Model Predictive Control

Model Predictive ControlUnderstand the practical side of controlling industrial processesModel Predictive Control (MPC) is a method for controlling a process according to given parameters, derived in many cases from empirical models. It has been widely applied in industrial units to increase revenue and promoting sustainability. Systematic overviews of this subject, however, are rare, and few draw on direct experience in industrial settings.Assuming basic knowledge of the relevant mathematical and algebraic modeling techniques, the book's title combines foundational theories of MPC with a thorough sense of its practical applications in an industrial context. The result is a presentation uniquely suited to rapid incorporation in an industrial workplace.Model Predictive Control readers will also find: Two-part organization to balance theory and applications Selection of topics directly driven by industrial demand An author with decades of experience in both teaching and industrial practice This book is ideal for industrial control engineers and researchers looking to understand MPC technology, as well as advanced undergraduate and graduate students studying predictive control and related subjects.

Auteur

Baocang Ding, PhD, teaches MPC to both undergraduate and graduate students in the School of Automation, Chongqing University of Posts and Telecommunications, China. His research interests include model predictive control, control of power network, process control, and control software development. Yuanqing Yang, PhD, teaches MPC to both undergraduate and graduate students in the School of Automation, Chongqing University of Posts and Telecommunications, China. His research interests include model predictive control, fuzzy control, networked control, and distributed control systems.

Texte du rabat
Understand the practical side of controlling industrial processes Model Predictive Control (MPC) is a method for controlling a process according to given parameters, derived in many cases from empirical models. It has been widely applied in industrial units to increase revenue and promoting sustainability. Systematic overviews of this subject, however, are rare, and few draw on direct experience in industrial settings. Assuming basic knowledge of the relevant mathematical and algebraic modeling techniques, the book's title combines foundational theories of MPC with a thorough sense of its practical applications in an industrial context. The result is a presentation uniquely suited to rapid incorporation in an industrial workplace. Model Predictive Control readers will also find: Two-part organization to balance theory and applications Selection of topics directly driven by industrial demand * An author with decades of experience in both teaching and industrial practice This book is ideal for industrial control engineers and researchers looking to understand MPC technology, as well as advanced undergraduate and graduate students studying predictive control and related subjects.

Résumé
Model Predictive Control Understand the practical side of controlling industrial processes Model Predictive Control (MPC) is a method for controlling a process according to given parameters, derived in many cases from empirical models. It has been widely applied in industrial units to increase revenue and promoting sustainability. Systematic overviews of this subject, however, are rare, and few draw on direct experience in industrial settings. Assuming basic knowledge of the relevant mathematical and algebraic modeling techniques, the book's title combines foundational theories of MPC with a thorough sense of its practical applications in an industrial context. The result is a presentation uniquely suited to rapid incorporation in an industrial workplace. Model Predictive Control readers will also find: Two-part organization to balance theory and applications Selection of topics directly driven by industrial demand * An author with decades of experience in both teaching and industrial practice This book is ideal for industrial control engineers and researchers looking to understand MPC technology, as well as advanced undergraduate and graduate students studying predictive control and related subjects.

Contenu

About the Authors xi

Preface xiii

Acronyms xv

Introduction xvii

1 Concepts 1

1.1 PID and Model Predictive Control 1

1.2 Two-Layered Model Predictive Control 4

1.3 Hierarchical Model Predictive Control 7

2 Parameter Estimation and Output Prediction 11

2.1 Test Signal for Model Identification 11

2.1.1 Step Test 11

2.1.2 White Noise 11

2.1.3 Pseudo-Random Binary Sequence 13

2.1.4 Generalized Binary Noise 14

2.2 Step Response Model Identification 15

2.2.1 Model 15

2.2.2 Data Processing 17

2.2.2.1 Marking or Interpolation of Bad Data 17

2.2.2.2 Smoothing Data 18

2.2.3 Model Identification 19

2.2.3.1 Case Grouping 19

2.2.3.2 Cased Data Preparation for Stable Dependent Variables 19

2.2.3.3 Cased Data Preparation for Integral Dependent Variables 21

2.2.3.4 Least Square Solution to Parameter Regression 22

2.2.3.5 Least Square Solution by SVD Decomposition 24

2.2.3.6 Filtering Pulse Response Coefficients 24

2.2.4 Numerical Example 27

2.3 Prediction Based on Step Response Model and Kalman Filter 30

2.3.1 Steady-State Kalman Filter and Predictor 31

2.3.2 Steady-State Kalman Filter and Predictor Based on Step Response Model 32

2.3.2.1 Open-Loop Prediction of Stable CV 33

2.3.2.2 Open-Loop Prediction of Integral CV 36

3 Steady-State Target Calculation 39

3.1 RTO and External Target 39

3.2 Economic Optimization and Target Tracking Problem 40

3.2.1 Economic Optimization 41

3.2.1.1 Optimization Problem 41

3.2.1.2 Minimum-Move Problem 42

3.2.2 Target Tracking Problem 46

3.3 Judging Feasibility and Adjusting Soft Constraint 46

3.3.1 Weight Method 47

3.3.1.1 An Illustrative Example 47

3.3.1.2 Weight Method 50

3.3.2 Priority-Rank Method 51

3.3.2.1 Ascending-Number Method 52

3.3.2.2 Descending-Number Method 52

3.3.3 Compromise Between Adjusting Soft Constraints and Economic Optimization 55

4 Two-Layered DMC for Stable Processes 57

4.1 Open-Loop Prediction Module 59

4.2 Steady-State Target Calculation Module 61

4.2.1 Hard and Soft Constraints 61

4.2.2 Priority Rank of Soft Constraints 63

4.2.3 Feasibility Stage 64

4.2.4 Economic Stage 66

4.3 Dynamic Calculation Module 67

4.4 Numerical Example 70

5 Two-Layered DMC for Stable and Integral Processes 73

5.1 Open-Loop Prediction Module 74

5.2 Steady-State Target Calculation Module 77

5.2.1 Hard and Soft Constraints 78

5.2.2 Priority Rank of Soft Constraints 80

5.2.3 Feasibility Stage 81

5.2.4 Economic Stage 83

5.3 Dynamic Calculation Module 85

5.4 Numerical Example 87

6 Two-Layered DMC for State-Space Model 95

6.1 Artificial Disturbance Model 95

6.1.1 Basic Model 96

6.1.2 Controlled Variable as Additional State 97

6.1.3 Manipulated Variable as Additional State 98

6.1.4 Kalman Filter 100

6.2 Open-Loop Prediction Module 103

6.3 Steady-State Target Calculation Module 104

6.3.1 Constraints on Steady-State Perturbation Increment 104

6.3.2 Feasibility Stage 106

6.3.3 Economic Stage Without Soft Constraint 107

6.4 Dynamic Calculation Module 108

6.5 Numerical Example 110

7 Offset-Free, Nonlinearity and Variable Structure in Two-Layered MPC 115

7.1 State Space Steady-State Target Calculation with Target Tracking 115

7.1.1 Case all External Targets Having Equal Importance 117

7.1.2 Case CV External Target Being More Important Than MV External Target 117

7.2 QP-Based Dynamic Control and Offset-Free 119

7.3 Static Nonlinear Transformation 125

7.3.1 Principle of Nonlinear Transformation 125

7.3.2 Usual Nonlinear Trans…