Atjaunināt sīkdatņu piekrišanu

E-grāmata: Predictive Control: Fundamentals and Developments

,
  • Formāts: PDF+DRM
  • Izdošanas datums: 28-Jun-2019
  • Izdevniecība: John Wiley & Sons Inc
  • Valoda: eng
  • ISBN-13: 9781119119586
Citas grāmatas par šo tēmu:
  • Formāts - PDF+DRM
  • Cena: 124,85 €*
  • * ši ir gala cena, t.i., netiek piemērotas nekādas papildus atlaides
  • Ielikt grozā
  • Pievienot vēlmju sarakstam
  • Šī e-grāmata paredzēta tikai personīgai lietošanai. E-grāmatas nav iespējams atgriezt un nauda par iegādātajām e-grāmatām netiek atmaksāta.
  • Bibliotēkām
Predictive Control: Fundamentals and DevelopmentsPalielināt
  • Formāts: PDF+DRM
  • Izdošanas datums: 28-Jun-2019
  • Izdevniecība: John Wiley & Sons Inc
  • Valoda: eng
  • ISBN-13: 9781119119586
Citas grāmatas par šo tēmu:

DRM restrictions

  • Kopēšana (kopēt/ievietot):

    nav atļauts

  • Drukāšana:

    nav atļauts

  • Lietošana:

    Digitālo tiesību pārvaldība (Digital Rights Management (DRM))
    Izdevējs ir piegādājis šo grāmatu šifrētā veidā, kas nozīmē, ka jums ir jāinstalē bezmaksas programmatūra, lai to atbloķētu un lasītu. Lai lasītu šo e-grāmatu, jums ir jāizveido Adobe ID. Vairāk informācijas šeit. E-grāmatu var lasīt un lejupielādēt līdz 6 ierīcēm (vienam lietotājam ar vienu un to pašu Adobe ID).

    Nepieciešamā programmatūra
    Lai lasītu šo e-grāmatu mobilajā ierīcē (tālrunī vai planšetdatorā), jums būs jāinstalē šī bezmaksas lietotne: PocketBook Reader (iOS / Android)

    Lai lejupielādētu un lasītu šo e-grāmatu datorā vai Mac datorā, jums ir nepieciešamid Adobe Digital Editions (šī ir bezmaksas lietotne, kas īpaši izstrādāta e-grāmatām. Tā nav tas pats, kas Adobe Reader, kas, iespējams, jau ir jūsu datorā.)

    Jūs nevarat lasīt šo e-grāmatu, izmantojot Amazon Kindle.

This book is a comprehensive introduction to model predictive control (MPC), including its basic principles and algorithms, system analysis and design methods, strategy developments and practical applications. The main contents of the book include an overview of the development trajectory and basic principles of MPC, typical MPC algorithms, quantitative analysis of classical MPC systems, design and tuning methods for MPC parameters, constrained multivariable MPC algorithms and online optimization decomposition methods. Readers will then progress to more advanced topics such as nonlinear MPC and its related algorithms, the diversification development of MPC with respect to control structures and optimization strategies, and robust MPC. Finally, applications of MPC and its generalization to optimization-based dynamic problems other than control will be discussed. 

  • Systematically introduces fundamental concepts, basic algorithms, and applications of MPC
  • Includes a comprehensive overview of MPC development, emphasizing recent advances and modern approaches
  • Features numerous MPC models and structures, based on rigorous research
  • Based on the best-selling Chinese edition, which is a key text in China

Predictive Control: Fundamentals and Developments is written for advanced undergraduate and graduate students and researchers specializing in control technologies. It is also a useful reference for industry professionals, engineers, and technicians specializing in advanced optimization control technology.

