Includes bibliographical references and index.

"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 has become a cornerstone in the Chinese market Modeling Predictive Control (MPC) is an advanced control technology that can effectively handle optimization control under constraints. Since MPC appeared in the industrial process control field in the 1970's, the demand for constrained optimization control, in particular MPC, in various application fields has been increasing continuously. The MPC application fields extend from traditional oil refinery, petrochemical, and chemical industries, to almost all fields such as power systems, manufacturing, aerospace, electromechanics, urban transportation, agricultural greenhouse, and medicine etc. MPC has the ability to anticipate future events and can take control actions accordingly. PID and LQR controllers do not have this predictive ability. MPC is nearly universally implemented as a digital control, although there is research into achieving faster response times with specially designed analog circuitry"-- Provided by publisher.

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Intro; Title Page; Copyright Page; Contents; Preface; Chapter 1 Brief History and Basic Principles of Predictive Control; 1.1 Generation and Development of Predictive Control; 1.2 Basic Methodological Principles of Predictive Control; 1.2.1 Prediction Model; 1.2.2 Rolling Optimization; 1.2.3 Feedback Correction; 1.3 Contents of this Book; References; Chapter 2 Some Basic Predictive Control Algorithms; 2.1 Dynamic Matrix Control (DMC) Based on the Step Response Model; 2.1.1 DMC Algorithm and Implementation; 2.1.2 Description of DMC in the State Space Framework

2.2 Generalized Predictive Control (GPC) Based on the Linear Difference Equation Model2.3 Predictive Control Based on the State Space Model; 2.4 Summary; References; Chapter 3 Trend Analysis and Tuning of SISO Unconstrained DMC Systems; 3.1 The Internal Model Control Structure of the DMC Algorithm; 3.2 Controller of DMC in the IMC Structure; 3.2.1 Stability of the Controller; 3.2.2 Controller with the One-Step Optimization Strategy; 3.2.3 Controller for Systems with Time Delay; 3.3 Filter of DMC in the IMC Structure; 3.3.1 Three Feedback Correction Strategies and Corresponding Filters

3.3.2 Influence of the Filter to Robust Stability of the System3.4 DMC Parameter Tuning Based on Trend Analysis; 3.5 Summary; References; Chapter 4 Quantitative Analysis of SISO Unconstrained Predictive Control Systems; 4.1 Time Domain Analysis Based on the Kleinman Controller; 4.2 Coefficient Mapping of Predictive Control Systems; 4.2.1 Controller of GPC in the IMC Structure; 4.2.2 Minimal Form of the DMC Controller and Uniform Coefficient Mapping; 4.3 Z Domain Analysis Based on Coefficient Mapping; 4.3.1 Zero Coefficient Condition and the Deadbeat Property of Predictive Control Systems

4.3.2 Reduced Order Property and Stability of Predictive Control Systems4.4 Quantitative Analysis of Predictive Control for Some Typical Systems; 4.4.1 Quantitative Analysis for First-Order Systems; 4.4.2 Quantitative Analysis for Second-Order Systems; 4.5 Summary; References; Chapter 5 Predictive Control for MIMO Constrained Systems; 5.1 Unconstrained DMC for Multivariable Systems; 5.2 Constrained DMC for Multivariable Systems; 5.2.1 Formulation of the Constrained Optimization Problem in Multivariable DMC; 5.2.2 Constrained Optimization Algorithm Based on the Matrix Tearing Technique

5.2.3 Constrained Optimization Algorithm Based on QP5.3 Decomposition of Online Optimization for Multivariable Predictive Control; 5.3.1 Hierarchical Predictive Control Based on Decomposition-Coordination; 5.3.2 Distributed Predictive Control; 5.3.3 Decentralized Predictive Control; 5.3.4 Comparison of Three Decomposition Algorithms; 5.4 Summary; References; Chapter 6 Synthesis of Stable Predictive Controllers; 6.1 Fundamental Philosophy of the Qualitative Synthesis Theory of Predictive Control; 6.1.1 Relationships between MPC and Optimal Control