System identification: theory for the user
System identification: theory for the user
On minimizing the maximum eigenvalue of a symmetric matrix
SIAM Journal on Matrix Analysis and Applications
A class of algorithms for identification in H∞
Automatica (Journal of IFAC)
Worst case system identification in l1 ne-equation1: optimal algorithms and error bounds
Systems & Control Letters
Time domain identification for robust control
Systems & Control Letters
Worst-case control-relevant identification
Automatica (Journal of IFAC) - Special issue on trends in system identification
SIAM Review
A Nevanlinna--Pick Approach to Time-Domain Constrained i Control
SIAM Journal on Control and Optimization
Brief paper: Probabilistic bounds for l1 uncertainty model validation
Automatica (Journal of IFAC)
| Hi-index | 22.15 |
In this paper we propose a new robust identification framework that combines both frequency and time-domain experimental data. The main result of the paper shows that the problem of obtaining a nominal model consistent with the experimental data and bounds on the identification error can be recast as a constrained finite-dimensional convex optimization problem that can be efficiently solved using Linear Matrix Inequalities techniques. This approach, based upon a generalized interpolation theory, contains as special cases the Caratheodory-Fejer (purely time-domain) and Nevanlinna- Pick (purely frequency-domain) problems. The proposed procedure interpolates the frequency and time domain experimental data while restricting the identified system to be in an a priori given class of models, resulting in a nominal model consistent with both sources of data. Thus, it is convergent and optimal up to a factor of two (with respect to central algorithms).