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Biblioteca

MARC Lista (tabellare)
Advanced Model Predictive Control / / Tao Zheng, editor
Advanced Model Predictive Control / / Tao Zheng, editor
Pubbl/distr/stampa Rijeka, Croatia : , : InTech, , [2011]
Descrizione fisica 1 online resource (x, 418 pages) : illustrations
Disciplina 629.8
Soggetto topico Predictive control
ISBN 953-51-6015-X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910138283503321
Rijeka, Croatia : , : InTech, , [2011]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Advanced Model Predictive Control for Autonomous Marine Vehicles / / Yang Shi [and three others]
Advanced Model Predictive Control for Autonomous Marine Vehicles / / Yang Shi [and three others]
Autore Shi Yang <1960->
Edizione [1st ed. 2023.]
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2023]
Descrizione fisica 1 online resource (210 pages)
Disciplina 629.8
Collana Advances in Industrial Control Series
Soggetto topico Predictive control
Autonomous underwater vehicles
ISBN 3-031-19354-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 1. Introduction -- 2. AUV Modelling -- 3. Receding Horizon Optimization for Integrated Path Planning and Tracking Control of an AUV -- 4. Lyapunov-Based Model Predictive Control for Dynamic Positioning and Trajectory Tracking Control of an AUV -- 5. Multi-Objective Model Predictive Control for Path Following Control of an AUV -- 6. Efficient Implementation Algorithms for NMPC Trajectory Tracking Control of an AUV -- 7. Distributed Lyapunov-based Model Predictive Formation Tracking Control for AUVs Subject to Disturbances -- 8. Conclusions and Future Work.
Record Nr. UNINA-9910659482003321
Shi Yang <1960->  
Cham, Switzerland : , : Springer, , [2023]
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Battery control using stochastic model predictive control / / Michael Blonsky
Battery control using stochastic model predictive control / / Michael Blonsky
Autore Blonsky Michael
Pubbl/distr/stampa Golden, CO : , : National Renewable Energy Laboratory, , [2021]
Descrizione fisica 1 online resource (13 pages) : color illustrations
Collana NREL/PR
Soggetto topico Stochastic models
Predictive control
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910716729903321
Blonsky Michael  
Golden, CO : , : National Renewable Energy Laboratory, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Cultures of prediction : how engineering and science evolve with mathematical tools / / Ann Johnson, Johannes Lenhard
Cultures of prediction : how engineering and science evolve with mathematical tools / / Ann Johnson, Johannes Lenhard
Autore Johnson Ann <1965-2016, >
Edizione [1st ed.]
Pubbl/distr/stampa Cambridge, Massachusetts : , : The MIT Press, , 2024
Descrizione fisica 1 online resource (272 pages)
Disciplina 620.001/51
Collana Engineering studies
Soggetto topico Mathematical models - History
Engineering mathematics - History
Predictive analytics
Predictive control
Soggetto non controllato TECHNOLOGY & ENGINEERING / History
SCIENCE / Philosophy & Social Aspects
SOCIAL SCIENCE / Future Studies
ISBN 0-262-37905-8
0-262-37904-X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910901897503321
Johnson Ann <1965-2016, >  
Cambridge, Massachusetts : , : The MIT Press, , 2024
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Distributed model predictive control made easy / / Jose M. Maestre, Rudy R. Negenborn, editors
Distributed model predictive control made easy / / Jose M. Maestre, Rudy R. Negenborn, editors
Edizione [1st ed. 2014.]
