A cutting plane algorithm for convex programming that uses analytic centers
Mathematical Programming: Series A and B
Complexity estimates of some cutting plane methods based on the analytic barrier
Mathematical Programming: Series A and B
Robust and optimal control
Polynomial bounds for VC dimension of sigmoidal and general Pfaffian neural networks
Journal of Computer and System Sciences - Special issue: dedicated to the memory of Paris Kanellakis
Probabilistic robustness analysis: explicit bounds for the minimum number of samples
Systems & Control Letters
Probabilistic enhancement of classical robustness margins: the unirectangularity concept
Systems & Control Letters
Statistical learning control of uncertain systems: theory and algorithms
Applied Mathematics and Computation
Robust Solutions to Uncertain Semidefinite Programs
SIAM Journal on Optimization
Randomized Algorithms: A System-Level, Poly-Time Analysis of Robust Computation
IEEE Transactions on Computers
Introduction to Stochastic Search and Optimization
Introduction to Stochastic Search and Optimization
Fast Construction of Robustness Degradation Function
SIAM Journal on Control and Optimization
Uncertain convex programs: randomized solutions and confidence levels
Mathematical Programming: Series A and B
Brief paper: Guaranteed cost regulator design: A probabilistic solution and a randomized algorithm
Automatica (Journal of IFAC)
Brief paper: A risk adjusted approach to robust simultaneous fault detection and isolation
Automatica (Journal of IFAC)
Simulated Annealing for Convex Optimization
Mathematics of Operations Research
A survey of randomized algorithms for control synthesis and performance verification
Journal of Complexity
A probabilistic analytic center cutting plane method for feasibility of uncertain LMIs
Automatica (Journal of IFAC)
Probabilistic reachability and safety for controlled discrete time stochastic hybrid systems
Automatica (Journal of IFAC)
Brief paper: Probabilistic sorting and stabilization of switched systems
Automatica (Journal of IFAC)
The Exact Feasibility of Randomized Solutions of Uncertain Convex Programs
SIAM Journal on Optimization
Learning and Generalization: With Applications to Neural Networks
Learning and Generalization: With Applications to Neural Networks
A Randomized Cutting Plane Method with Probabilistic Geometric Convergence
SIAM Journal on Optimization
SIAM Journal on Optimization
Decentralized Cooperative Policy for Conflict Resolution in Multivehicle Systems
IEEE Transactions on Robotics
Monte Carlo Optimization for Conflict Resolution in Air Traffic Control
IEEE Transactions on Intelligent Transportation Systems
Brief Recursive algorithms for inner ellipsoidal approximation of convex polytopes
Automatica (Journal of IFAC)
Randomized algorithms for robust controller synthesis using statistical learning theory
Automatica (Journal of IFAC)
Brief Probabilistic solutions to some NP-hard matrix problems
Automatica (Journal of IFAC)
A probabilistic framework for problems with real structured uncertainty in systems and control
Automatica (Journal of IFAC)
A probably approximately correct framework to estimate performance degradation in embedded systems
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Randomized algorithms for stability and robustness analysis of high-speed communication networks
IEEE Transactions on Neural Networks
Efficient optimal design of uncertain discrete time dynamical systems
Automatica (Journal of IFAC)
Probabilistic set invariance and ultimate boundedness
Automatica (Journal of IFAC)
Stochastic model predictive control of LPV systems via scenario optimization
Automatica (Journal of IFAC)
Stochastic surveillance strategies for spatial quickest detection
International Journal of Robotics Research
Technical communique: Policy set iteration for Markov decision processes
Automatica (Journal of IFAC)
Randomized Approaches for Control of QuadRotor UAVs
Journal of Intelligent and Robotic Systems
| Hi-index | 22.15 |
A novel approach based on probability and randomization has emerged to synergize with the standard deterministic methods for control of systems with uncertainty. The main objective of this paper is to provide a broad perspective on this area of research known as ''probabilistic robust control'', and to address in a systematic manner recent advances. The focal point is on design methods, based on the interplay between uncertainty randomization and convex optimization, and on the illustration of specific control applications.