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 journalConference articlepeer-review

Abstract

The linear-quadratic-Gaussian regulator problem is considered for multivariable linear stochastic systems with uncertain second-order statistical properties. 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 is discussed. To illustrate the analytical results, the specific example of regulating a double-integrator plant is treated in detail.

Original languageEnglish (US)
Article number4787943
Pages (from-to)694-695
Number of pages2
JournalProceedings of the American Control Conference
Volume1982-June
StatePublished - Jan 1 1982
Event1st American Control Conference, ACC 1982 - Arlington, United States
Duration: Jun 14 1982Jun 16 1982

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

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

Cite this