The authors provide a "data-driven" approach. No traditional parametric models are used: the intermediate subspace matrices are used directly for the designs. Thus the design process is simplified and the modelling error caused by parameterization is cut out.

A typical design procedure for model predictive control or control performance monitoring consists of: 1. identification of a parametric or nonparametric model; 2. derivation of the output predictor from the model; 3. design of the control law or calculation of performance indices according to the predictor.

Both design problems need an explicit model form and both require this three-step design procedure. Can this design procedure be simplified? Can an explicit model be avoided? With these questions in mind, the authors eliminate the first and second step of the above design procedure, a data-driven approach in the sense that no traditional parametric models are used; hence, the intermediate subspace matrices, which are obtained from the process data and otherwise identified as a first step in the subspace identification methods, are used directly for the designs. Without using an explicit model, the design procedure is simplified and the modelling error caused by parameterization is eliminated.

Provides the reader with control design methods derived directly from process data Simplifies control design and avoids the modelling errors consequent on parameterization

Autorentext

Biao Huang is a professor and researcher in the area of subspace identification, predictive control, and control performance monitoring with 20-years' worth of experience in this field. He has received numerous awards for his contributions in these areas including Germany's Alexander von Humboldt Research Fellowship award, the Canadian Chemical Engineering Society's Syncrude Canada Innovation Award, the University of Alberta's McCalla Professorship award, and Petro-Canada Young Innovator Award, and recipient of the best paper award for Journal of Process Control. Internationally, Professor Huang is recognised as a leading expert in control-loop performance monitoring, his contributions in this area including Performance Assessment of Control Loops (1-85233-639-0). He has applied his expertise extensively in industrial practice particularly in the oil sands industry. His research contributions in control performance monitoring have enjoyed wide application in the chemical, petrochemical, oil and gas, mineral processing, and pulp and paper industries throughout the world. Professor Huang has also been actively involved in research activities in system identification, particularly in subspace identification. He has served on a number of national and international engineering and science communities including, Chair of CSChE's System and Control Division, Associate Editor of CJChE, and Area Co-chair for IFAC ADCHEM and IFAC DYCOPS. Since 1997 Biao Huang has published over 100 refereed papers in international journals and conference proceedings. He has been invited to speak in a number of institutions as well as workshops worldwide including, most recently, an invited speech representing academia in the Round Table on Asset Management and the Round Table on Nonlinear System Identification, in the International Workshop on Solving Industrial Control and Optimization Problems, Cramado, Brazil, 6th - 7th April 2006. Dr. Ramesh Kadali iscurrently a practising process control engineer in Sunco Inc. Canada and has extensive experiences in applying system identification, model predictive control, and control performance monitoring in real industrial processes. His PhD research was on the data-driven subspace approach to predictive control design and performance analysis.

Inhalt
I Dynamic Modeling through Subspace Identification.- System Identification: Conventional Approach.- Open-loop Subspace Identification.- Closed-loop Subspace Identification.- Identification of Dynamic Matrix and Noise Model Using Closed-loop Data.- II Predictive Control.- Model Predictive Control: Conventional Approach.- Data-driven Subspace Approach to Predictive Control.- III Control Performance Monitoring.- Control Loop Performance Assessment: Conventional Approach.- State-of-the-art MPC Performance Monitoring.- Subspace Approach to MIMO Feedback Control Performance Assessment.- Prediction Error Approach to Feedback Control Performance Assessment.- Performance Assessment with LQG-benchmark from Closed-loop Data.