Dumitru POPESCU, University Professor, Mathematician, PhD Engineer. Amira GHARBI, Assistant Professor at the Ecole Nationale d'Ingénieurs de Carthage.Dan STEFANOIU, Professor at Politehnica University of Bucharest, Romania.Pierre BORNE, Professor, Ecole Centrale de Lille, Villeneuve d'Ascq Cedex, France.

• Preface ixList of Acronyms and Notations xiChapter 1 Introduction – Models and Dynamic Systems 11.1 Overview 11.2 Industrial process modeling 31.3 Model classes 51.3.1 State space models 51.3.2 Input–output models 12Chapter 2 Linear Identification of Closed-Loop Systems 212.1 Overview of system identification 212.2 Framework 222.3 Preliminary identification of a CL process 272.3.1 Multivariable linear identification methods 272.3.2 Estimation of linear MIMO models using the LSM 302.3.3 Identifying CL processes using the MV-LSM 352.4 CLOE class of identification methods 392.4.1 Principle of CLOE methods 392.4.2 Basic CLOE method 412.4.3 Weighted CLOE method 462.4.4 Filtered CLOE method or adaptively filtered CLOE 562.4.5 Extended CLOE method 582.4.6 Generalized CLOE method 652.4.7 CLOE methods for systems with integrator 742.4.8 On the validation of CLOE identified models 782.5 Application: identification of active suspension 80Chapter 3 Digital Control Design Using Pole Placement 933.1 Digital proportional-integral-derivative algorithm control 933.2 Digital polynomial RST control 963.3 RST control by pole placement 983.3.1 RST control for regulation dynamics 993.3.2 RST polynomial control for tracking dynamics (setpoint change) 1003.3.3 RST control with independent objectives in tracking and regulation 1013.4 Predictive RST control 1043.4.1 Finite horizon predictive control 1053.4.2 Predictive control with unitary horizon 107Chapter 4 Adaptive Control and Robust Control 1134.1 Adaptive polynomial control systems 1134.1.1 Estimation of the parameters for closed-loop systems 1144.1.2 Design of the adaptive control 1154.2 Robust polynomial control systems 1174.2.1 Robustness of closed-loop systems 1184.2.2 Studying the stability–robustness connection 1214.2.3 Study of the nonlinearity–robustness connection 1234.2.4 Study of the performance–robustness connection 1244.2.5 Analysis of robustness in the study of the sensitivity function 1254.2.6 Design of the robust RST control 1274.2.7 Calibrating the sensitivity function 128Chapter 5 Multimodel Control 1315.1 Construction of multimodels 1325.1.1 Fuzzy logic: Mamdani models 1325.1.2 Identification from input–output data: direct method 1385.1.3 Identification from input–output data: neural approach 1395.1.4 Linearization around various operating points 1415.1.5 Convex polytopic transformation from an analytical model refined for the command 1415.1.6 Calculation of the validity of base models 1435.2 Stabilization and control of multimodels 1445.3 Design of multimodel command: fuzzy approach 1445.4 Trajectory tracking 145Chapter 6 Ill-Defined and/or Uncertain Systems 1476.1 Study of the stability of nonlinear systems from vector norms 1476.1.1 Vector norms 1476.1.2 Comparison systems and overvaluing systems 1486.1.3 Determination of attractors 1536.1.4 Nested attractors [GHA 15a] 1566.2 Adaptation of control 1566.2.1 Minimizing the size of attractors: direct approach 1566.2.2 Minimizing the size of attractors by metaheuristics 1576.3 Overvaluation of the maximum error for various applications 1576.3.1 Control of nonlinear systems by pole placement 1576.3.2 Diffeomorphism command of nonlinear processes 1596.3.3 Determining the attractor for Lur’e Postnikov type processes [GHA 14] 1616.3.4 Minimizing the attractor through tabu search 1656.4 Fuzzy secondary loop control 171Chapter 7 Modeling