Probabilistic robustness analysis: Explicit bounds for the minimum number of samples
From MaRDI portal
Publication:1391598
DOI10.1016/S0167-6911(97)00005-4zbMath0901.93017OpenAlexW2180359187MaRDI QIDQ1391598
Publication date: 22 July 1998
Published in: Systems \& Control Letters (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/s0167-6911(97)00005-4
Related Items (40)
A probabilistic framework for problems with real structured uncertainty in systems and control ⋮ Robust control of nonlinear systems with parametric uncertainty ⋮ A survey of randomized algorithms for control synthesis and performance verification ⋮ Polynomial-time probabilistic observability analysis of sampled-data piecewise affine systems ⋮ Probabilistic sorting and stabilization of switched systems ⋮ Polynomial-time algorithms for probabilistic solutions of parameter-dependent linear matrix inequalities ⋮ A randomized approach to sensor placement with observability assurance ⋮ Least costly identification experiment for control ⋮ Probabilistic performance validation of deep learning‐based robust NMPC controllers ⋮ A random least-trimmed-squares identification algorithm ⋮ Worst-case violation of sampled convex programs for optimization with uncertainty ⋮ A framework for optimization under limited information ⋮ Probabilistic design of LPV control systems. ⋮ Stability properties of multi-stage nonlinear model predictive control ⋮ Computational complexity of randomized algorithms for solving parameter-dependent linear matrix inequalities. ⋮ Randomized sampling for large zero-sum games ⋮ Unified treatment of robust stability conditions for discrete-time systems through an infinite matrix framework ⋮ Control design with hard/soft performance specifications: aQ-parameter randomization approach ⋮ Outliers, inliers and the generalized least trimmed squares estimator in system identification ⋮ Probabilistic output admissible set for systems with time-varying uncertainties ⋮ A sampling-and-discarding approach to chance-constrained optimization: feasibility and Optimality ⋮ On the binomial confidence interval and probabilistic robust control ⋮ Probabilistic robust design with linear quadratic regulators ⋮ From experiment design to closed-loop control ⋮ Randomized algorithms for robust controller synthesis using statistical learning theory ⋮ Probabilistic solutions to some NP-hard matrix problems ⋮ Statistical Learning Theory: A Pack-based Strategy for Uncertain Feasibility and Optimization Problems ⋮ Research on probabilistic methods for control system design ⋮ A primal--dual probabilistic setting for quadratic stability of uncertain systems ⋮ Monte Carlo and Las Vegas randomized algorithms for systems and control. An introduction ⋮ Randomized algorithms for robust controller synthesis using statistical learning theory: a tutorial overview ⋮ Discussion on: ``Why is resorting to fate wise? A critical look at randomized algorithms in systems and control ⋮ Distributed randomized algorithms for probabilistic performance analysis ⋮ Reliability Modeling of Fault Tolerant Control Systems ⋮ Worst-case properties of the uniform distribution and randomized algorithms for robustness analysis ⋮ Robustness of feedback stabilization of quasi non-integrable Hamiltonian systems with parametric uncertainty ⋮ Lebesgue piecewise affine approximation of nonlinear systems ⋮ Randomized methods for design of uncertain systems: sample complexity and sequential algorithms ⋮ Statistical learning methods in linear algebra and control problems: The example of finite-time control of uncertain linear systems ⋮ Average H 2 control by randomized algorithms
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Root locations of an entire polytope of polynomials: It suffices to check the edges
- Worst-case properties of the uniform distribution and randomized algorithms for robustness analysis
- Several NP-hard problems arising in robust stability analysis
- The uniform distribution: A rigorous justification for its use in robustness analysis
- A Monte Carlo approach to the analysis of control system robustness
- Stochastic robustness of linear time-invariant control systems
- Computational complexity of μ calculation
- A Measure of Asymptotic Efficiency for Tests of a Hypothesis Based on the sum of Observations
This page was built for publication: Probabilistic robustness analysis: Explicit bounds for the minimum number of samples