Dynamic Modeling, Predictive Control and Performance Monitoring: A Data-driven Subspace Approach

✍ Scribed by Biao Huang, Ramesh Kadali (auth.)

• Publisher: Springer-Verlag London
• Year: 2008
• Tongue: English
• Leaves: 238
• Series: Lecture Notes in Control and Information Sciences 374
• Edition: 1
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Front Matter....Pages -
Introduction....Pages 1-5
Front Matter....Pages 7-7
System Identification: Conventional Approach....Pages 9-29
Open-loop Subspace Identification....Pages 31-53
Closed-loop Subspace Identification....Pages 55-78
Identification of Dynamic Matrix and Noise Model Using Closed-loop Data....Pages 79-97
Front Matter....Pages 99-99
Model Predictive Control: Conventional Approach....Pages 101-119
Data-driven Subspace Approach to Predictive Control....Pages 121-141
Front Matter....Pages 143-143
Control Loop Performance Assessment: Conventional Approach....Pages 145-155
State-of-the-art MPC Performance Monitoring....Pages 157-175
Subspace Approach to MIMO Feedback Control Performance Assessment....Pages 177-193
Prediction Error Approach to Feedback Control Performance Assessment....Pages 195-211
Performance Assessment with LQG-benchmark from Closed-loop Data....Pages 213-227
Back Matter....Pages -

✦ Subjects

Control Engineering;Systems Theory, Control;Industrial Chemistry/Chemical Engineering;Vibration, Dynamical Systems, Control;Automation and Robotics;Systems and Information Theory in Engineering