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.
| Autorius: | Ramesh Kadali, Biao Huang, |
| Serija: | Lecture Notes in Control and Information Sciences |
| Leidėjas: | Springer London |
| Išleidimo metai: | 2008 |
| Knygos puslapių skaičius: | 268 |
| ISBN-10: | 1848002327 |
| ISBN-13: | 9781848002326 |
| Formatas: | Knyga minkštu viršeliu |
| Kalba: | Anglų |
| Žanras: | Cybernetics and systems theory |
Parašykite atsiliepimą apie „Dynamic Modeling, Predictive Control and Performance Monitoring: A Data-driven Subspace Approach“
