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180405 ||| eng |
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|a 9789811084096
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1 |
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|a Tri Tran C., Anthony
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| 245 |
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|a A Quadratic Constraint Approach to Model Predictive Control of Interconnected Systems
|h Elektronische Ressource
|c by Anthony Tri Tran C., Quang Ha
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| 250 |
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|a 1st ed. 2018
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| 260 |
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|a Singapore
|b Springer Nature Singapore
|c 2018, 2018
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| 300 |
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|a XVII, 236 p. 100 illus., 83 illus. in color
|b online resource
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| 505 |
0 |
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|a Introduction -- Quadratic Constraint for Decentralised Model Predictive Control -- Quadratic Constraint for Parallel Splitting Systems -- Quadratic Constraint for Semi-Automatic Control -- Quadratic Constraint with Data Losses
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| 653 |
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|a Computational intelligence
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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 Control engineering
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| 653 |
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|a Telecommunication
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| 653 |
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|a Communications Engineering, Networks
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| 700 |
1 |
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|a Ha, Quang
|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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| 490 |
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|a Studies in Systems, Decision and Control
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| 028 |
5 |
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|a 10.1007/978-981-10-8409-6
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| 856 |
4 |
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|u https://doi.org/10.1007/978-981-10-8409-6?nosfx=y
|x Verlag
|3 Volltext
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| 082 |
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|a 006.3
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| 520 |
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|a This book focuses on the stabilization and model predictive control of interconnected systems with mixed connection configurations. It introduces the concept of dissipation-based quadratic constraint for developing attractivity assurance methods for interconnected systems. In order to develop these methods, distributed and decentralized architectures are employed, whereby the communication between subsystems is fully connected, partially connected, or completely disconnected. Given that the control inputs are entirely or partially decoupled between subsystems and no additional constraints are imposed on the interactive variables beyond the coupling constraint itself, the proposed approaches can be used with various types of systems and applications. Further, the book describes how the effects of coupling delays and data losses in device networks are resolved. From a practical perspective, the innovations presented are of benefit in applications in a broad range of fields, including theprocess and manufacturing industries, networked robotics, and network-centric systems such as chemical process systems, power systems, telecommunication networks, transportation networks, and, no less importantly, supply chain automation
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