Identification of Multivariable Industrial Processes

1 Introduction.- 1.1 Some Preliminary Concepts.- 1.2 Digital Control of Industrial Processes.- 1.3 Outline of the Book.- 2 Linear Models of Dynamic Processes and Signals.- 2.1 SISO Continuous-Time Models.- 2.2 SISO Discrete-Time Models.- 2.3 MIMO Models.- 2.4 Models of Signals.- 2.5 Linear Processes with Disturbances; Conclusion.- 3 Identification Experiments and Data Pre-treatmet.- 3.1 Selection of Inputs/Outputs and Preliminary Experiments.- 3.2 Experiment for Model Estimation.- 3.3 Pre-treatment of Data.- 3.4 Conclusions.- 4 Identification by the Least-Squares Method.- 4.1 The Principle of Least-Squares.- 4.2 Estimating Models of Linear Processes.- 4.2.1 Finite Impulse Response (FIR) Models.- 4.2.2 Transfer Operator Models.- 4.2.3 Model Validation and Order Selection; A Simulation Approach.- 4.3 Two Industrial Case Studies.- 4.3.1 Identification and Control of a Single-Stand Rolling Mill.- 4.3.2 Identification and Control of a Glass Tube Process.- 4.4 Properties of the Least-Squares Estimator.- 4.5 Conclusions.- 5 Extensions of the Least-Squares Method.- 5.1 Modifying the Frequency Weighting by Prefiltering.- 5.2 A Natural Choice of Criterion — Output Error Method.- 5.3 Using Correlation Techniques — Instrumental Variable (IV) Methods.- 5.4 Obtaining White Residuals — Prediction Error Methods.- 5.4.1 Generalized Least-Squares (GLS) Method.- 5.4.2 General Properties of the Family of Prediction Error Methods.- 5.5 Identifying the Glass Tube Process Using a Prediction Error Method.- 5.6 Conclusions and Discussion.- 6 MIMO Process Identification: A Markov Parameter Approach.- 6.1 Rationale of the Method.- 6.2 The Identification Procedure.- 6.3 Identification of the Glass Tube Manufacturing Process.- 6.4 Conclusions.- 7 Identification for Robust Control; SISO Case.- 7.1 Asymptotic Properties of Prediction Error Models.- 7.2 The Identification Method.- 7.2.1 The Procedure.- 7.2.2 Model Order Selection.- 7.2.3 Optimal Experiment Design for Simulation.- 7.3 Recursive Estimation.- 7.3.1 The Recursive LS Method.- 7.3.2 A Recursive Output Error Method.- 7.3.3 A Recursive Identifier for Robust Adaptive Control.- 7.4 A Simulation Study.- 7.5 Conclusions.- 8 Identification for Robust Control; MIMO Case.- 8.1 The MIMO Version of the Asymptotic Theory.- 8.2 The Identification Method.- 8.2.1 The Procedure.- 8.2.2 Model Structure Selection.- 8.2.3 Input Design for Simulation.- 8.2.4 Determining a State Space Realization.- 8.3 Identification of Two Industrial Processes.- 8.3.1 Identification of the Glass Tube Process.- 8.3.2 Identification of a Four-Effect Evaporator.- 8.4 Closed Loop Identification of Coprime Factors.- 8.5 Conclusions.- 9 Identification and Robust Control of the Glass Tube Process.- 9.1 From Identification to Robust Control; Guidelines.- 9.2 Identification and Control of the Glass Tube Process; Control Results.- 9.3 Conclusions.- 10 Identification for Fault Diagnosis; Estimation of Continuous-Time Models.- 10.1 An Indirect Method of Continuous-Time Model Estimation.- 10.2 Enhancing a Parameters Subset by Input Design.- 10.3 A Simulation Study.- 10.4 Conclusions.- Symbols and Abbreviations.- References.