Pubbl/distr/stampa Dordrecht, Netherlands : , : Springer, , 2014
Descrizione fisica 1 online resource (xviii, 600 pages) : illustrations (some color)
Disciplina 629.836
Collana Intelligent Systems, Control and Automation: Science and Engineering
Soggetto topico Predictive control
ISBN 94-007-7006-5
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Preface -- List of Contributors -- On 35 Approaches for Distributed MPC Made Easy, by R.R. Negenborn, J.M. Maestre.- Part I: From Small-Scale to Large-Scale. The Group of Autonomous Systems Perspective -- 1 Bargaining game based distributed MPC, by F. Valencia, J.D. López, J.A. Patiño, J.J. Espinosa.- 2 Cooperative tube-based distributed MPC for linear uncertain systems coupled via constraints, by P.A. Trodden, A.G. Richards -- 3 Price-driven coordination for distributed NMPC using a feedback control law, by R. Martí, D. Sarabia, C. de Prada -- 4 Distributed MPC for consensus and synchronization, by M.A. Müller, F. Allgöwer.- 5 Distributed MPC under coupled constraints based on Dantzig-Wolfe decomposition, by R. Bourdais, J. Buisson, D. Dumur, H. Guéguen, P-D. Moroşan -- 6 Distributed MPC via dual decomposition and alternative direction method of multipliers , by F. Farokhi, I. Shames, K.H. Johansson -- 7 D-SIORHC, distributed MPC with stability constraints based on a game approach, by J.M. Lemos, J.M. Igreja -- 8 A distributed-in-time NMPC-based coordination mechanism for resource sharing problems , by M.Y. Lamoudi, M. Alamir, P. Béguery -- 9 Rate analysis of inexact dual fast gradient method for distributed MPC, by I. Necoara -- 10 Distributed MPC via dual decomposition , by B. Biegel, J. Stoustrup, P. Andersen -- 11 Distributed optimization for MPC of linear dynamic networks, by E. Camponogara -- 12 Adaptive quasi-decentralized MPC of networked process systems, by Y. Hu, N.H. El-Farra.- 13 Distributed Lyapunov-based MPC, by R. Hermans, M. Lazar, A. Jokić -- 14 A distributed reference management scheme in presence of non-convex constraints: an MPC based approach, by F. Tedesco, D.M. Raimondo, A. Casavola -- 15 The distributed command governor approach in a nutshell, by A. Casavola, E. Garone, F. Tedesco -- 16 Mixed-integer programming techniques in distributed MPC problems, by I. Prodan, F. Stoican, S. Olaru, C. Stoica, S-I. Niculescu -- 17 Distributed MPC of interconnected nonlinear systems by dynamic dual decomposition, by A. Grancharova, T.A. Johansen -- 18 Generalized accelerated gradient methods for distributed MPC based on dual decomposition, by P. Giselsson, A. Rantzer.- 19 Distributed multiple shooting for large scale nonlinear systems, by A. Kozma, C. Savorgnan, M. Diehl.- 20 Nash-based distributed MPC for multi-rate systems , by S. Roshany-Yamchi, R.R. Negenborn, A.A. Cornelio.- Part II: From Large-Scale to Small-Scale. The Decomposed Monolithic System Perspective.- 21 Cooperative dynamic MPC for networked control systems, by I. Jurado, D.E. Quevedo, K.H. Johansson, A. Ahlén -- 22 Parallel implementation of hybrid MPC, by D. Axehill, A. Hansson -- 23 A hierarchical MPC approach with guaranteed feasibility for dynamically coupled linear systems, by M.D. Doan, T. Keviczky, B. De Schutter -- 24 Distributed MPC based on a team game, by J.M. Maestre, F.J. Muros, F. Fele, D. Muñoz de la Peña, E. F. Camacho -- 25 Distributed MPC: A noncooperative approach based on robustness concepts , by G. Betti, M. Farina, R. Scattolini -- 26 Decompositions of augmented Lagrange formulations for serial and parallel distributed MPC, by R.R. Negenborn -- 27 A hierarchical distributed MPC approach: A practical implementation, by A. Zafra-Cabeza, J.M. Maestre -- 28 Distributed MPC based on agent negotiation, by J.M. Maestre, D. Muñoz de la Peña, E.F. Camacho.- 29 Lyapunov-based distributed MPC schemes: Sequential and iterative approaches, by J. Liu, D. Muñoz de la Peña, P.D. Christofides -- 30 Multi-layer decentralized MPC of large-scale networked systems, by C. Ocampo-Martinez, V. Puig, J.M. Grosso, S. Montes-de-Oca -- 31 Distributed MPC using reinforcement learning based negotiation: Application to large scale systems, by B. Morcego, V. Javalera, V. Puig, R. Vito -- 32 Hierarchical MPC for multiple commodity transportation networks, by J.L. Nabais, R.R. Negenborn, R.B. Carmona-Benítez, L.F. Mendonça, M.A. Botto -- 33 On the use of suboptimal solvers for efficient cooperative distributed linear MPC, by G. Pannocchia, S.J. Wright, J.B. Rawlings -- 34 Cooperative distributed MPC integrating a steady state target optimizer, by A. Ferramosca, D. Limon, A.H. González -- 35 Cooperative MPC with guaranteed exponential stability, by A. Ferramosca.