Preface xi
1 Brief History and Basic Principles of Predictive Control
1(14)
1.1 Generation and Development of Predictive Control
1(5)
1.2 Basic Methodological Principles of Predictive Control
6(4)
1.2.1 Prediction Model
6(1)
1.2.2 Rolling Optimization
6(1)
1.2.3 Feedback Correction
7(3)
1.3 Contents of this Book
10(1)
References
11(4)
2 Some Basic Predictive Control Algorithms
15(26)
2.1 Dynamic Matrix Control (DMC) Based on the Step Response Model
15(10)
2.1.1 DMC Algorithm and Implementation
15(6)
2.1.2 Description of DMC in the State Space Framework
21(4)
2.2 Generalized Predictive Control (GPC) Based on the Linear Difference Equation Model
25(7)
2.3 Predictive Control Based on the State Space Model
32(5)
2.4 Summary
37(2)
References
39(2)
3 Trend Analysis and Tuning of SISO Unconstrained DMC Systems
41(34)
3.1 The Internal Model Control Structure of the DMC Algorithm
41(7)
3.2 Controller of DMC in the IMC Structure
48(8)
3.2.1 Stability of the Controller
48(5)
3.2.2 Controller with the One-Step Optimization Strategy
53(1)
3.2.3 Controller for Systems with Time Delay
54(2)
3.3 Filter of DMC in the IMC Structure
56(6)
3.3.1 Three Feedback Correction Strategies and Corresponding Filters
56(4)
3.3.2 Influence of the Filter to Robust Stability of the System
60(2)
3.4 DMC Parameter Tuning Based on Trend Analysis
62(10)
3.5 Summary
72(1)
References
73(2)
4 Quantitative Analysis of SISO Unconstrained Predictive Control Systems
75(40)
4.1 Time Domain Analysis Based on the Kleinman Controller
76(5)
4.2 Coefficient Mapping of Predictive Control Systems
81(9)
4.2.1 Controller of GPC in the IMC Structure
81(5)
4.2.2 Minimal Form of the DMC Controller and Uniform Coefficient Mapping
86(4)
4.3 Z Domain Analysis Based on Coefficient Mapping
90(8)
4.3.1 Zero Coefficient Condition and the Deadbeat Property of Predictive Control Systems
90(4)
4.3.2 Reduced Order Property and Stability of Predictive Control Systems
94(4)
4.4 Quantitative Analysis of Predictive Control for Some Typical Systems
98(14)
4.4.1 Quantitative Analysis for First-Order Systems
98(6)
4.4.2 Quantitative Analysis for Second-Order Systems
104(8)
4.5 Summary
112(1)
References
113(2)
5 Predictive Control for MIMO Constrained Systems
115(34)
5.1 Unconstrained DMC for Multivariable Systems
115(8)
5.2 Constrained DMC for Multivariable Systems
123(9)
5.2.1 Formulation of the Constrained Optimization Problem in Multivariable DMC
123(2)
5.2.2 Constrained Optimization Algorithm Based on the Matrix Tearing Technique
125(3)
5.2.3 Constrained Optimization Algorithm Based on QP
128(4)
5.3 Decomposition of Online Optimization for Multivariable Predictive Control
132(14)
5.3.1 Hierarchical Predictive Control Based on Decomposition-Coordination
133(4)
5.3.2 Distributed Predictive Control
137(3)
5.3.3 Decentralized Predictive Control
140(3)
5.3.4 Comparison of Three Decomposition Algorithms
143(3)
5.4 Summary
146(1)
References
147(2)
6 Synthesis of Stable Predictive Controllers
149(32)
6.1 Fundamental Philosophy of the Qualitative Synthesis Theory of Predictive Control
150(13)
6.1.1 Relationships between MPC and Optimal Control
150(2)
6.1.2 Infinite Horizon Approximation of Online Open-Loop Finite Horizon Optimization
152(3)
6.1.3 Recursive Feasibility in Rolling Optimization
155(2)
6.1.4 Preliminary Knowledge
157(6)
6.2 Synthesis of Stable Predictive Controllers
163(11)
6.2.1 Predictive Control with Zero Terminal Constraints
163(2)
6.2.2 Predictive Control with Terminal Cost Functions
165(5)
6.2.3 Predictive Control with Terminal Set Constraints
170(4)
6.3 General Stability Conditions of Predictive Control and Suboptimality Analysis
174(5)
6.3.1 General Stability Conditions of Predictive Control
174(3)
6.3.2 Suboptimality Analysis of Predictive Control
177(2)
6.4 Summary
179(1)
References
179(2)
7 Synthesis of Robust Model Predictive Control
181(50)
7.1 Robust Predictive Control for Systems with Polytopic Uncertainties
181(24)
7.1.1 Synthesis of RMPC Based on Ellipsoidal Invariant Sets
181(6)
7.1.2 Improved RMPC with Parameter-Dependent Lyapunov Functions
187(4)
7.1.3 Synthesis of RMPC with Dual-Mode Control
191(8)
7.1.4 Synthesis of RMPC with Multistep Control Sets
199(6)
7.2 Robust Predictive Control for Systems with Disturbances
205(9)
7.2.1 Synthesis with Disturbance Invariant Sets
205(4)
7.2.2 Synthesis with Mixed H2/H∞ Performances
209(5)
7.3 Strategies for Improving Robust Predictive Controller Design
214(13)
7.3.1 Difficulties for Robust Predictive Controller Synthesis
214(2)
7.3.2 Efficient Robust Predictive Controller
216(4)
7.3.3 Off-Line Design and Online Synthesis
220(3)
7.3.4 Synthesis of the Robust Predictive Controller by QP
223(4)
7.4 Summary
227(1)
