State feedback control based on twin support vector regression compensating for a class of nonlinear systems

Chaoxu Mu; Changyin Sun; Xinghuo Yu

doi:10.1007/978-3-642-21090-7_60

State feedback control based on twin support vector regression compensating for a class of nonlinear systems

Chaoxu Mu
, Changyin Sun
, Xinghuo Yu

Southeast University, Nanjing

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

Abstract

In this paper, we introduce a new twin support vector regression (TSVR) algorithm, which estimates an unknown function by approaching its up and lower boundary, the ending function is obtained by the mean of the two function. For the class of nonlinear systems composed by linear and nonlinear parts, we use TSVR with a wavelet kernel to estimate the unknown nonlinear part in the original system and to counteract it, and then a state feedback control is carried out to realize a close loop control for the compensated system. Simulation results show that the TSVR with the wavelet kernel has good approaching ability and generalization. The whole close loop system with a state feedback control is stable when the compensating errors satisfy certain conditions.

Original language	English
Title of host publication	Advances in Neural Networks - 8th International Symposium on Neural Networks, ISNN 2011
Pages	515-524
Number of pages	10
Edition	PART 2
DOIs	https://doi.org/10.1007/978-3-642-21090-7_60
State	Published - 2011
Externally published	Yes
Event	8th International Symposium on Neural Networks, ISNN 2011 - Guilin, China Duration: 29 May 2011 → 1 Jun 2011

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 2
Volume	6676 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	8th International Symposium on Neural Networks, ISNN 2011
Country/Territory	China
City	Guilin
Period	29/05/11 → 1/06/11

Access to Document

10.1007/978-3-642-21090-7_60

Cite this

Mu, C., Sun, C., & Yu, X. (2011). State feedback control based on twin support vector regression compensating for a class of nonlinear systems. In Advances in Neural Networks - 8th International Symposium on Neural Networks, ISNN 2011 (PART 2 ed., pp. 515-524). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6676 LNCS, No. PART 2). https://doi.org/10.1007/978-3-642-21090-7_60

Mu, Chaoxu ; Sun, Changyin ; Yu, Xinghuo. / State feedback control based on twin support vector regression compensating for a class of nonlinear systems. Advances in Neural Networks - 8th International Symposium on Neural Networks, ISNN 2011. PART 2. ed. 2011. pp. 515-524 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2).

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title = "State feedback control based on twin support vector regression compensating for a class of nonlinear systems",

abstract = "In this paper, we introduce a new twin support vector regression (TSVR) algorithm, which estimates an unknown function by approaching its up and lower boundary, the ending function is obtained by the mean of the two function. For the class of nonlinear systems composed by linear and nonlinear parts, we use TSVR with a wavelet kernel to estimate the unknown nonlinear part in the original system and to counteract it, and then a state feedback control is carried out to realize a close loop control for the compensated system. Simulation results show that the TSVR with the wavelet kernel has good approaching ability and generalization. The whole close loop system with a state feedback control is stable when the compensating errors satisfy certain conditions.",

author = "Chaoxu Mu and Changyin Sun and Xinghuo Yu",

year = "2011",

doi = "10.1007/978-3-642-21090-7\_60",

language = "英语",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

number = "PART 2",

pages = "515--524",

booktitle = "Advances in Neural Networks - 8th International Symposium on Neural Networks, ISNN 2011",

edition = "PART 2",

note = "8th International Symposium on Neural Networks, ISNN 2011 ; Conference date: 29-05-2011 Through 01-06-2011",

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Mu, C, Sun, C & Yu, X 2011, State feedback control based on twin support vector regression compensating for a class of nonlinear systems. in Advances in Neural Networks - 8th International Symposium on Neural Networks, ISNN 2011. PART 2 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 2, vol. 6676 LNCS, pp. 515-524, 8th International Symposium on Neural Networks, ISNN 2011, Guilin, China, 29/05/11. https://doi.org/10.1007/978-3-642-21090-7_60

State feedback control based on twin support vector regression compensating for a class of nonlinear systems. / Mu, Chaoxu; Sun, Changyin; Yu, Xinghuo.
Advances in Neural Networks - 8th International Symposium on Neural Networks, ISNN 2011. PART 2. ed. 2011. p. 515-524 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6676 LNCS, No. PART 2).

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

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AB - In this paper, we introduce a new twin support vector regression (TSVR) algorithm, which estimates an unknown function by approaching its up and lower boundary, the ending function is obtained by the mean of the two function. For the class of nonlinear systems composed by linear and nonlinear parts, we use TSVR with a wavelet kernel to estimate the unknown nonlinear part in the original system and to counteract it, and then a state feedback control is carried out to realize a close loop control for the compensated system. Simulation results show that the TSVR with the wavelet kernel has good approaching ability and generalization. The whole close loop system with a state feedback control is stable when the compensating errors satisfy certain conditions.

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Mu C, Sun C, Yu X. State feedback control based on twin support vector regression compensating for a class of nonlinear systems. In Advances in Neural Networks - 8th International Symposium on Neural Networks, ISNN 2011. PART 2 ed. 2011. p. 515-524. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2). doi: 10.1007/978-3-642-21090-7_60

State feedback control based on twin support vector regression compensating for a class of nonlinear systems

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