Minimax Control of Linear Stochastic Systems with Noise Uncertainty

Douglas P. Looze, H. Vincent Poor, Kenneth S. Vastola, John C. Darragh

Research output: Contribution to journalArticlepeer-review

43 Scopus citations


The problem of linear-quadratic-Gaussian control of multivariable linear stochastic systems with uncertain second-order statistical properties is considered. Uncertainty is modeled by allowing process and observation noise spectral density matrices to vary' arbitrarily within given classes, and a minimax control formulation is applied to the quadratic objective functional. General theorems proving the existence and characterization of saddle-point solutions to this problem are presented, and the relationship of these results to earlier results on minimax state estimation are discussed. To illustrate the analytical results, the specific example of regulating a double-integrator plant is treated in detail.

Original languageEnglish (US)
Pages (from-to)882-888
Number of pages7
JournalIEEE Transactions on Automatic Control
Issue number9
StatePublished - Sep 1983
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering


Dive into the research topics of 'Minimax Control of Linear Stochastic Systems with Noise Uncertainty'. Together they form a unique fingerprint.

Cite this