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Iterative learning stabilization and...
~
Gao, Furong.
Iterative learning stabilization and fault-tolerant control for batch processes
Record Type:
Electronic resources : Monograph/item
Title/Author:
Iterative learning stabilization and fault-tolerant control for batch processesby Limin Wang, Ridong Zhang, Furong Gao.
Author:
Wang, Limin.
other author:
Zhang, Ridong.
Published:
Singapore :Springer Singapore :2020.
Description:
x, 323 p. :ill., digital ;24 cm.
Contained By:
Springer eBooks
Subject:
Process control.
Online resource:
https://doi.org/10.1007/978-981-13-5790-9
ISBN:
9789811357909$q(electronic bk.)
Iterative learning stabilization and fault-tolerant control for batch processes
Wang, Limin.
Iterative learning stabilization and fault-tolerant control for batch processes
[electronic resource] /by Limin Wang, Ridong Zhang, Furong Gao. - Singapore :Springer Singapore :2020. - x, 323 p. :ill., digital ;24 cm.
Introduction -- Iterative learning control of linear batch processes -- Iterative learning control of nonlinear batch processes -- Iterative learning optimal guaranteed cost control of batch processes -- Iterative learning control of multi-phase batch processes -- Delay-dependent iterative learning control of multi-phase batch processes -- Iterative learning fault-tolerant control of linear batch processes -- Iterative learning fault-tolerant control of nonlinear batch processes -- Iterative learning fault-tolerant control of multi-phase batch processes -- Further ideas on constrained infinite horizon fault-tolerant control of batch processes.
This book is based on the authors' research on the stabilization and fault-tolerant control of batch processes, which are flourishing topics in the field of control system engineering. It introduces iterative learning control for linear/nonlinear single/multi-phase batch processes; iterative learning optimal guaranteed cost control; delay-dependent iterative learning control; and iterative learning fault-tolerant control for linear/nonlinear single/multi-phase batch processes. Providing important insights and useful methods and practical algorithms that can potentially be applied in batch process control and optimization, it is a valuable resource for researchers, scientists, and engineers in the field of process system engineering and control engineering.
ISBN: 9789811357909$q(electronic bk.)
Standard No.: 10.1007/978-981-13-5790-9doiSubjects--Topical Terms:
182219
Process control.
LC Class. No.: TS156.8
Dewey Class. No.: 670.427
Iterative learning stabilization and fault-tolerant control for batch processes
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Introduction -- Iterative learning control of linear batch processes -- Iterative learning control of nonlinear batch processes -- Iterative learning optimal guaranteed cost control of batch processes -- Iterative learning control of multi-phase batch processes -- Delay-dependent iterative learning control of multi-phase batch processes -- Iterative learning fault-tolerant control of linear batch processes -- Iterative learning fault-tolerant control of nonlinear batch processes -- Iterative learning fault-tolerant control of multi-phase batch processes -- Further ideas on constrained infinite horizon fault-tolerant control of batch processes.
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This book is based on the authors' research on the stabilization and fault-tolerant control of batch processes, which are flourishing topics in the field of control system engineering. It introduces iterative learning control for linear/nonlinear single/multi-phase batch processes; iterative learning optimal guaranteed cost control; delay-dependent iterative learning control; and iterative learning fault-tolerant control for linear/nonlinear single/multi-phase batch processes. Providing important insights and useful methods and practical algorithms that can potentially be applied in batch process control and optimization, it is a valuable resource for researchers, scientists, and engineers in the field of process system engineering and control engineering.
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Intelligent Technologies and Robotics (Springer-42732)
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EB TS156.8 .W246 2020 2020
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