Abstract
We present a novel problem formulation and algorithm for H∞ system identification based on a stochastic noise model and constrained model set to reduce the conservatism in deterministic noise models, and statistical inefficiency and computational complexity associated with high-order estimates. By establishing a connection between a minimax problem and a sequence of weighted least square problems, we show that the proposed stochastic, constrained problem can be solved with a computationally attractive and conceptually simple iteratively weighted least square (IWLS) identification algorithm. The IWLS procedure is based on a sequence of standard parametric weighted least square output error identification routines, where the weighting is updated via non-parametric estimation of the modeling error to asymptotically achieve the H∞ identification criterion.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 3374-3379 |
| Number of pages | 6 |
| Journal | Proceedings of the IEEE Conference on Decision and Control |
| Volume | 4 |
| State | Published - 1994 |
| Event | Proceedings of the 33rd IEEE Conference on Decision and Control. Part 1 (of 4) - Lake Buena Vista, FL, USA Duration: Dec 14 1994 → Dec 16 1994 |
ASJC Scopus subject areas
- Control and Systems Engineering
- Modeling and Simulation
- Control and Optimization