Autonomous Frequency Domain Identification: Theory and Experiment
Abstract
The analysis, design, and on-orbit tuning of robust controllers require more information about the plant than simply a nominal estimate of the plant transfer function. Information is also required concerning the uncertainty in the nominal estimate, or more generally, the identification of a model set within which the true plant is known to lie. The identification methodology that was developed and experimentally demonstrated makes use of a simple but useful characterization of the model uncertainty based on the output error. This is a characterization of the additive uncertainty in the plant model, which has found considerable use in many robust control analysis and synthesis techniques. The identification process is initiated by a stochastic input u which is applied to the plant p giving rise to the output y. Spectral estimation (h = Puy/Puu) is used as an estimate of p and the model order is estimated using the product moment matrix (PMM) method. A parametric model P is then determined by curve fitting the spectral estimate to a rational transfer function. The additive uncertainty Dm = p - P is then estimated by the cross-spectral estimate delta = Pue/Pun where e = y - Y is the output error, and Y = Pu is the computed output of the parametric model subjected to the actual input u. The experimental results demonstrate the curve fitting algorithm produces the reduced-order plant model which minimizes the additive uncertainty. The nominal transfer function estimate P and the estimate delta of the additive uncertainty Dm are subsequently available to be used for optimization of robust controller performance and stability.
Document Details
- Document Type
- Technical Report
- Publication Date
- Apr 15, 1989
- Accession Number
- ADA338998
Entities
People
- D. S. Bayard
- E. Mettler
- F. Y. Hadaegh
- M. H. Milman
- Y. Yam
Organizations
- Jet Propulsion Laboratory