Identification of Multivariable Industrial Processes presents a unified approach to multivariable industrial process identification. It concentrates on industrial processes with reference to model applications. The areas covered are experiment design, model structure selection, parameter estimation as well as error bounds of the transfer function. This publication is intended to fill the gap between modern systems and control theory and industrial application. It is based on the results of 10 years of research and application experiences. The theories and models discussed are fully explained…mehr
Identification of Multivariable Industrial Processes presents a unified approach to multivariable industrial process identification. It concentrates on industrial processes with reference to model applications. The areas covered are experiment design, model structure selection, parameter estimation as well as error bounds of the transfer function. This publication is intended to fill the gap between modern systems and control theory and industrial application. It is based on the results of 10 years of research and application experiences. The theories and models discussed are fully explained and illustrated with case studies. At an early stage the reader is introduced to real applications.
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.- 4.3 Two Industrial Case Studies.- 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.- 5.5 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.- 7.2.3 Optimal Experiment Design for Simulation.- 7.3 Recursive Estimation.- 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.- 8.3 Identification of Two Industrial Processes.-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.
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.- 4.3 Two Industrial Case Studies.- 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.- 5.5 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.- 7.2.3 Optimal Experiment Design for Simulation.- 7.3 Recursive Estimation.- 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.- 8.3 Identification of Two Industrial Processes.-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.
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