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1. |
Record Nr. |
UNINA9910876549903321 |
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Autore |
Ding Baocang |
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Titolo |
Model Predictive Control |
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Pubbl/distr/stampa |
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Newark : , : John Wiley & Sons, Incorporated, , 2024 |
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©2024 |
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ISBN |
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1-119-47142-7 |
1-119-47145-1 |
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Edizione |
[1st ed.] |
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Descrizione fisica |
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1 online resource (307 pages) |
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Collana |
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Altri autori (Persone) |
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Soggetti |
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Predictive control |
Process control |
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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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Cover -- Title Page -- Copyright -- Contents -- About the Authors -- Preface -- Acronyms -- Introduction -- Chapter 1 Concepts -- 1.1 PID and Model Predictive Control -- 1.2 Two‐Layered Model Predictive Control -- 1.3 Hierarchical Model Predictive Control -- Chapter 2 Parameter Estimation and Output Prediction -- 2.1 Test Signal for Model Identification -- 2.1.1 Step Test -- 2.1.2 White Noise -- 2.1.3 Pseudo‐Random Binary Sequence -- 2.1.4 Generalized Binary Noise -- 2.2 Step Response Model Identification -- 2.2.1 Model -- 2.2.2 Data Processing -- 2.2.2.1 Marking or Interpolation of Bad Data -- 2.2.2.2 Smoothing Data -- 2.2.3 Model Identification -- 2.2.3.1 Case Grouping -- 2.2.3.2 Cased Data Preparation for Stable Dependent Variables -- 2.2.3.3 Cased Data Preparation for Integral Dependent Variables -- 2.2.3.4 Least Square Solution to Parameter Regression -- 2.2.3.5 Least Square Solution by SVD Decomposition -- 2.2.3.6 Filtering Pulse Response Coefficients |
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Sommario/riassunto |
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This book provides an in-depth exploration of Model Predictive Control (MPC), a class of model-based control algorithms. It delves into various aspects such as parameter estimation, steady-state target calculation, and two-layered dynamic matrix control (DMC) for stable and integral processes. The authors, Baocang Ding and Yuanqing Yang, aim to educate both undergraduate and graduate students, as well as |
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practitioners in automation and control systems. The text covers theoretical foundations and practical applications, including robust and heuristic models, output feedback, and real-time optimization. The book is intended for those studying or working in fields like process control and automation, offering insights into advanced control strategies and algorithm implementation. |
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