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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 contr...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
London, UK : Hoboken, NJ :
ISTE, Ltd. ; Wiley,
2017.
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| Colección: | Robotics series.
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
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 methods2.4.1. 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 dynamics3.3.2. 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 connection4.2.3. 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 command5.1.6. 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.