System identification and model development
US9235657B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Mar 13, 2013 |
| Grant date | Jan 12, 2016 |
| Priority date | — |
| Expiry date | Feb 8, 2034 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG05B2219/2642
- WIPO fieldControl
- WIPO sectorInstruments
Abstract
Methods for system identification are presented using model predictive control to frame a gray-box parameterized state space model. System parameters are identified using an optimization procedure to minimize a first error cost function within a range of filtered training data. Disturbances are accounted for using an implicit integrator within the system model, as well as a parameterized Kalman gain. Kalman gain parameters are identified using an optimization procedure to minimize a second error cost function within a range of non-filtered training data. Recursive identification methods are presented to provide model adaptability using an extended Kalman filter to estimate model parameters and a Kalman gain to estimate system states.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.