Process control design for industrial applications

Process control design for industrial applications

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 cont...

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Detalles Bibliográficos
Otros Autores: Popescu, Dumitru (Writer on computer science and engineering), author (author), Popescu, Dumitru, author
Formato: Libro electrónico
Idioma:Inglés
Publicado: Hoboken, New Jersey : Wiley 2017.
Edición:1st edition
Colección:Systems and Industrial Engineering - Robotics Series
Materias:
Process control.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009630117606719
Tabla de Contenidos:
  • Cover
  • Title Page
  • Copyright
  • Contents
  • Preface
  • List of Notations and Acronyms
  • 1. Introduction - Models and Dynamic Systems
  • 1.1. Overview
  • 1.2. Industrial process modeling
  • 1.3. Model classes
  • 1.3.1. State space models
  • 1.3.2. Input-output models
  • 2. Linear Identification of Closed-Loop Systems
  • 2.1. Overview of system identification
  • 2.2. Framework
  • 2.3. Preliminary identification of a CL process
  • 2.3.1. Multivariable linear identification methods
  • 2.3.2. Estimation of linear MIMO models using the LSM
  • 2.3.3. Identifying CL processes using the MV-LSM
  • 2.4. CLOE class of identification methods
  • 2.4.1. Principle of CLOE methods
  • 2.4.2. Basic CLOE method
  • 2.4.3. Weighted CLOE method
  • 2.4.4. Filtered CLOE method or adaptively filtered CLOE
  • 2.4.5. Extended CLOE method
  • 2.4.6. Generalized CLOE method
  • 2.4.7. CLOE methods for systems with integrator
  • 2.4.8. On the validation of CLOE identified models
  • 2.5. Application: identification of active suspension
  • 3. Digital Control Design Using Pole Placement
  • 3.1. Digital proportional-integral-derivative algorithm control
  • 3.2. Digital polynomial RST control
  • 3.3. RST control by pole placement
  • 3.3.1. RST control for regulation dynamics
  • 3.3.2. RST polynomial control for tracking dynamics (setpoint change)
  • 3.3.3. RST control with independent objectives in tracking and regulation
  • 3.4. Predictive RST control
  • 3.4.1. Finite horizon predictive control
  • 3.4.2. Predictive control with unitary horizon
  • 4. Adaptive Control and Robust Control
  • 4.1. Adaptive polynomial control systems
  • 4.1.1. Estimation of the parameters for closed-loop systems
  • 4.1.2. Design of the adaptive control
  • 4.2. Robust polynomial control systems
  • 4.2.1. Robustness of closed-loop systems
  • 4.2.2. Studying the stability-robustness connection.
  • 4.2.3. Study of the nonlinearity-robustness connection
  • 4.2.4. Study of the performance-robustness connection
  • 4.2.5. Analysis of robustness in the study of the sensitivity function
  • 4.2.6. Design of the robust RST control
  • 4.2.7. Calibrating the sensitivity function
  • 5. Multimodel Control
  • 5.1. Construction of multimodels
  • 5.1.1. Fuzzy logic: Mamdani models
  • 5.1.2. Identification from input-output data: direct method
  • 5.1.3. Identification from input-output data: neural approach
  • 5.1.4. Linearization around various operating points
  • 5.1.5. Convex polytopic transformation from an analytical model refined for the command
  • 5.1.6. Calculation of the validity of base models
  • 5.2. Stabilization and control of multimodels
  • 5.3. Design of multimodel command: fuzzy approach
  • 5.4. Trajectory tracking
  • 6. III-Defined and/or Uncertain Systems
  • 6.1. Study of the stability of nonlinear systems from vector norms
  • 6.1.1. Vector norms
  • 6.1.2. Comparison systems and overvaluing systems
  • 6.1.3. Determination of attractors
  • 6.1.4. Nested attractors [GHA 15a]
  • 6.2. Adaptation of control
  • 6.2.1. Minimizing the size of attractors: direct approach
  • 6.2.2. Minimizing the size of attractors by metaheuristics
  • 6.3. Overvaluation of the maximum error for various applications
  • 6.3.1. Control of nonlinear systems by pole placement
  • 6.3.2. Diffeomorphism command of nonlinear processes
  • 6.3.3. Determining the attractor for Lur'e Postnikov type processes [GHA 14]
  • 6.3.4. Minimizing the attractor through tabu search
  • 6.4. Fuzzy secondary loop control
  • 7. Modeling and Control of an Elementary Industrial Process
  • 7.1. Modeling and control of fluid transfer processes
  • 7.1.1. Modeling fluid flow processes
  • 7.1.2. Designing flow control systems
  • 7.2. Modeling and controlling liquid storage processes.
  • 7.2.1. Constant output flow
  • 7.2.2. Variable output flow
  • 7.2.3. Designing liquid level control systems
  • 7.3. Modeling and controlling the storage process of a pneumatic capacitor
  • 7.3.1. Modeling a pneumatic capacitor
  • 7.3.2. Designing pneumatic capacitor control systems
  • 7.4. Modeling and controlling heat transfer processes
  • 7.4.1. Modeling a thermal transfer process
  • 7.4.2. Designing temperature control systems
  • 7.5. Modeling and control of component transfer processes
  • 7.5.1. Modeling a chemical mixing process without reaction
  • 7.5.2. Modeling a chemical reaction process
  • 7.5.3. Designing systems for controlling the concentration of chemical components
  • 8. Industrial Applications - Case Studies
  • 8.1. Digital control for an installation of air heating in a steel plant
  • 8.1.1. Automation solution and design of the control algorithms
  • 8.1.2. Optimization of the combustion process
  • 8.2. Control and optimization of an ethylene installation
  • 8.2.1. Automation solution and designing the control algorithms
  • 8.2.2. Optimizing the pyrolysis process
  • 8.3. Digital control of a thermoenergy plant
  • 8.3.1. Solving the problem of automation of a thermal operating point
  • 8.3.2. Optimization of thermal transfer and agent product
  • 8.4. Extremal control of a photovoltaic installation
  • 8.4.1. Extremal control of a photovoltaic panel
  • Appendix A: Matrix Transformation from Any Representation to the Companion Form or Arrow Form
  • Appendix B: Determination of the Maximum Error for Pole Placement for a Nonlinear Third-Order Process
  • Appendix C: Determining the Attractor in a Nonlinear Process Controlled by Linear Decoupling
  • Appendix D: Overvaluation of the Maximum Error in a Tracking Problem for a Lur'e Postnikov Type Process
  • Bibliography
  • Index.
  • Other titles from iSTE in Systems and Industrial Engineering - Robotics
  • EULA.

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