Probabilistic control for uncertain systems

Research output: Contribution to journalArticle

Abstract

In this paper a new framework has been applied to the design of controllers which encompasses nonlinearity, hysteresis and arbitrary density functions of forward models and inverse controllers. Using mixture density networks, the probabilistic models of both the forward and inverse dynamics are estimated such that they are dependent on the state and the control input. The optimal control strategy is then derived which minimizes uncertainty of the closed loop system. In the absence of reliable plant models, the proposed control algorithm incorporates uncertainties in model parameters, observations, and latent processes. The local stability of the closed loop system has been established. The efficacy of the control algorithm is demonstrated on two nonlinear stochastic control examples with additive and multiplicative noise.
Original languageEnglish
Article number021018
Number of pages7
JournalJournal of Dynamic Systems, Measurement and Control
Volume134
Issue number2
DOIs
Publication statusPublished - 12 Jan 2012

Bibliographical note

ASME the original publisher. Herzallah, Randa, Journal of Dynamic Systems, Measurement, and Control. Copyright © 2012 by American Society of Mechanical Engineers.

Fingerprint Dive into the research topics of 'Probabilistic control for uncertain systems'. Together they form a unique fingerprint.

Cite this