A Monte Carlo approach to the analysis of control system robustness
Automatica (Journal of IFAC) - Special issue on robust control
A formula for computation of the real stability radius
Automatica (Journal of IFAC)
Probabilistic robustness analysis: explicit bounds for the minimum number of samples
Systems & Control Letters
Random variate generation for multivariate unimodal densities
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Robust Solutions to Least-Squares Problems with Uncertain Data
SIAM Journal on Matrix Analysis and Applications
Robust Solutions to Uncertain Semidefinite Programs
SIAM Journal on Optimization
Randomized algorithms for robust controller synthesis using statistical learning theory
Automatica (Journal of IFAC)
Survey A survey of computational complexity results in systems and control
Automatica (Journal of IFAC)
Brief Probabilistic solutions to some NP-hard matrix problems
Automatica (Journal of IFAC)
Brief Robust maximum likelihood estimation in the linear model
Automatica (Journal of IFAC)
A survey of randomized algorithms for control synthesis and performance verification
Journal of Complexity
Brief paper: Stochastic ellipsoid methods for robust control: Multiple updates and multiple cuts
Automatica (Journal of IFAC)
Survey paper: Research on probabilistic methods for control system design
Automatica (Journal of IFAC)
| Hi-index | 22.15 |
The objective of this paper is twofold. First, the problem of generation of real random matrix samples with uniform distribution in structured (spectral) norm bounded sets is studied. This includes an analysis of the distribution of the singular values of uniformly distributed real matrices, and an efficient (i.e. polynomial-time) algorithm for their generation. Second, it is shown how the developed techniques may be used to solve in a probabilistic setting several hard problems involving systems subject to real structured uncertainty.