Advanced Control Methods for Industrial Processes

Pablo Antonio López-Pérez, Universidad Autónoma del Estado de Hidalgo, México.Omar Jacobo Santos Sánchez, Universidad Autónoma del Estado de Hidalgo, México.Liliam Rodríguez Guerrero, Universidad Autónoma del Estado de Hidalgo, México.Patricio Ordaz, Universidad Autónoma del Estado de Hidalgo, México.

• Preface xiAcknowledgments xvPart I Classical and Advanced Control Theory: Simulation and Examples 11 Field Elements of Classic Control Systems 31.1 The Principles of Control (Industry 5.0) 31.2 Field Elements of Classic and Modern Control Systems 61.2.1 Advantages 71.2.2 Disadvantages 71.2.3 Why Control and Monitor? 71.3 Process Modeling in Control Systems Design 111.4 Ordinary Differential Equations and Laplace 131.5 Linear Systems 181.6 Nonlinear Dynamical Systems 201.7 Stability Theory 211.8 Systems’ Identification 231.8.1 Recursive Least Squares Method (Applied to Chapter 8) 231.8.2 Parameter Identification 251.8.3 Ordinary Least Squares 251.8.4 Recursive Least Squares (Applied to Chapter 6) 261.9 General Methodology Based on Recursive Least Squares for Nonlinear Systems 281.10 Optimal Controllers 331.10.1 Linear Quadratic Regulator 331.10.2 Optimal PI 361.10.3 Pontryagin Maximum Principle 441.11 Observer-based Controllers 451.12 Examples of Modeling, Simulation, and Practical Platforms for Industrial Processes 461.12.1 LabVIEW 471.13 Sensors 481.13.1 Esp 32 481.13.2 Specifications 491.13.3 Sensor Infrastructure 491.14 Module MQ 501.15 Sensor Operation 501.15.1 Sensor Calibration 511.15.2 Methane Sensor Programming Codes 521.15.3 Carbon Dioxide Sensor Programming 541.15.4 Carbon Dioxide Vernier Probe Programming 561.15.5 MATLAB Function 56References 572 Advanced Control Theory Fundamentals 632.1 Nonlinear Controllers and Advanced Control Theory 632.2 Nonlinear Control 672.3 Accessibility Rank Condition 672.4 Steady-output Controllability 692.5 Controllable and Reachable Subspaces 702.6 Controllable Matrix Test 702.7 Eigenvector Test for Controllability 702.8 Popov–Belevitch–Hautus 712.9 Lyapunov Test for Controllability 712.10 Sliding-mode Control Systems 712.10.1 Sliding surface design 722.10.2 Control Law First-order SMC 732.11 Filippov’s 732.12 Lyapunov Method 742.13 Sontag Universal Formula 742.14 Control of Industrial Time-delay Systems 772.14.1 Delayed Systems 772.14.2 Extension of LCF to Time-delay Systems 792.15 Linear Time Systems with Delays and the Predictive Control Scheme 832.15.1 LTI System with Input Delay 832.15.2 Predictive Control for Systems with Input Delay 832.15.3 LTIS with State Delay 842.15.4 LTIS with State Delay and Input Delay 872.15.5 Prediction-based Control for LTIS with State Delay and Input Delay 872.15.6 Dynamic Predictor-based Control for LTIS with State Delay and Input Delay 882.15.7 Linear Systems with State Delay and Two Input Delays 892.15.8 Predictor-based Control for LTIS with State Delay and Two Input Delays 892.15.9 Dynamic Predictor-based Control for Linear Systems with Both State Delay and Two Input Delays 92References 93Part II Advanced Control Methods for Industrial Process 993 Design of a Nonlinear Controller to Regulate Hydrogen Production in a Microbial Electrolysis Cell 1013.1 Introduction 1013.2 Mathematical Models 1043.3 Bioprocess Modeling 1053.3.1 Unstructured