Improved robust control of nonlinear stochastic systems using uncertain models


We introduce a technique for quantifying and then exploiting uncertainty in nonlinear stochastic control systems. The approach is suboptimal though robust and relies upon the approximation of the forward and inverse plant models by neural networks, which also estimate the intrinsic uncertainty. Sampling from the resulting Gaussian distributions of the inversion based neurocontroller allows us to introduce a control law which is demonstrably more robust than traditional adaptive controllers.

Divisions: College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
Event Title: 5th Portuguese Conference on Automatic Control
Event Type: Other
Event Dates: 2002-09-05 - 2002-09-07
Uncontrolled Keywords: uncertainity,Neural Networks,Stochastic Systems,error bar,Distribution modelling
Last Modified: 27 Dec 2023 10:00
Date Deposited: 14 Sep 2009 09:39
PURE Output Type: Conference contribution
Published Date: 2002-09
Authors: Herzallah, Randa (ORCID Profile 0000-0001-9128-6814)
Lowe, David


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