical and Electronics Engineers Inc., pp. 1984-1989, 2003 European Control Conference, ECC 2003, Cambridge, United Kingdom, 09/1/03. https://doi.org/10.23919/ecc.2003.7085257

Neual state space model based approximation pole assignment control for a class of unknown nonlinear systems. / Wu, Q.; Wang, Y. J.; Wang, H.
European Control Conference, ECC 2003. Institute of Electrical and Electronics Engineers Inc., 2003. p. 1984-1989 7085257 (European Control Conference, ECC 2003).

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

TY - GEN

T1 - Neual state space model based approximation pole assignment control for a class of unknown nonlinear systems

AU - Wu, Q.

AU - Wang, Y. J.

AU - Wang, H.

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N2 - In this paper, an extended linearized neural state space (ELNSS) model is proposed and used to design an approximate pole assignment control strategy for a class of nonlinear systems. At first, the applicability of the ELNSS model to approximate affine nonlinear systems is studied, where the extended Kalman filter (EKF) algorithm is employed to train the weights of the ELNSS model. It has been shown that such a training algorithm can guarantee the convergence of the network weights. Using the trained weights in the ELNSS model, the design of an approximate pole assignment controller is performed using a state feedback framework. The convergence of the approximate pole assignment adaptive control algorithm is also analyzed.

AB - In this paper, an extended linearized neural state space (ELNSS) model is proposed and used to design an approximate pole assignment control strategy for a class of nonlinear systems. At first, the applicability of the ELNSS model to approximate affine nonlinear systems is studied, where the extended Kalman filter (EKF) algorithm is employed to train the weights of the ELNSS model. It has been shown that such a training algorithm can guarantee the convergence of the network weights. Using the trained weights in the ELNSS model, the design of an approximate pole assignment controller is performed using a state feedback framework. The convergence of the approximate pole assignment adaptive control algorithm is also analyzed.

KW - Approximate pole assignment

KW - Neural state space

KW - Nonlinear systems

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M3 - Conference contribution

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T3 - European Control Conference, ECC 2003

SP - 1984

EP - 1989

BT - European Control Conference, ECC 2003

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2003 European Control Conference, ECC 2003

Y2 - 1 September 2003 through 4 September 2003

ER -

Neual state space model based approximation pole assignment control for a class of unknown nonlinear systems

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Keywords

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