Stochastic algorithms for robustness of control performances

Benedetto Piccoli, Katarzyna Zadarnowska, Matteo Gaeta

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


In recent years, there has been a growing interest in developing statistical learning methods to provide approximate solutions to "difficult" control problems. In particular, randomized algorithms have become a very popular tool used for stability and performance analysis as well as for design of control systems. However, as randomized algorithms provide an efficient solution procedure to the "intractable" problems, stochastic methods bring closer to understanding the properties of the real systems. The topic of this paper is the use of stochastic methods in order to solve the problem of control robustness: the case of parametric stochastic uncertainty is considered. Necessary concepts regarding stochastic control theory and stochastic differential equations are introduced. Then a convergence analysis is provided by means of the Chernoff bounds, which guarantees robustness in mean and in probability. As an illustration, the robustness of control performances of example control systems is computed.

Original languageEnglish (US)
Pages (from-to)1407-1414
Number of pages8
Issue number6
StatePublished - Jun 2009
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Electrical and Electronic Engineering


  • Control system
  • Randomized algorithms
  • Robustness
  • Stochastic algorithms
  • Stochastic differential equations


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