and Control of an Elementary Industrial Process 1737.1 Modeling and control of fluid transfer processes 1737.1.1 Modeling fluid flow processes 1737.1.2 Designing flow control systems 1787.2 Modeling and controlling liquid storage processes 1807.2.1 Constant output flow 1817.2.2 Variable output flow 1837.2.3 Designing liquid level control systems 1857.3 Modeling and controlling the storage process of a pneumatic capacitor 1877.3.1 Modeling a pneumatic capacitor 1877.3.2 Designing pneumatic capacitor control systems 1907.4 Modeling and controlling heat transfer processes 1917.4.1 Modeling a thermal transfer process 1917.4.2 Designing temperature control systems 1947.5 Modeling and control of component transfer processes 1957.5.1 Modeling a chemical mixing process without reaction 1957.5.2 Modeling a chemical reaction process 1987.5.3 Designing systems for controlling the concentration of chemical components 200Chapter 8 Industrial Applications – Case Studies 2038.1 Digital control for an installation of air heating in a steel plant 2038.1.1 Automation solution and design of the control algorithms 2048.1.2 Optimization of the combustion process 2078.2 Control and optimization of an ethylene installation 2108.2.1 Automation solution and designing the control algorithms 2118.2.2 Optimizing the pyrolysis process 2178.3 Digital control of a thermoenergy plant 2198.3.1 Solving the problem of automation of a thermal operating point 2208.3.2 Optimization of thermal transfer and agent product 2258.4 Extremal control of a photovoltaic installation 2268.4.1 Extremal control of a photovoltaic panel 237Appendix A 243Appendix B 249Appendix C 257Appendix D 261Bibliography 271Index 279

Dumitru POPESCU, University Professor, Mathematician, PhD Engineer. Amira GHARBI, Assistant Professor at the Ecole Nationale d'Ingénieurs de Carthage.Dan STEFANOIU, Professor at Politehnica University of Bucharest, Romania.Pierre BORNE, Professor, Ecole Centrale de Lille, Villeneuve d'Ascq Cedex, France.

• Preface ixList of Acronyms and Notations xiChapter 1 Introduction – Models and Dynamic Systems 11.1 Overview 11.2 Industrial process modeling 31.3 Model classes 51.3.1 State space models 51.3.2 Input–output models 12Chapter 2 Linear Identification of Closed-Loop Systems 212.1 Overview of system identification 212.2 Framework 222.3 Preliminary identification of a CL process 272.3.1 Multivariable linear identification methods 272.3.2 Estimation of linear MIMO models using the LSM 302.3.3 Identifying CL processes using the MV-LSM 352.4 CLOE class of identification methods 392.4.1 Principle of CLOE methods 392.4.2 Basic CLOE method 412.4.3 Weighted CLOE method 462.4.4 Filtered CLOE method or adaptively filtered CLOE 562.4.5 Extended CLOE method 582.4.6 Generalized CLOE method 652.4.7 CLOE methods for systems with integrator 742.4.8 On the validation of CLOE identified models 782.5 Application: identification of active suspension 80Chapter 3 Digital Control Design Using Pole Placement 933.1 Digital proportional-integral-derivative algorithm control 933.2 Digital polynomial RST control 963.3 RST control by pole placement 983.3.1 RST control for regulation dynamics 993.3.2 RST polynomial control for tracking dynamics (setpoint change) 1003.3.3 RST control with independent objectives in tracking and regulation 1013.4 Predictive RST control 1043.4.1 Finite horizon predictive control 1053.4.2 Predictive control with unitary horizon 107Chapter 4 Adaptive Control and Robust Control 1134.1 Adaptive polynomial control systems 1134.1.1 Estimation of the parameters for closed-loop systems 1144.1.2 Design of the adaptive control 1154.2 Robust polynomial control systems 1174.2.1 