Kinetic Models 1053.4 MEC Modeling 1063.5 Control Preliminaries 1093.6 Methodology 1113.7 Results and Discussion 1113.8 System Model 1143.9 Local Controllability Properties of the MEC Model 1173.10 Measuring Hydrogen 1213.11 Stability Test of the Proposed Controller 1233.12 Conclusions 128References 1284 Comparison of Linear and Nonlinear State Observer Design Algorithms for Monitoring Energy Production in a Microbial Fuel Digester 1354.1 Introduction 1354.1.1 Anaerobic Biodigester 1384.1.2 Key Biodigester Parameters 1384.2 Biodigester Operation 1414.2.1 Wet Biodigester 1414.2.2 Dry Biodigester 1424.2.3 Continuous Biodigester 1424.2.4 Semicontinuous Biodigester 1434.2.5 Anaerobic Digestion Model No. 1 1434.3 State Estimation 1474.4 Luenberger Observer 1484.5 Sliding-mode Estimator 1494.6 Proposed Nonlinear Estimator 1524.7 Estimator Performance Index 1554.8 Mathematical Modeling and Steady States 1554.8.1 Proposed Biodigester Model 1564.8.2 Stationary States 1604.8.3 Local Observability Analysis 1614.8.4 Simulation and Comparison of Estimators 1654.8.5 Simulation of Disturbance with Sensor Noise 1684.8.6 Sensor Proposal 1714.9 Conclusions 175References 1755 Optimal Control Approach Applied to a Fed-batch Reactor for Wastewater Treatment Plants 1835.1 Introduction 1835.2 Metal and Contaminants’ Removal 1845.3 Operation Bioreactor 1855.4 Dynamic Model 1865.5 Proposed Model 1875.5.1 Sulfate-reduction Processes 1875.6 Isolation and Propagation of a Sulfate-reducing Bacteria Consortium 1885.7 Analytic Methods 1895.8 Results and Discussion 1895.8.1 Sulfate-reduction Processes 1895.8.2 Sensitivity Analysis 1945.8.3 Optimal Nonlinear Control of Finite Horizon 2005.8.4 Optimal Control of Finite Horizon for the Bioreactor 2015.8.5 Experimental System 2035.9 Conclusion 206References 2076 Experimental Implementation of the Dynamic Predictive-based Control to a Coupled Tank System 2136.1 Introduction 2136.2 Coupled Tank System Description 2146.2.1 Mathematical Nonlinear Model 2146.2.2 Model Linearization 2176.2.3 Parameter Identification of the Coupled Tank System 2196.2.4 Implementation of the Recursive Least Square on LabVIEW 2216.2.5 Discretization of the Dynamic Predictors 2246.2.6 Gain Tuning and Poles 2256.2.7 Implementation of the Dynamic Predictive Control on LabVIEW 2266.3 Experimental Results 2286.4 Conclusion 229References 2297 Temperature Robust Control Applied to a Tomato Dehydrator with the CLKF Approach 2337.1 Introduction 2337.2 Dehydrator: Modeling and Description 2347.2.1 Description 2357.2.2 Mathematical Model 2367.3 Control Synthesis 2417.4 System Parameters 2447.5 Experimental Results 2467.6 Wi-Fi Monitoring System 2507.7 Conclusions 256References 2568 Design of an Adaptive Robust Controller: Temperature Regulation of a Heat Exchanger Prototype 2618.1 Introduction 2618.2 Methodology 2638.3 Robust and Adaptive Control Design 2638.4 Representation of the Control System 2648.5 Robust P and PI Control Law Design 2658.6 Adaptative P and PI Control Law Design 2698.7 Experimental Results 2768.8 MATLAB Code 2768.9 Experimental Platform and Identification 2788.10 Identification by Least Squares 2828.11 Additional Tools 2848.12 Robust P and PI Control 2858.13 Adaptative Robust P and PI Control 2878.14 Conclusions 290References 291Credits 293Acronyms 295Index 299