References
228(3)
8 Predictive Control for Nonlinear Systems
231(28)
8.1 General Description of Predictive Control for Nonlinear Systems
231(4)
8.2 Predictive Control for Nonlinear Systems Based on Input-Output Linearization
235(6)
8.3 Multiple Model Predictive Control Based on Fuzzy Clustering
241(7)
8.4 Neural Network Predictive Control
248(5)
8.5 Predictive Control for Hammerstein Systems
253(3)
8.6 Summary
256(1)
References
257(2)
9 Comprehensive Development of Predictive Control Algorithms and Strategies
259(38)
9.1 Predictive Control Combined with Advanced Structures
259(8)
9.1.1 Predictive Control with a Feedforward-Feedback Structure
259(3)
9.1.2 Cascade Predictive Control
262(5)
9.2 Alternative Optimization Formulation in Predictive Control
267(10)
9.2.1 Predictive Control with Infinite Norm Optimization
267(3)
9.2.2 Constrained Multiobjective Multidegree of Freedom Optimization and Satisfactory Control
270(7)
9.3 Input Parametrization of Predictive Control
277(4)
9.3.1 Blocking Strategy of Optimization Variables
277(2)
9.3.2 Predictive Functional Control
279(2)
9.4 Aggregation of the Online Optimization Variables in Predictive Control
281(13)
9.4.1 General Framework of Optimization Variable Aggregation in Predictive Control
282(2)
9.4.2 Online Optimization Variable Aggregation with Guaranteed Performances
284(10)
9.5 Summary
294(1)
References
294(3)
10 Applications of Predictive Control
297(56)
10.1 Applications of Predictive Control in Industrial Processes
297(16)
10.1.1 Industrial Application and Software Development of Predictive Control
297(3)
10.1.2 The Role of Predictive Control in Industrial Process Optimization
300(2)
10.1.3 Key Technologies of Predictive Control Implementation
302(6)
10.1.4 QDMC for a Refinery Hydrocracking Unit
308(1)
10.1.4.1 Process Description and Control System Configuration
309(1)
10.1.4.2 Problem Formulation and Variable Selection
310(1)
10.1.4.3 Plant Testing and Model Identification
310(1)
10.1.4.4 Off-Line Simulation and Design
311(1)
10.1.4.5 Online Implementation and Results
312(1)
10.2 Applications of Predictive Control in Other Fields
313(22)
10.2.1 Brief Description of Extension of Predictive Control Applications
313(5)
10.2.2 Online Optimization of a Gas Transportation Network
318(1)
10.2.2.1 Problem Description for Gas Transportation Network Optimization
318(2)
10.2.2.2 Black Box Technique and Online Optimization
320(1)
10.2.2.3 Application Example
321(2)
10.2.2.4 Hierarchical Decomposition for a Large-Scale Network
323(1)
10.2.3 Application of Predictive Control in an Automatic Train Operation System
323(5)
10.2.4 Hierarchical Predictive Control of Urban Traffic Networks
328(1)
10.2.4.1 Two-Level Hierarchical Control Framework
328(1)
10.2.4.2 Upper Level Design
329(2)
10.2.4.3 Lower Level Design
331(1)
10.2.4.4 Example and Scenarios Setting
331(1)
10.2.4.5 Results and Analysis
332(3)
10.3 Embedded Implementation of Predictive Controller with Applications
335(12)
10.3.1 QP Implementation in FPGA with Applications
337(6)
10.3.2 Neural Network QP Implementation in DSP with Applications
343(4)
10.4 Summary
347(4)
References
351(2)
11 Generalization of Predictive Control Principles
353(16)
11.1 Interpretation of Methodological Principles of Predictive Control
353(2)
11.2 Generalization of Predictive Control Principles to General Control Problems
355(12)
11.2.1 Description of Predictive Control Principles in Generalized Form
355(3)
11.2.2 Rolling Job Shop Scheduling in Flexible Manufacturing Systems
358(5)
11.2.3 Robot Rolling Path Planning in an Unknown Environment
363(4)
11.3 Summary
367(1)
References
367(2)
Index 369
Yugeng Xi is a Chair Professor of Shanghai Jiao Tong University (SJTU). He received Dr.-Ing. degree on automatic control from Technical University Munich, Germany in 1984. Since then he has been with the Department of Automation, SJTU. His research interests include predictive control theory and applications, control and optimization of large scale complex systems. He has been working in the area of predictive control for more than 35 years.??



Dewei Li is an Associate Professor of Shanghai Jiao Tong University (SJTU). He received PhD. degree on automatic control from SJTU, China in 2009. From 2011, he has been with the Department of Automation, SJTU. His research interests include predictive control theory and applications, the control of robots, intelligent systems, control and optimization of large scale complex systems. He has been working in the area of predictive control for more than 10 years.
Biežāk uzdotie jautājumi par e-grāmatām Biežāk uzdotie jautājumi par e-grāmatām
Permanent link: https://www.kriso.lv/db/97811191195862e.html
Keywords: