Abstract
Probabilistic methods have recently been the subject of considerable attention in the context of robust performance assessment. However, in spite of their potential, these methods have been limited to the case of parametric uncertainty; the problem of sampling causal bounded operators is largely open. In this paper, we take steps towards removing this limitation by providing a computationally efficient algorithm aimed at uniform sampling over balls contained in suitably chosen proper subspaces of H∞. As shown in the paper, samples generated from these balls can be used, for instance by Monte Carlo methods, to assess robust performance for uncertainty models involving the H∞ norm.
Original language | English (US) |
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Pages (from-to) | 5038-5043 |
Number of pages | 6 |
Journal | Proceedings of the IEEE Conference on Decision and Control |
Volume | 5 |
State | Published - 2001 |
Event | 40th IEEE Conference on Decision and Control (CDC) - Orlando, FL, United States Duration: Dec 4 2001 → Dec 7 2001 |
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Modeling and Simulation
- Control and Optimization