Robust process model identification in model based control techniques
US7840287B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Apr 13, 2006 |
| Grant date | Nov 23, 2010 |
| Priority date | — |
| Expiry date | Sep 20, 2027 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG05B17/02
- WIPO fieldControl
- WIPO sectorInstruments
Abstract
A robust method of creating process models for use in controller generation, such as in MPC controller generation, adds noise to the process data collected and used in the model generation process. In particular, a robust method of creating a parametric process model first collects process outputs based on known test input signals or sequences, adds random noise to the collected process data and then uses a standard or known technique to determine a process model from the collected process data. Unlike existing techniques for noise removal that focus on clean up of non-random noise prior to generating a process model, the addition of random, zero-mean noise to the process data enables, in many cases, the generation of an acceptable parametric process model in situations where no process model parameter convergence was otherwise obtained. Additionally, process models created using this technique generally have wider confidence intervals, therefore providing a model that works adequately in many process situations without needing to manually or graphically change the model.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.