Robustness of closed-loop systems 1184.2.2 Studying the stability–robustness connection 1214.2.3 Study of the nonlinearity–robustness connection 1234.2.4 Study of the performance–robustness connection 1244.2.5 Analysis of robustness in the study of the sensitivity function 1254.2.6 Design of the robust RST control 1274.2.7 Calibrating the sensitivity function 128Chapter 5 Multimodel Control 1315.1 Construction of multimodels 1325.1.1 Fuzzy logic: Mamdani models 1325.1.2 Identification from input–output data: direct method 1385.1.3 Identification from input–output data: neural approach 1395.1.4 Linearization around various operating points 1415.1.5 Convex polytopic transformation from an analytical model refined for the command 1415.1.6 Calculation of the validity of base models 1435.2 Stabilization and control of multimodels 1445.3 Design of multimodel command: fuzzy approach 1445.4 Trajectory tracking 145Chapter 6 Ill-Defined and/or Uncertain Systems 1476.1 Study of the stability of nonlinear systems from vector norms 1476.1.1 Vector norms 1476.1.2 Comparison systems and overvaluing systems 1486.1.3 Determination of attractors 1536.1.4 Nested attractors [GHA 15a] 1566.2 Adaptation of control 1566.2.1 Minimizing the size of attractors: direct approach 1566.2.2 Minimizing the size of attractors by metaheuristics 1576.3 Overvaluation of the maximum error for various applications 1576.3.1 Control of nonlinear systems by pole placement 1576.3.2 Diffeomorphism command of nonlinear processes 1596.3.3 Determining the attractor for Lur’e Postnikov type processes [GHA 14] 1616.3.4 Minimizing the attractor through tabu search 1656.4 Fuzzy secondary loop control 171Chapter 7 Modeling and Control of an Elementary Industrial Process 1737.1 Modeling and control of fluid transfer processes 1737.1.1 Modeling fluid flow processes 1737.1.2 Designing flow control systems 1787.2 Modeling and controlling liquid storage processes 1807.2.1 Constant output flow 1817.2.2 Variable output flow 1837.2.3 Designing liquid level control systems 1857.3 Modeling and controlling the storage process of a pneumatic capacitor 1877.3.1 Modeling a pneumatic capacitor 1877.3.2 Designing pneumatic capacitor control systems 1907.4 Modeling and controlling heat transfer processes 1917.4.1 Modeling a thermal transfer process 1917.4.2 Designing temperature control systems 1947.5 Modeling and control of component transfer processes 1957.5.1 Modeling a chemical mixing process without reaction 1957.5.2 Modeling a chemical reaction process 1987.5.3 Designing systems for controlling the concentration of chemical components 200Chapter 8 Industrial Applications – Case Studies 2038.1 Digital control for an installation of air heating in a steel plant 2038.1.1 Automation solution and design of the control algorithms 2048.1.2 Optimization of the combustion process 2078.2 Control and optimization of an ethylene installation 2108.2.1 Automation solution and designing the control algorithms 2118.2.2 Optimizing the pyrolysis process 2178.3 Digital control of a thermoenergy plant 2198.3.1 Solving the problem of automation of a thermal operating point 2208.3.2 Optimization of thermal transfer and agent product 2258.4 Extremal control of a photovoltaic installation 2268.4.1 Extremal control of a photovoltaic panel 237Appendix A 243Appendix B 249Appendix C 257Appendix D 261Bibliography 271Index 279

Dumitru POPESCU, University Professor, Mathematician, PhD Engineer. Amira GHARBI, Assistant Professor at the Ecole Nationale d'Ingénieurs de Carthage.Dan STEFANOIU, Professor at Politehnica University of Bucharest, Romania.Pierre BORNE, Professor, Ecole Centrale de Lille, Villeneuve d'Ascq Cedex, France.

