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.
Table of Contents
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 Controllability Properties of the MEC Model 117
3.10 Measuring Hydrogen 121
3.11 Stability Test of the Proposed Controller 123
3.12 Conclusions 128
References 128
4 Comparison of Linear and Nonlinear State Observer Design Algorithms for Monitoring Energy Production in a Microbial Fuel Digester 135
4.1 Introduction 135
4.1.1 Anaerobic Biodigester 138
4.1.2 Key Biodigester Parameters 138
4.2 Biodigester Operation 141
4.2.1 Wet Biodigester 141
4.2.2 Dry Biodigester 142
4.2.3 Continuous Biodigester 142
4.2.4 Semicontinuous Biodigester 143
4.2.5 Anaerobic Digestion Model No 1 143
4.3 State Estimation 147
4.4 Luenberger Observer 148
4.5 Sliding-mode Estimator 149
4.6 Proposed Nonlinear Estimator 152
4.7 Estimator Performance Index 155
4.8 Mathematical Modeling and Steady States 155
4.8.1 Proposed Biodigester Model 156
4.8.2 Stationary States 160
4.8.3 Local Observability Analysis 161
4.8.4 Simulation and Comparison of Estimators 165
4.8.5 Simulation of Disturbance with Sensor Noise 168
4.8.6 Sensor Proposal 171
4.9 Conclusions 175
References 175
5 Optimal Control Approach Applied to a Fed-batch Reactor for Wastewater Treatment Plants 183
5.1 Introduction 183
5.2 Metal and Contaminants’ Removal 184
5.3 Operation Bioreactor 185
5.4 Dynamic Model 186
5.5 Proposed Model 187
5.5.1 Sulfate-reduction Processes 187
5.6 Isolation and Propagation of a Sulfate-reducing Bacteria Consortium 188
5.7 Analytic Methods 189
5.8 Results and Discussion 189
5.8.1 Sulfate-reduction Processes 189
5.8.2 Sensitivity Analysis 194
5.8.3 Optimal Nonlinear Control of Finite Horizon 200
5.8.4 Optimal Control of Finite Horizon for the Bioreactor 201
5.8.5 Experimental System 203
5.9 Conclusion 206
References 207
6 Experimental Implementation of the Dynamic Predictive-based Control to a Coupled Tank System 213
6.1 Introduction 213
6.2 Coupled Tank System Description 214
6.2.1 Mathematical Nonlinear Model 214
6.2.2 Model Linearization 217
6.2.3 Parameter Identification of the Coupled Tank System 219
6.2.4 Implementation of the Recursive Least Square on LabVIEW 221
6.2.5 Discretization of the Dynamic Predictors 224
6.2.6 Gain Tuning and Poles 225
6.2.7 Implementation of the Dynamic Predictive Control on LabVIEW 226
6.3 Experimental Results 228
6.4 Conclusion 229
References 229
7 Temperature Robust Control Applied to a Tomato Dehydrator with the CLKF Approach 233
7.1 Introduction 233
7.2 Dehydrator: Modeling and Description 234
7.2.1 Description 235
7.2.2 Mathematical Model 236
7.3 Control Synthesis 241
7.4 System Parameters 244
7.5 Experimental Results 246
7.6 Wi-Fi Monitoring System 250
7.7 Conclusions 256
References 256
8 Design of an Adaptive Robust Controller: Temperature Regulation of a Heat Exchanger Prototype 261
8.1 Introduction 261
8.2 Methodology 263
8.3 Robust and Adaptive Control Design 263
8.4 Representation of the Control System 264
8.5 Robust P and PI Control Law Design 265
8.6 Adaptative P and PI Control Law Design 269
8.7 Experimental Results 276
8.8 MATLAB Code 276
8.9 Experimental Platform and Identification 278
8.10 Identification by Least Squares 282
8.11 Additional Tools 284
8.12 Robust P and PI Control 285
8.13 Adaptative Robust P and PI Control 287
8.14 Conclusions 290
References 291
Credits 293
Acronyms 295
Index 299