Record Nr. UNINA-9910299709903321
Dordrecht, Netherlands : , : Springer, , 2014
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Frontiers of model predictive control / / edited by Tao Zheng
Frontiers of model predictive control / / edited by Tao Zheng
Pubbl/distr/stampa Rijeka, Croatia : , : IntechOpen, , [2012]
Descrizione fisica 1 online resource (170 pages) : illustrations
Disciplina 629.8
Soggetto topico Predictive control
ISBN 953-51-6123-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910317643703321
Rijeka, Croatia : , : IntechOpen, , [2012]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Model based control [[electronic resource] ] : case studies in process engineering / / Paul Serban Agachi ... [et al.]
Model based control [[electronic resource] ] : case studies in process engineering / / Paul Serban Agachi ... [et al.]
Autore Agachi Paul Șerban
Pubbl/distr/stampa Weinheim, : Wiley-VCH, c2006
Descrizione fisica 1 online resource (291 p.)
Disciplina 660.2815
Altri autori (Persone) AgachiPaul Serban
Soggetto topico Chemical engineering
Chemical process control
Predictive control
Soggetto genere / forma Electronic books.
ISBN 1-281-08795-5
9786611087951
3-527-60947-4
3-527-60922-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Model Based Control; Table of Contents; Preface; 1 Introduction; 1.1 Introductory Concepts of Process Control; 1.2 Advanced Process Control Techniques; 1.2.1 Key Problems in Advanced Control of Chemical Processes; 1.2.1.1 Nonlinear Dynamic Behavior; 1.2.1.2 Multivariable Interactions between Manipulated and Controlled Variables; 1.2.1.3 Uncertain and Time-Varying Parameters; 1.2.1.4 Deadtime on Inputs and Measurements; 1.2.1.5 Constraints on Manipulated and State Variables; 1.2.1.6 High-Order and Distributed Processes
1.2.1.7 Unmeasured State Variables and Unmeasured and Frequent Disturbances1.2.2 Classification of the Advanced Process Control Techniques; 2 Model Predictive Control; 2.1 Internal Model Control; 2.2 Linear Model Predictive Control; 2.3 Nonlinear Model Predictive Control; 2.3.1 Introduction; 2.3.2 Industrial Model-Based Control: Current Status and Challenges; 2.3.2.1 Challenges in Industrial NMPC; 2.3.3 First Principle (Analytical) Model-Based NMPC; 2.3.4 NMPC with Guaranteed Stability; 2.3.5 Artificial Neural Network (ANN)-Based Nonlinear Model Predictive Control; 2.3.5.1 Introduction
2.3.5.2 Basics of ANNs2.3.5.3 Algorithms for ANN Training; 2.3.5.4 Direct ANN Model-Based NMPC (DANMPC); 2.3.5.5 Stable DANMPC Control Law; 2.3.5.6 Inverse ANN Model-Based NMPC; 2.3.5.7 ANN Model-Based NMPC with Feedback Linearization; 2.3.5.8 ANN Model-Based NMPC with On-Line Linearization; 2.3.6 NMPC Software for Simulation and Practical Implementation; 2.3.6.1 Computational Issues; 2.3.6.2 NMPC Software for Simulation; 2.3.6.3 NMPC Software for Practical Implementation; 2.4 MPC General Tuning Guidelines; 2.4.1 Model Horizon (n); 2.4.2 Prediction Horizon (p); 2.4.3 Control Horizon (m)
