國立虎尾科技大學 |

Discrete-Time Adaptive Iterative Learning Control = From Model-Based to Data-Driven /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Discrete-Time Adaptive Iterative Learning Control/ by Ronghu Chi, Na Lin, Huimin Zhang, Ruikun Zhang.
其他題名:	From Model-Based to Data-Driven /
作者:	Chi, Ronghu.
其他作者:	Lin, Na.
面頁冊數:	X, 206 p. 83 illus., 72 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Control engineering. -
電子資源:	https://doi.org/10.1007/978-981-19-0464-6
ISBN:	9789811904646

Discrete-Time Adaptive Iterative Learning Control = From Model-Based to Data-Driven /
Chi, Ronghu.

Discrete-Time Adaptive Iterative Learning ControlFrom Model-Based to Data-Driven /[electronic resource] :by Ronghu Chi, Na Lin, Huimin Zhang, Ruikun Zhang. - 1st ed. 2022. - X, 206 p. 83 illus., 72 illus. in color.online resource. - Intelligent Control and Learning Systems,12662-5466 ;. - Intelligent Control and Learning Systems,4.

Chapter 1: Introduction -- Part 1: Model-based Discrete-time Adaptive ILC -- Chapter 2: Discrete-time Adaptive ILC for Linear Parametric Systems -- Chapter 3: Discrete-time Adaptive ILC for Higher order Parametric Systems -- Chapter 4: Data-weighted Discrete-time Adaptive ILC Chapter -- 5: Discrete-time Adaptive ILC for Nonparametric Nonlinear Systems Part 2: Data-driven Discrete-time Adaptive ILC Chapter -- 6: Neural Network based Discrete-time Adaptive ILC -- Chapter 7: Data-driven Discrete-time Adaptive ILC for Nonaffined Nonlinear Systems -- Chapter 8: Multi-input Enhanced Data-driven Discrete-time Adaptive ILC -- Chapter 9: High-order Internal Model based Data-driven Terminal Adaptive ILC -- Chapter 10: Conclusions Appendices.

This book belongs to the subject of control and systems theory. The discrete-time adaptive iterative learning control (DAILC) is discussed as a cutting-edge of ILC and can address random initial states, iteration-varying targets, and other non-repetitive uncertainties in practical applications. This book begins with the design and analysis of model-based DAILC methods by referencing the tools used in the discrete-time adaptive control theory. To overcome the extreme difficulties in modeling a complex system, the data-driven DAILC methods are further discussed by building a linear parametric data mapping between two consecutive iterations. Other significant improvements and extensions of the model-based/data-driven DAILC are also studied to facilitate broader applications. The readers can learn the recent progress on DAILC with consideration of various applications. This book is intended for academic scholars, engineers and graduate students who are interested in learning control, adaptive control, nonlinear systems, and related fields.

ISBN: 9789811904646

Standard No.: 10.1007/978-981-19-0464-6doiSubjects--Topical Terms:

1249728
Control engineering.

LC Class. No.: TJ212-225

Dewey Class. No.: 629.8312

Discrete-Time Adaptive Iterative Learning Control = From Model-Based to Data-Driven /
LDR:03365nam a22004335i 4500 001 1090978
003 DE-He213
005 20220923020925.0
007 cr nn 008mamaa
008 221228s2022 si | s |||| 0|eng d
020 $a 9789811904646 $9 978-981-19-0464-6
024 7 $a 10.1007/978-981-19-0464-6 $2 doi
035 $a 978-981-19-0464-6
050 4 $a TJ212-225
072 7 $a TJFM $2 bicssc
072 7 $a GPFC $2 bicssc
072 7 $a TEC004000 $2 bisacsh
072 7 $a TJFM $2 thema
082 0 4 $a 629.8312 $2 23
082 0 4 $a 003 $2 23
100 1 $a Chi, Ronghu. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1392032
245 1 0 $a Discrete-Time Adaptive Iterative Learning Control $h [electronic resource] : $b From Model-Based to Data-Driven / $c by Ronghu Chi, Na Lin, Huimin Zhang, Ruikun Zhang.
250 $a 1st ed. 2022.
264 1 $a Singapore : $b Springer Nature Singapore : $b Imprint: Springer, $c 2022.
300 $a X, 206 p. 83 illus., 72 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a Intelligent Control and Learning Systems, $x 2662-5466 ; $v 1
505 0 $a Chapter 1: Introduction -- Part 1: Model-based Discrete-time Adaptive ILC -- Chapter 2: Discrete-time Adaptive ILC for Linear Parametric Systems -- Chapter 3: Discrete-time Adaptive ILC for Higher order Parametric Systems -- Chapter 4: Data-weighted Discrete-time Adaptive ILC Chapter -- 5: Discrete-time Adaptive ILC for Nonparametric Nonlinear Systems Part 2: Data-driven Discrete-time Adaptive ILC Chapter -- 6: Neural Network based Discrete-time Adaptive ILC -- Chapter 7: Data-driven Discrete-time Adaptive ILC for Nonaffined Nonlinear Systems -- Chapter 8: Multi-input Enhanced Data-driven Discrete-time Adaptive ILC -- Chapter 9: High-order Internal Model based Data-driven Terminal Adaptive ILC -- Chapter 10: Conclusions Appendices.
520 $a This book belongs to the subject of control and systems theory. The discrete-time adaptive iterative learning control (DAILC) is discussed as a cutting-edge of ILC and can address random initial states, iteration-varying targets, and other non-repetitive uncertainties in practical applications. This book begins with the design and analysis of model-based DAILC methods by referencing the tools used in the discrete-time adaptive control theory. To overcome the extreme difficulties in modeling a complex system, the data-driven DAILC methods are further discussed by building a linear parametric data mapping between two consecutive iterations. Other significant improvements and extensions of the model-based/data-driven DAILC are also studied to facilitate broader applications. The readers can learn the recent progress on DAILC with consideration of various applications. This book is intended for academic scholars, engineers and graduate students who are interested in learning control, adaptive control, nonlinear systems, and related fields.
650 0 $a Control engineering. $3 1249728
650 0 $a Stochastic processes. $3 528256
650 0 $a Mathematics—Data processing. $3 1365953
650 1 4 $a Control and Systems Theory. $3 1211358
650 2 4 $a Stochastic Systems and Control. $3 1392035
650 2 4 $a Computational Science and Engineering. $3 670319
700 1 $a Lin, Na. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1398495
700 1 $a Zhang, Huimin. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1398496
700 1 $a Zhang, Ruikun. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1398497
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9789811904639
776 0 8 $i Printed edition: $z 9789811904653
776 0 8 $i Printed edition: $z 9789811904660
830 0 $a Intelligent Control and Learning Systems, $x 2662-5466 ; $v 4 $3 1391654
856 4 0 $u https://doi.org/10.1007/978-981-19-0464-6
912 $a ZDB-2-INR
912 $a ZDB-2-SXIT
950 $a Intelligent Technologies and Robotics (SpringerNature-42732)
950 $a Intelligent Technologies and Robotics (R0) (SpringerNature-43728)