Abstract
The paper addresses the worst-case parameter identification problem for linear systems under partial state measurements. Using the cost-to-come function method, worst-case identifiers are derived for SISO systems. The worst-case identifier thus obtained includes the Kreisselmeier observer as part of its structure, with parameters set at some optimal values. Its structure is different from the common least-squares (LS) identifier, however, in the sense that there is an additional dynamics for the state estimate, coupled with the dynamics of the parameter estimate in a nontrivial way. A reduced-order identifier is obtained, which is numerically much better conditioned when the disturbances in the measurement equations are 'small.'
| Original language | English (US) |
|---|---|
| Pages (from-to) | 709-714 |
| Number of pages | 6 |
| Journal | Proceedings of the IEEE Conference on Decision and Control |
| Volume | 1 |
| State | Published - 1995 |
| Event | Proceedings of the 1995 34th IEEE Conference on Decision and Control. Part 1 (of 4) - New Orleans, LA, USA Duration: Dec 13 1995 → Dec 15 1995 |
ASJC Scopus subject areas
- Control and Optimization
- Control and Systems Engineering
- Modeling and Simulation