2.4.4 Sampling Time (T)2.4.5 Weight Matrices (Γ(/)(y) and Γ(/)(u)); 2.4.6 Feedback Filter; 2.4.7 Dynamic Sensitivity Used for MPC Tuning; 3 Case Studies; 3.1 Productivity Optimization and Nonlinear Model Predictive Control (NMPC) of a PVC Batch Reactor; 3.1.1 Introduction; 3.1.2 Dynamic Model of the PVC Batch Reactor; 3.1.2.1 The Complex Analytical Model of the PVC Reactor; 3.1.2.2 Morphological Model; 3.1.2.3 The Simplified Dynamic Analytical Model of the PVC Reactor; 3.1.3 Productivity Optimization of the PVC Batch Reactor; 3.1.3.1 The Basic Elements of GAs
3.1.3.2 Optimization of the PVC Reactor Productivity through the Initial Concentration of Initiators3.1.3.3 Optimization of PVC Reactor Productivity by obtaining an Optimal Temperature Policy; 3.1.4 NMPC of the PVC Batch Reactor; 3.1.4.1 Multiple On-Line Linearization-Based NMPC of the PVC Batch Reactor; 3.1.4.2 Sequential NMPC of the PVC Batch Reactor; 3.1.5 Conclusions; 3.1.6 Nomenclature; 3.2 Modeling, Simulation, and Control of a Yeast Fermentation Bioreactor; 3.2.1 First Principle Model of the Continuous Fermentation Bioreactor
3.2.2 Linear Model Identification and LMPC of the Bioreactor
Record Nr. UNINA-9910144003503321
Agachi Paul Șerban  
Weinheim, : Wiley-VCH, c2006
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Model based control [[electronic resource] ] : case studies in process engineering / / Paul Serban Agachi ... [et al.]
Model based control [[electronic resource] ] : case studies in process engineering / / Paul Serban Agachi ... [et al.]
Autore Agachi Paul Șerban
Pubbl/distr/stampa Weinheim, : Wiley-VCH, c2006
Descrizione fisica 1 online resource (291 p.)
Disciplina 660.2815
Altri autori (Persone) AgachiPaul Serban
Soggetto topico Chemical engineering
Chemical process control
Predictive control
ISBN 1-281-08795-5
9786611087951
3-527-60947-4
3-527-60922-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Model Based Control; Table of Contents; Preface; 1 Introduction; 1.1 Introductory Concepts of Process Control; 1.2 Advanced Process Control Techniques; 1.2.1 Key Problems in Advanced Control of Chemical Processes; 1.2.1.1 Nonlinear Dynamic Behavior; 1.2.1.2 Multivariable Interactions between Manipulated and Controlled Variables; 1.2.1.3 Uncertain and Time-Varying Parameters; 1.2.1.4 Deadtime on Inputs and Measurements; 1.2.1.5 Constraints on Manipulated and State Variables; 1.2.1.6 High-Order and Distributed Processes
