temporarily out of stock
MULTIVARIABLE SYSTEM IDENTIFICATION FOR PROCESS CONTROL
Systems and control theory has experienced significant development in the past few decades. New techniques have emerged which hold enormous potential for industrial applications, and which have therefore also attracted much interest from academic researchers. However, the impact of these developments on the process industries has been limited.
The purpose of
Chapter headings. Foreward. Preface. Symbols and Abbreviations. Introduction. What is process identification? The hierarchy of modern automation systems. Multivariable model-based process control. Outline of the book. Models of Dynamic Processes and Signals. SISO Continuous-time models. SISO Discrete-time models. MIMO models. Models of signals. Linear processes with disturbances. Nonlinear models. Identification Test Design and Data Pretreatment. Controller configuration; selection of MV's, CV's and DV's. Preliminary process tests. Test signals for final test, persistent excitation. Test for model identification, final test. Sampling frequency and anti-aliasing filter. Pre-treatment of data. When is excitation allowed? Concluding remarks. Identification by the Least Squares Method. The principle of least-squares. Estimating models of linear processess. Industrial case studies. Properties of the least-squares estimator. Conclusions. Extensions of the Least-Squares Method. Modifying the frequency weighting by prefiltering. Output error method. Instrumental Variable (IV) methods. Prediction error methods. User's choice in identification for control. More on order/structure selection. Model validation. Identifying the glass tube drawing process. Recursive parameter estimation. Conclusions and discussion. Asymptotic Method; SISO Case. The Asymptotic theory. Optimal test signal spectrum for control. Parameter estimation and order selection. Model validation using upper error bound. Simulation studies and conclusion. Asymptotic Method; MIMO Case. MIMO version of the asymptotic theory. Asymptotic method. Identification of the glass tube drawing processes. Conclusions. Subspace Model Identification of MIMO Processes. Introduction. Definition of the state space identification problem. Definition of the data equation. Analysis of step response measurements. Subspace identification using generic input signals. Treatment of additive perturbations. A simulation study. Summary of extensions. Nonlinear Process Identification. Identification of Hammerstein models. Identification of Wiener models. Identification of NLN Hammerstein-Wiener model. Conclusions and recommendations. Applications of Identification in Process Control. A project approach to advanced process control. Identification requirements for process control. PID autotuning using process identification. Identification of I11-conditioned processes. Identification of a crude unit for MPC. Closed-loop identification of a Deethanizer. Conclusions and perspectives. Model Based Fault Detection and Isolation. Introduction. Residuals for linear systems with additive faults. Residuals for non additive faults in nonlinear systems. Residual evaluation. Industrial applications. Refresher on Matrix Theory. Definitions and some basic properties of matrices. Eigenvalues and eigenvectors. The singluar value decomposition and QR factorization. The Hankel matrix of a linear process. Bibliography. Index.