This book presents the most important methods used for the design of digital controls implemented in industrial applications. The best modelling  and identification techniques for dynamical systems are presented as well as the algorithms for the implementation of the modern solutions of process control. The proposed described methods are illustrated by various case studies for the main industrial sectors There exist a number of books related each one to a single type of control, yet usually without comparisons for various industrial sectors. Some other books present modelling and identification methods or signal processing. This book presents the methods to solve all the problems linked to the design of a process control without the need to find additional information.

Table of Contents:
Preface ix List of Acronyms and Notations xi Chapter 1 Introduction – Models and Dynamic Systems 1 1.1 Overview 1 1.2 Industrial process modeling 3 1.3 Model classes 5 1.3.1 State space models 5 1.3.2 Input–output models 12 Chapter 2 Linear Identification of Closed-Loop Systems 21 2.1 Overview of system identification 21 2.2 Framework 22 2.3 Preliminary identification of a CL process 27 2.3.1 Multivariable linear identification methods 27 2.3.2 Estimation of linear MIMO models using the LSM 30 2.3.3 Identifying CL processes using the MV-LSM 35 2.4 CLOE class of identification methods 39 2.4.1 Principle of CLOE methods 39 2.4.2 Basic CLOE method 41 2.4.3 Weighted CLOE method 46 2.4.4 Filtered CLOE method or adaptively filtered CLOE 56 2.4.5 Extended CLOE method 58 2.4.6 Generalized CLOE method 65 2.4.7 CLOE methods for systems with integrator 74 2.4.8 On the validation of CLOE identified models 78 2.5 Application: identification of active suspension 80 Chapter 3 Digital Control Design Using Pole Placement 93 3.1 Digital proportional-integral-derivative algorithm control 93 3.2 Digital polynomial RST control 96 3.3 RST control by pole placement 98 3.3.1 RST control for regulation dynamics 99 3.3.2 RST polynomial control for tracking dynamics (setpoint change) 100 3.3.3 RST control with independent objectives in tracking and regulation 101 3.4 Predictive RST control 104 3.4.1 Finite horizon predictive control 105 3.4.2 Predictive control with unitary horizon 107 Chapter 4 Adaptive Control and Robust Control 113 4.1 Adaptive polynomial control systems 113 4.1.1 Estimation of the parameters for closed-loop systems 114 4.1.2 Design of the adaptive control 115 4.2 Robust polynomial control systems 117 4.2.1 Robustness of closed-loop systems 118 4.2.2 Studying the stability–robustness connection 121 4.2.3 Study of the nonlinearity–robustness connection 123 4.2.4 Study of the performance–robustness connection 124 4.2.5 Analysis of robustness in the study of the sensitivity function 125 4.2.6 Design of the robust RST control 127 4.2.7 Calibrating the sensitivity function 128 Chapter 5 Multimodel Control 131 5.1 Construction of multimodels 132 5.1.1 Fuzzy logic: Mamdani models 132 5.1.2 Identification from input–output data: direct method 138 5.1.3 Identification from input–output data: neural approach 139 5.1.4 Linearization around various operating points 141 5.1.5 Convex polytopic transformation from an analytical model refined for the command 141 5.1.6 Calculation of the validity of base models 143 5.2 Stabilization and control of multimodels 144 5.3 Design of multimodel command: fuzzy approach 144 5.4 Trajectory tracking 145 Chapter 6 Ill-Defined and/or Uncertain Systems 147 6.1 Study of the stability of nonlinear systems from vector norms 147 6.1.1 Vector norms 147 6.1.2 Comparison systems and overvaluing systems 148 6.1.3 Determination of attractors 153 6.1.4 Nested attractors [GHA 15a] 156 6.2 Adaptation of control 156 6.2.1 Minimizing the size of attractors: direct approach 156 6.2.2 Minimizing the size of attractors by metaheuristics 157 6.3 Overvaluation of the maximum error for various applications 157 6.3.1 Control of nonlinear systems by pole placement 157 6.3.2 Diffeomorphism command of nonlinear processes 159 6.3.3 Determining the attractor for Lur’e Postnikov type processes [GHA 14] 161 6.3.4 Minimizing the attractor through tabu search 165 6.4 Fuzzy secondary loop control 171 Chapter 7 Modeling and Control of an Elementary Industrial Process 173 7.1 Modeling and control of fluid transfer processes 173 7.1.1 Modeling fluid flow processes 173 7.1.2 Designing flow control systems 178 7.2 Modeling and controlling liquid storage processes 180 7.2.1 Constant output flow 181 7.2.2 Variable output flow 183 7.2.3 Designing liquid level control systems 185 7.3 Modeling and controlling the storage process of a pneumatic capacitor 187 7.3.1 Modeling a pneumatic capacitor 187 7.3.2 Designing pneumatic capacitor control systems 190 7.4 Modeling and controlling heat transfer processes 191 7.4.1 Modeling a thermal transfer process 191 7.4.2 Designing temperature control systems 194 7.5 Modeling and control of component transfer processes 195 7.5.1 Modeling a chemical mixing process without reaction 195 7.5.2 Modeling a chemical reaction process 198 7.5.3 Designing systems for controlling the concentration of chemical components 200 Chapter 8 Industrial Applications – Case Studies 203 8.1 Digital control for an installation of air heating in a steel plant 203 8.1.1 Automation solution and design of the control algorithms 204 8.1.2 Optimization of the combustion process 207 8.2 Control and optimization of an ethylene installation 210 8.2.1 Automation solution and designing the control algorithms 211 8.2.2 Optimizing the pyrolysis process 217 8.3 Digital control of a thermoenergy plant 219 8.3.1 Solving the problem of automation of a thermal operating point 220 8.3.2 Optimization of thermal transfer and agent product 225 8.4 Extremal control of a photovoltaic installation 226 8.4.1 Extremal control of a photovoltaic panel 237 Appendix A 243 Appendix B 249 Appendix C 257 Appendix D 261 Bibliography 271 Index 279