Stochastic approximation algorithms for risk-adjusted quadratic stability

Research output: Contribution to journalConference articlepeer-review

4 Scopus citations


This paper concentrates on a risk-adjusted version of the well known quadratic stability problem for uncertain linear systems. For a wide class of probability density functions, we provide algorithms for solving the following problem: With nominally determined quadratic Lyapunov function V(x) = xTPx, find a state feedback gain which maximizes the probability of quadratic stability. This so-called Probabilistic Design Problem has been previously shown to be a convex program. In this paper we provide stochastic approximation algorithms which converge to its solution. It is demonstrated that for small values of the risk probability ε, the controller gains which are required can be much smaller than their counterparts obtained via classical robust theory.

Original languageEnglish (US)
Pages (from-to)2254-2259
Number of pages6
JournalProceedings of the IEEE Conference on Decision and Control
StatePublished - 2002
Event41st IEEE Conference on Decision and Control - Las Vegas, NV, United States
Duration: Dec 10 2002Dec 13 2002

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization


Dive into the research topics of 'Stochastic approximation algorithms for risk-adjusted quadratic stability'. Together they form a unique fingerprint.

Cite this