Robust control of nonlinear stochastic systems by modelling conditional distributions of control signals


We introduce a novel inversion-based neuro-controller for solving control problems involving uncertain nonlinear systems that could also compensate for multi-valued systems. The approach uses recent developments in neural networks, especially in the context of modelling statistical distributions, which are applied to forward and inverse plant models. Provided that certain conditions are met, an estimate of the intrinsic uncertainty for the outputs of neural networks can be obtained using the statistical properties of networks. More generally, multicomponent distributions can be modelled by the mixture density network. In this work a novel robust inverse control approach is obtained based on importance sampling from these distributions. This importance sampling provides a structured and principled approach to constrain the complexity of the search space for the ideal control law. The performance of the new algorithm is illustrated through simulations with example systems.

Publication DOI:
Divisions: College of Engineering & Physical Sciences > School of Informatics and Digital Engineering > Mathematics
College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
Additional Information: The original publication is available at
Uncontrolled Keywords: distribution modelling,error bar,neural networks,stochastic systems,uncertainty,Control and Systems Engineering,Artificial Intelligence
Publication ISSN: 1433-3058
Full Text Link:
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
https://link.sp ... 0521-003-0375-y (Publisher URL)
PURE Output Type: Article
Published Date: 2003-11
Authors: Herzallah, Randa (ORCID Profile 0000-0001-9128-6814)
Lowe, David



Version: Accepted Version

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