Maximum Conditional Probability Stochastic Controller for Scalar Linear Systems with Additive Cauchy Noises

Nati Twito, Moshe Idan, Jason L. Speyer

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this work a stochastic controller, motivated by the sliding mode control methodology, is proposed for linear, single-state system with additive Cauchy distributed noises. The control law utilizes the time propagated probability density function (pdf) of the system state given measurements that has been derived in recent studies addressing the Cauchy estimation problem. The motivation for the proposed approach is mainly the high numerical complexity of the currently available methods for such systems. The controller performance is evaluated numerically and compared to an alternative approach presented recently and to a Gaussian approximation to the problem. A fundamental difference between the Cauchy and the Gaussian controllers is their response to noise outliers. While all controllers respond to process noises, even to the outliers, the Cauchy controllers drive the state faster towards zero after those events. On the other hand, the Cauchy controllers do not respond to measurement noise outliers, while the Gaussian does. The newly proposed Cauchy controller exhibits similar performance to the previously proposed one, while requiring lower computational effort.

Original languageEnglish
Title of host publication2018 European Control Conference, ECC 2018
Pages2708-2713
Number of pages6
ISBN (Electronic)9783952426982
DOIs
StatePublished - 27 Nov 2018
Event16th European Control Conference, ECC 2018 - Limassol, Cyprus
Duration: 12 Jun 201815 Jun 2018

Publication series

Name2018 European Control Conference, ECC 2018

Conference

Conference16th European Control Conference, ECC 2018
Country/TerritoryCyprus
CityLimassol
Period12/06/1815/06/18

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Control and Optimization

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