Advanced Control Methods for Industrial Processes - López-Pérez, Pablo A.

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Résumé :

A detailed introduction to mathematical models for new and established control engineers

Control engineering is a system that helps us understand electrical, physical, chemical, and biochemical systems through the use of mathematical modeling, using inputs, outputs, and simulations. These experimental platforms are implemented in most systems of modern advanced control engineering.

Advanced Control Methods for Industrial Processes provides a solid grounding in traditional control techniques. It emphasizes practical application methods alongside the underlying theory and core instrumentation. Each chapter discusses the full profile of the technology covered, from the field layer and control layer to its implementation. It also includes the interfaces for advanced control systems: between controllers and systems theory, between different layers, and between operators-systems. Through an emphasis on the practical issues of components, devices, and hardware circuits, the book offers working principles and operation mechanisms that allow an engineer to put theory into practice for the advanced control techniques.

Advanced Control Methods for Industrial Processes readers will also find:

• A practical overview on advanced control methods applied to real-time and in-silico systems
• Specific parameters, install procedures, calibration and configuration methodologies necessary to conduct the relevant models
• Clear insights into the necessary mathematical models
• Tutorial material to facilitate the understanding of core concepts

Advanced Control Methods for Industrial Processes is an ideal companion for process engineers, control engineers, and chemists in industry....

Biographie:
the detailed introduction of mathematical models and included tutorial material is essential for every control engineer....

Sommaire:

Preface xi

Acknowledgments xv

Part I Classical and Advanced Control Theory: Simulation and Examples 1

1 Field Elements of Classic Control Systems 3

1.1 The Principles of Control (Industry 5.0) 3

1.2 Field Elements of Classic and Modern Control Systems 6

1.2.1 Advantages 7

1.2.2 Disadvantages 7

1.2.3 Why Control and Monitor? 7

1.3 Process Modeling in Control Systems Design 11

1.4 Ordinary Differential Equations and Laplace 13

1.5 Linear Systems 18

1.6 Nonlinear Dynamical Systems 20

1.7 Stability Theory 21

1.8 Systems' Identification 23

1.8.1 Recursive Least Squares Method (Applied to Chapter 8) 23

1.8.2 Parameter Identification 25

1.8.3 Ordinary Least Squares 25

1.8.4 Recursive Least Squares (Applied to Chapter 6) 26

1.9 General Methodology Based on Recursive Least Squares for Nonlinear Systems 28

1.10 Optimal Controllers 33

1.10.1 Linear Quadratic Regulator 33

1.10.2 Optimal PI 36

1.10.3 Pontryagin Maximum Principle 44

1.11 Observer-based Controllers 45

1.12 Examples of Modeling, Simulation, and Practical Platforms for Industrial Processes 46

1.12.1 LabVIEW 47

1.13 Sensors 48

1.13.1 Esp 32 48

1.13.2 Specifications 49

1.13.3 Sensor Infrastructure 49

1.14 Module MQ 50

1.15 Sensor Operation 50

1.15.1 Sensor Calibration 51

1.15.2 Methane Sensor Programming Codes 52

1.15.3 Carbon Dioxide Sensor Programming 54

1.15.4 Carbon Dioxide Vernier Probe Programming 56

1.15.5 MATLAB Function 56

References 57

2 Advanced Control Theory Fundamentals 63

2.1 Nonlinear Controllers and Advanced Control Theory 63

2.2 Nonlinear Control 67

2.3 Accessibility Rank Condition 67

2.4 Steady-output Controllability 69

2.5 Controllable and Reachable Subspaces 70

2.6 Controllable Matrix Test 70

2.7 Eigenvector Test for Controllability 70

2.8 Popov-Belevitch-Hautus 71

2.9 Lyapunov Test for Controllability 71

2.10 Sliding-mode Control Systems 71

2.10.1 Sliding surface design 72

2.10.2 Control Law First-order SMC 73

2.11 Filippov's 73

2.12 Lyapunov Method 74

2.13 Sontag Universal Formula 74

2.14 Control of Industrial Time-delay Systems 77

2.14.1 Delayed Systems 77

2.14.2 Extension of LCF to Time-delay Systems 79

2.15 Linear Time Systems with Delays and the Predictive Control Scheme 83

2.15.1 LTI System with Input Delay 83

2.15.2 Predictive Control for Systems with Input Delay 83

2.15.3 LTIS with State Delay 84

2.15.4 LTIS with State Delay and Input Delay 87

2.15.5 Prediction-based Control for LTIS with State Delay and Input Delay 87

2.15.6 Dynamic Predictor-based Control for LTIS with State Delay and Input Delay 88

2.15.7 Linear Systems with State Delay and Two Input Delays 89

2.15.8 Predictor-based Control for LTIS with State Delay and Two Input Delays 89

2.15.9 Dynamic Predictor-based Control for Linear Systems with Both State Delay and Two Input Delays 92

References 93

Part II Advanced Control Methods for Industrial Process 99

3 Design of a Nonlinear Controller to Regulate Hydrogen Production in a Microbial Electrolysis Cell 101

3.1 Introduction 101

3.2 Mathematical Models 104

3.3 Bioprocess Modeling 105

3.3.1 Unstructured Kinetic Models 105

3.4 MEC Modeling 106

3.5 Control Preliminaries 109

3.6 Methodology 111

3.7 Results and Discussion 111

3.8 System Model 114

3.9 Local Contr...