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1. |
Record Nr. |
UNIORUON00454780 |
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Autore |
ERENBURG, Il'ja Grigor'evič |
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Titolo |
Portrety russkich poetov / Il'ja Erenburg |
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Pubbl/distr/stampa |
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Berlin', : Argonavty, 1922 |
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ISBN |
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Descrizione fisica |
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Disciplina |
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Soggetti |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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2. |
Record Nr. |
UNINA9910483180103321 |
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Autore |
Zhang Ridong |
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Titolo |
Model Predictive Control : Approaches Based on the Extended State Space Model and Extended Non-minimal State Space Model / / by Ridong Zhang, Anke Xue, Furong Gao |
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Pubbl/distr/stampa |
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Singapore : , : Springer Singapore : , : Imprint : Springer, , 2019 |
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ISBN |
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Edizione |
[1st ed. 2019.] |
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Descrizione fisica |
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1 online resource (xv, 137 pages) : illustrations |
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Disciplina |
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Soggetti |
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Systems theory |
Mathematical optimization |
Control and Systems Theory |
Systems Theory, Control |
Calculus of Variations and Optimal Control; Optimization |
Energy Efficiency |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Introduction -- Model Predictive Control Based on Extended State Space Model -- Predictive Functional Control Based on Extended State Space Model -- Model Predictive Control Based on Extended Non-Minimal State Space Model -- Predictive Functional Control Based on Extended Non-minimal State Space Model -- Model Predictive Control Under Constraints -- PID Control Using Extended Non-minimal State Space Model Optimization -- Closed-loop System Performance Analysis -- Model Predictive Control Performance Optimized by Genetic Algorithm -- Industrial Application -- Further Ideas on MPC and PFC Using Relaxed Constrained Optimization. |
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Sommario/riassunto |
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This monograph introduces the authors’ work on model predictive control system design using extended state space and extended non-minimal state space approaches. It systematically describes model predictive control design for chemical processes, including the basic control algorithms, the extension to predictive functional control, constrained control, closed-loop system analysis, model predictive control optimization-based PID control, genetic algorithm optimization-based model predictive control, and industrial applications. Providing important insights, useful methods and practical algorithms that can be used in chemical process control and optimization, it offers a valuable resource for researchers, scientists and engineers in the field of process system engineering and control engineering. . |
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