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190425 ||| eng |
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|a 9789811357909
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| 100 |
1 |
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|a Wang, Limin
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| 245 |
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|a Iterative Learning Stabilization and Fault-Tolerant Control for Batch Processes
|h Elektronische Ressource
|c by Limin Wang, Ridong Zhang, Furong Gao
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| 250 |
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|a 1st ed. 2020
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| 260 |
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|a Singapore
|b Springer Singapore
|c 2020, 2020
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| 300 |
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|a X, 323 p. 66 illus., 58 illus. in color
|b online resource
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| 505 |
0 |
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|a 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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| 653 |
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|a Computer Communication Networks
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| 653 |
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|a Communications Engineering, Networks
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| 653 |
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|a Engineering
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| 653 |
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|a Calculus of Variations and Optimal Control; Optimization
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| 653 |
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|a Control and Systems Theory
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| 653 |
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|a Computational Intelligence
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| 653 |
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|a Computer Communication Networks
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| 653 |
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|a Telecommunication
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| 653 |
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|a Mathematical optimization
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| 700 |
1 |
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|a Zhang, Ridong
|e [author]
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| 700 |
1 |
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|a Gao, Furong
|e [author]
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b Springer
|a Springer eBooks 2005-
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| 856 |
4 |
0 |
|u https://doi.org/10.1007/978-981-13-5790-9?nosfx=y
|x Verlag
|3 Volltext
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| 082 |
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|a 629.8
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| 520 |
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|a 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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