• Preface ixList of Acronyms and Notations xiChapter 1 Introduction – Models and Dynamic Systems 11.1 Overview 11.2 Industrial process modeling 31.3 Model classes 51.3.1 State space models 51.3.2 Input–output models 12Chapter 2 Linear Identification of Closed-Loop Systems 212.1 Overview of system identification 212.2 Framework 222.3 Preliminary identification of a CL process 272.3.1 Multivariable linear identification methods 272.3.2 Estimation of linear MIMO models using the LSM 302.3.3 Identifying CL processes using the MV-LSM 352.4 CLOE class of identification methods 392.4.1 Principle of CLOE methods 392.4.2 Basic CLOE method 412.4.3 Weighted CLOE method 462.4.4 Filtered CLOE method or adaptively filtered CLOE 562.4.5 Extended CLOE method 582.4.6 Generalized CLOE method 652.4.7 CLOE methods for systems with integrator 742.4.8 On the validation of CLOE identified models 782.5 Application: identification of active suspension 80Chapter 3 Digital Control Design Using Pole Placement 933.1 Digital proportional-integral-derivative algorithm control 933.2 Digital polynomial RST control 963.3 RST control by pole placement 983.3.1 RST control for regulation dynamics 993.3.2 RST polynomial control for tracking dynamics (setpoint change) 1003.3.3 RST control with independent objectives in tracking and regulation 1013.4 Predictive RST control 1043.4.1 Finite horizon predictive control 1053.4.2 Predictive control with unitary horizon 107Chapter 4 Adaptive Control and Robust Control 1134.1 Adaptive polynomial control systems 1134.1.1 Estimation of the parameters for closed-loop systems 1144.1.2 Design of the adaptive control 1154.2 Robust polynomial control systems 1174.2.1 Robustness of closed-loop systems 1184.2.2 Studying the stability–robustness connection 1214.2.3 Study of the nonlinearity–robustness connection 1234.2.4 Study of the performance–robustness connection 1244.2.5 Analysis of robustness in the study of the sensitivity function 1254.2.6 Design of the robust RST control 1274.2.7 Calibrating the sensitivity function 128Chapter 5 Multimodel Control 1315.1 Construction of multimodels 1325.1.1 Fuzzy logic: Mamdani models 1325.1.2 Identification from input–output data: direct method 1385.1.3 Identification from input–output data: neural approach 1395.1.4 Linearization around various operating points 1415.1.5 Convex polytopic transformation from an analytical model refined for the command 1415.1.6 Calculation of the validity of base models 1435.2 Stabilization and control of multimodels 1445.3 Design of multimodel command: fuzzy approach 1445.4 Trajectory tracking 145Chapter 6 Ill-Defined and/or Uncertain Systems 1476.1 Study of the stability of nonlinear systems from vector norms 1476.1.1 Vector norms 1476.1.2 Comparison systems and overvaluing systems 1486.1.3 Determination of attractors 1536.1.4 Nested attractors [GHA 15a] 1566.2 Adaptation of control 1566.2.1 Minimizing the size of attractors: direct approach 1566.2.2 Minimizing the size of attractors by metaheuristics 1576.3 Overvaluation of the maximum error for various applications 1576.3.1 Control of nonlinear systems by pole placement 1576.3.2 Diffeomorphism command of nonlinear processes 1596.3.3 Determining the attractor for Lur’e Postnikov type processes [GHA 14] 1616.3.4 Minimizing the attractor through tabu search 1656.4 Fuzzy secondary loop control 171Chapter 7 Modeling and Control of an Elementary Industrial Process 1737.1 Modeling and control of fluid transfer processes 1737.1.1 Modeling fluid flow processes 1737.1.2 Designing flow control systems 1787.2 Modeling and controlling liquid storage processes 1807.2.1 Constant output flow 1817.2.2 Variable output flow 1837.2.3 Designing liquid level control systems 1857.3 Modeling and controlling the storage process of a pneumatic capacitor 1877.3.1 Modeling a pneumatic capacitor 1877.3.2 Designing pneumatic capacitor control systems 1907.4 Modeling and controlling heat transfer processes 1917.4.1 Modeling a thermal transfer process 1917.4.2 Designing temperature control systems 1947.5 Modeling and control of component transfer processes 1957.5.1 Modeling a chemical mixing process without reaction 1957.5.2 Modeling a chemical reaction process 1987.5.3 Designing systems for controlling the concentration of chemical components 200Chapter 8 Industrial Applications – Case Studies 2038.1 Digital control for an installation of air heating in a steel plant 2038.1.1 Automation solution and design of the control algorithms 2048.1.2 Optimization of the combustion process 2078.2 Control and optimization of an ethylene installation 2108.2.1 Automation solution and designing the control algorithms 2118.2.2 Optimizing the pyrolysis process 2178.3 Digital control of a thermoenergy plant 2198.3.1 Solving the problem of automation of a thermal operating point 2208.3.2 Optimization of thermal transfer and agent product 2258.4 Extremal control of a photovoltaic installation 2268.4.1 Extremal control of a photovoltaic panel 237Appendix A 243Appendix B 249Appendix C 257Appendix D 261Bibliography 271Index 279