Stochastic optimal control: theory and application
Stochastic optimal control: theory and application
Information-based complexity
Random number generation and quasi-Monte Carlo methods
Random number generation and quasi-Monte Carlo methods
Probabilistic robustness analysis: explicit bounds for the minimum number of samples
Systems & Control Letters
Complexity and information
A Theory of Learning and Generalization: With Applications to Neural Networks and Control Systems
A Theory of Learning and Generalization: With Applications to Neural Networks and Control Systems
Brief paper: Guaranteed cost regulator design: A probabilistic solution and a randomized algorithm
Automatica (Journal of IFAC)
Least costly identification experiment for control
Automatica (Journal of IFAC)
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)
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)
Randomized algorithms for quadratic stability of quantized sampled-data systems
Automatica (Journal of IFAC)
Probabilistic design of LPV control systems
Automatica (Journal of IFAC)
Randomized algorithms for stability and robustness analysis of high-speed communication networks
IEEE Transactions on Neural Networks
Randomized Algorithms for Analysis and Control of Uncertain Systems: With Applications
Randomized Algorithms for Analysis and Control of Uncertain Systems: With Applications
Stochastic algorithms for robustness of control performances
Automatica (Journal of IFAC)
Survey paper: Research on probabilistic methods for control system design
Automatica (Journal of IFAC)
Brief paper: Linear computational complexity robust ILC for lifted systems
Automatica (Journal of IFAC)
| Hi-index | 0.01 |
In this paper, we present an overview of probabilistic techniques based on randomized algorithms for solving ''hard'' problems arising in performance verification and control of complex systems. This area is fairly recent, even though its roots lie in the robustness techniques for handling uncertain control systems developed in the 1980s. In contrast to these deterministic techniques, the main ingredient of the methods discussed in this survey is the use of probabilistic concepts. The introduction of probability and random sampling permits overcoming the fundamental tradeoff between numerical complexity and conservatism that lie at the roots of the worst-case deterministic methodology. The simplicity of implementation of randomized techniques may also help bridging the gap between theory and practical applications.