1.2.1.7 Unmeasured State Variables and Unmeasured and Frequent Disturbances1.2.2 Classification of the Advanced Process Control Techniques; 2 Model Predictive Control; 2.1 Internal Model Control; 2.2 Linear Model Predictive Control; 2.3 Nonlinear Model Predictive Control; 2.3.1 Introduction; 2.3.2 Industrial Model-Based Control: Current Status and Challenges; 2.3.2.1 Challenges in Industrial NMPC; 2.3.3 First Principle (Analytical) Model-Based NMPC; 2.3.4 NMPC with Guaranteed Stability; 2.3.5 Artificial Neural Network (ANN)-Based Nonlinear Model Predictive Control; 2.3.5.1 Introduction
2.3.5.2 Basics of ANNs2.3.5.3 Algorithms for ANN Training; 2.3.5.4 Direct ANN Model-Based NMPC (DANMPC); 2.3.5.5 Stable DANMPC Control Law; 2.3.5.6 Inverse ANN Model-Based NMPC; 2.3.5.7 ANN Model-Based NMPC with Feedback Linearization; 2.3.5.8 ANN Model-Based NMPC with On-Line Linearization; 2.3.6 NMPC Software for Simulation and Practical Implementation; 2.3.6.1 Computational Issues; 2.3.6.2 NMPC Software for Simulation; 2.3.6.3 NMPC Software for Practical Implementation; 2.4 MPC General Tuning Guidelines; 2.4.1 Model Horizon (n); 2.4.2 Prediction Horizon (p); 2.4.3 Control Horizon (m)
2.4.4 Sampling Time (T)2.4.5 Weight Matrices (Γ(/)(y) and Γ(/)(u)); 2.4.6 Feedback Filter; 2.4.7 Dynamic Sensitivity Used for MPC Tuning; 3 Case Studies; 3.1 Productivity Optimization and Nonlinear Model Predictive Control (NMPC) of a PVC Batch Reactor; 3.1.1 Introduction; 3.1.2 Dynamic Model of the PVC Batch Reactor; 3.1.2.1 The Complex Analytical Model of the PVC Reactor; 3.1.2.2 Morphological Model; 3.1.2.3 The Simplified Dynamic Analytical Model of the PVC Reactor; 3.1.3 Productivity Optimization of the PVC Batch Reactor; 3.1.3.1 The Basic Elements of GAs
3.1.3.2 Optimization of the PVC Reactor Productivity through the Initial Concentration of Initiators3.1.3.3 Optimization of PVC Reactor Productivity by obtaining an Optimal Temperature Policy; 3.1.4 NMPC of the PVC Batch Reactor; 3.1.4.1 Multiple On-Line Linearization-Based NMPC of the PVC Batch Reactor; 3.1.4.2 Sequential NMPC of the PVC Batch Reactor; 3.1.5 Conclusions; 3.1.6 Nomenclature; 3.2 Modeling, Simulation, and Control of a Yeast Fermentation Bioreactor; 3.2.1 First Principle Model of the Continuous Fermentation Bioreactor
3.2.2 Linear Model Identification and LMPC of the Bioreactor
Record Nr. UNINA-9910830387603321
Agachi Paul Șerban  
Weinheim, : Wiley-VCH, c2006
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Model based control : case studies in process engineering / / Paul Serban Agachi ... [et al.]
Model based control : case studies in process engineering / / Paul Serban Agachi ... [et al.]
Autore Agachi Paul Serban
Pubbl/distr/stampa Weinheim, : Wiley-VCH, c2006
Descrizione fisica 1 online resource (291 p.)
