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Iterative Learning Control: Robustness and Monotonic Convergence for Interval Systems

Hyo-Sung Ahn YangQuan Chen Kevin L. Moore

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Institución detectada Año de publicación Navegá Descargá Solicitá
No detectada 2007 SpringerLink

Información

Tipo de recurso:

libros

ISBN impreso

978-1-84628-846-3

ISBN electrónico

978-1-84628-859-3

Editor responsable

Springer Nature

País de edición

Reino Unido

Fecha de publicación

Información sobre derechos de publicación

© Springer-Verlag London Limited 2007

Cobertura temática

electronica

Ingeniería eléctrica, electrónica e informática

ingenieria-medica

Ingeniería médica

Tabla de contenidos

doi: 10.1007/978-1-84628-859-3_1

Introduction

Hyo-Sung Ahn; YangQuan Chen; Kevin L. Moore

To solve intra-tour problems containing more than 10 orders we have developed various construction and improvement heuristics (Section 4.4). Especially, to treat larger offline problems with several hundred orders we developed a heuristics to reassign orders to other vehicles, and improve existing tours by simulated annealing (Section 4.4.3).

Part I - Iterative Learning Control Overview | Pp. 3-18

doi: 10.1007/978-1-84628-859-3_2

An Overview of the ILC Literature

Hyo-Sung Ahn; YangQuan Chen; Kevin L. Moore

To solve intra-tour problems containing more than 10 orders we have developed various construction and improvement heuristics (Section 4.4). Especially, to treat larger offline problems with several hundred orders we developed a heuristics to reassign orders to other vehicles, and improve existing tours by simulated annealing (Section 4.4.3).

Part I - Iterative Learning Control Overview | Pp. 19-25

doi: 10.1007/978-1-84628-859-3_3

The Super-vector Approach

Hyo-Sung Ahn; YangQuan Chen; Kevin L. Moore

To solve intra-tour problems containing more than 10 orders we have developed various construction and improvement heuristics (Section 4.4). Especially, to treat larger offline problems with several hundred orders we developed a heuristics to reassign orders to other vehicles, and improve existing tours by simulated annealing (Section 4.4.3).

Part I - Iterative Learning Control Overview | Pp. 27-33

doi: 10.1007/978-1-84628-859-3_4

Robust Interval Iterative Learning Control: Analysis

Hyo-Sung Ahn; YangQuan Chen; Kevin L. Moore

To solve intra-tour problems containing more than 10 orders we have developed various construction and improvement heuristics (Section 4.4). Especially, to treat larger offline problems with several hundred orders we developed a heuristics to reassign orders to other vehicles, and improve existing tours by simulated annealing (Section 4.4.3).

Part II - Robust Interval Iterative Learning Control | Pp. 37-54

doi: 10.1007/978-1-84628-859-3_5

Schur Stability Radius of Interval Iterative Learning Control

Hyo-Sung Ahn; YangQuan Chen; Kevin L. Moore

To solve intra-tour problems containing more than 10 orders we have developed various construction and improvement heuristics (Section 4.4). Especially, to treat larger offline problems with several hundred orders we developed a heuristics to reassign orders to other vehicles, and improve existing tours by simulated annealing (Section 4.4.3).

Part II - Robust Interval Iterative Learning Control | Pp. 55-68

doi: 10.1007/978-1-84628-859-3_6

Iterative Learning Control Design Based on Interval Model Conversion

Hyo-Sung Ahn; YangQuan Chen; Kevin L. Moore

To solve intra-tour problems containing more than 10 orders we have developed various construction and improvement heuristics (Section 4.4). Especially, to treat larger offline problems with several hundred orders we developed a heuristics to reassign orders to other vehicles, and improve existing tours by simulated annealing (Section 4.4.3).

Part II - Robust Interval Iterative Learning Control | Pp. 69-80

doi: 10.1007/978-1-84628-859-3_7

Robust Iterative Learning Control: Approach

Hyo-Sung Ahn; YangQuan Chen; Kevin L. Moore

To solve intra-tour problems containing more than 10 orders we have developed various construction and improvement heuristics (Section 4.4). Especially, to treat larger offline problems with several hundred orders we developed a heuristics to reassign orders to other vehicles, and improve existing tours by simulated annealing (Section 4.4.3).

Part III - Iteration-domain Robustness | Pp. 83-100

doi: 10.1007/978-1-84628-859-3_8

Robust Iterative Learning Control: Stochastic Approaches

Hyo-Sung Ahn; YangQuan Chen; Kevin L. Moore

To solve intra-tour problems containing more than 10 orders we have developed various construction and improvement heuristics (Section 4.4). Especially, to treat larger offline problems with several hundred orders we developed a heuristics to reassign orders to other vehicles, and improve existing tours by simulated annealing (Section 4.4.3).

Part III - Iteration-domain Robustness | Pp. 101-134

doi: 10.1007/978-1-84628-859-3_9

Conclusions

Hyo-Sung Ahn; YangQuan Chen; Kevin L. Moore

To solve intra-tour problems containing more than 10 orders we have developed various construction and improvement heuristics (Section 4.4). Especially, to treat larger offline problems with several hundred orders we developed a heuristics to reassign orders to other vehicles, and improve existing tours by simulated annealing (Section 4.4.3).

Part III - Iteration-domain Robustness | Pp. 135-139