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Predictive Control

Author Dewei Li , Yugeng Xi
Release Date: 2019/11/01
ISBN: 9781119119548
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Book Description

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.

Table of Contents

  1. Cover
  2. Preface
  3. 1 Brief History and Basic Principles of Predictive Control
    1. 1.1 Generation and Development of Predictive Control
    2. 1.2 Basic Methodological Principles of Predictive Control
    3. 1.3 Contents of this Book
    4. References
  4. 2 Some Basic Predictive Control Algorithms
    1. 2.1 Dynamic Matrix Control (DMC) Based on the Step Response Model
    2. 2.2 Generalized Predictive Control (GPC) Based on the Linear Difference Equation Model
    3. 2.3 Predictive Control Based on the State Space Model
    4. 2.4 Summary
    5. References
  5. 3 Trend Analysis and Tuning of SISO Unconstrained DMC Systems
    1. 3.1 The Internal Model Control Structure of the DMC Algorithm
    2. 3.2 Controller of DMC in the IMC Structure
    3. 3.3 Filter of DMC in the IMC Structure
    4. 3.4 DMC Parameter Tuning Based on Trend Analysis
    5. 3.5 Summary
    6. References
  6. 4 Quantitative Analysis of SISO Unconstrained Predictive Control Systems
    1. 4.1 Time Domain Analysis Based on the Kleinman Controller
    2. 4.2 Coefficient Mapping of Predictive Control Systems
    3. 4.3 Z Domain Analysis Based on Coefficient Mapping
    4. 4.4 Quantitative Analysis of Predictive Control for Some Typical Systems
    5. 4.5 Summary
    6. References
  7. 5 Predictive Control for MIMO Constrained Systems
    1. 5.1 Unconstrained DMC for Multivariable Systems
    2. 5.2 Constrained DMC for Multivariable Systems
    3. 5.3 Decomposition of Online Optimization for Multivariable Predictive Control
    4. 5.4 Summary
    5. References
  8. 6 Synthesis of Stable Predictive Controllers
    1. 6.1 Fundamental Philosophy of the Qualitative Synthesis Theory of Predictive Control
    2. 6.2 Synthesis of Stable Predictive Controllers
    3. 6.3 General Stability Conditions of Predictive Control and Suboptimality Analysis
    4. 6.4 Summary
    5. References
  9. 7 Synthesis of Robust Model Predictive Control
    1. 7.1 Robust Predictive Control for Systems with Polytopic Uncertainties
    2. 7.2 Robust Predictive Control for Systems with Disturbances
    3. 7.3 Strategies for Improving Robust Predictive Controller Design
    4. 7.4 Summary
    5. References
  10. 8 Predictive Control for Nonlinear Systems
    1. 8.1 General Description of Predictive Control for Nonlinear Systems
    2. 8.2 Predictive Control for Nonlinear Systems Based on Input–Output Linearization
    3. 8.3 Multiple Model Predictive Control Based on Fuzzy Clustering
    4. 8.4 Neural Network Predictive Control
    5. 8.5 Predictive Control for Hammerstein Systems
    6. 8.6 Summary
    7. References
  11. 9 Comprehensive Development of Predictive Control Algorithms and Strategies
    1. 9.1 Predictive Control Combined with Advanced Structures
    2. 9.2 Alternative Optimization Formulation in Predictive Control
    3. 9.3 Input Parametrization of Predictive Control
    4. 9.4 Aggregation of the Online Optimization Variables in Predictive Control
    5. 9.5 Summary
    6. References
  12. 10 Applications of Predictive Control
    1. 10.1 Applications of Predictive Control in Industrial Processes
    2. 10.2 Applications of Predictive Control in Other Fields
    3. 10.3 Embedded Implementation of Predictive Controller with Applications
    4. 10.4 Summary
    5. References
  13. 11 Generalization of Predictive Control Principles
    1. 11.1 Interpretation of Methodological Principles of Predictive Control
    2. 11.2 Generalization of Predictive Control Principles to General Control Problems
    3. 11.3 Summary
    4. References
  14. Index
  15. End User License Agreement