Disciplina 660.2815
Soggetto topico Chemical engineering
Chemical process control
Predictive control
ISBN 1-281-08795-5
9786611087951
3-527-60947-4
3-527-60922-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Model Based Control; Table of Contents; Preface; 1 Introduction; 1.1 Introductory Concepts of Process Control; 1.2 Advanced Process Control Techniques; 1.2.1 Key Problems in Advanced Control of Chemical Processes; 1.2.1.1 Nonlinear Dynamic Behavior; 1.2.1.2 Multivariable Interactions between Manipulated and Controlled Variables; 1.2.1.3 Uncertain and Time-Varying Parameters; 1.2.1.4 Deadtime on Inputs and Measurements; 1.2.1.5 Constraints on Manipulated and State Variables; 1.2.1.6 High-Order and Distributed Processes
1.2.1.7 Unmeasured State Variables and Unmeasured and Frequent Disturbances1.2.2 Classification of the Advanced Process Control Techniques; 2 Model Predictive Control; 2.1 Internal Model Control; 2.2 Linear Model Predictive Control; 2.3 Nonlinear Model Predictive Control; 2.3.1 Introduction; 2.3.2 Industrial Model-Based Control: Current Status and Challenges; 2.3.2.1 Challenges in Industrial NMPC; 2.3.3 First Principle (Analytical) Model-Based NMPC; 2.3.4 NMPC with Guaranteed Stability; 2.3.5 Artificial Neural Network (ANN)-Based Nonlinear Model Predictive Control; 2.3.5.1 Introduction
2.3.5.2 Basics of ANNs2.3.5.3 Algorithms for ANN Training; 2.3.5.4 Direct ANN Model-Based NMPC (DANMPC); 2.3.5.5 Stable DANMPC Control Law; 2.3.5.6 Inverse ANN Model-Based NMPC; 2.3.5.7 ANN Model-Based NMPC with Feedback Linearization; 2.3.5.8 ANN Model-Based NMPC with On-Line Linearization; 2.3.6 NMPC Software for Simulation and Practical Implementation; 2.3.6.1 Computational Issues; 2.3.6.2 NMPC Software for Simulation; 2.3.6.3 NMPC Software for Practical Implementation; 2.4 MPC General Tuning Guidelines; 2.4.1 Model Horizon (n); 2.4.2 Prediction Horizon (p); 2.4.3 Control Horizon (m)
2.4.4 Sampling Time (T)2.4.5 Weight Matrices (Γ(/)(y) and Γ(/)(u)); 2.4.6 Feedback Filter; 2.4.7 Dynamic Sensitivity Used for MPC Tuning; 3 Case Studies; 3.1 Productivity Optimization and Nonlinear Model Predictive Control (NMPC) of a PVC Batch Reactor; 3.1.1 Introduction; 3.1.2 Dynamic Model of the PVC Batch Reactor; 3.1.2.1 The Complex Analytical Model of the PVC Reactor; 3.1.2.2 Morphological Model; 3.1.2.3 The Simplified Dynamic Analytical Model of the PVC Reactor; 3.1.3 Productivity Optimization of the PVC Batch Reactor; 3.1.3.1 The Basic Elements of GAs
3.1.3.2 Optimization of the PVC Reactor Productivity through the Initial Concentration of Initiators3.1.3.3 Optimization of PVC Reactor Productivity by obtaining an Optimal Temperature Policy; 3.1.4 NMPC of the PVC Batch Reactor; 3.1.4.1 Multiple On-Line Linearization-Based NMPC of the PVC Batch Reactor; 3.1.4.2 Sequential NMPC of the PVC Batch Reactor; 3.1.5 Conclusions; 3.1.6 Nomenclature; 3.2 Modeling, Simulation, and Control of a Yeast Fermentation Bioreactor; 3.2.1 First Principle Model of the Continuous Fermentation Bioreactor
3.2.2 Linear Model Identification and LMPC of the Bioreactor
Record Nr. UNINA-9910876943803321
Agachi Paul Serban  
Weinheim, : Wiley-VCH, c2006
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Model Predictive Control
Model Predictive Control
Autore Ding Baocang
Edizione [1st ed.]
Pubbl/distr/stampa Newark : , : John Wiley & Sons, Incorporated, , 2024
Descrizione fisica 1 online resource (307 pages)
Altri autori (Persone) YangYuanqing
Collana IEEE Press Series
Soggetto topico Predictive control
Process control
ISBN 1-119-47142-7
1-119-47145-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 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
Record Nr. UNINA-9910876549903321
Ding Baocang  
Newark : , : John Wiley & Sons, Incorporated, , 2024
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui