Includes bibliographical references and index.

Online resource; title from PDF title page (John Wiley, viewed April 19, 2017).

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