A Bayesian perspective on stochastic neurocontrol


Control design for stochastic uncertain nonlinear systems is traditionally based on minimizing the expected value of a suitably chosen loss function. Moreover, most control methods usually assume the certainty equivalence principle to simplify the problem and make it computationally tractable. We offer an improved probabilistic framework which is not constrained by these previous assumptions, and provides a more natural framework for incorporating and dealing with uncertainty. The focus of this paper is on developing this framework to obtain an optimal control law strategy using a fully probabilistic approach for information extraction from process data, which does not require detailed knowledge of system dynamics. Moreover, the proposed control method framework allows handling the problem of input-dependent noise. A basic paradigm is proposed and the resulting algorithm is discussed. The proposed probabilistic control method is for the general nonlinear class of discrete-time systems. It is demonstrated theoretically on the affine class. A nonlinear simulation example is also provided to validate theoretical development.

Divisions: College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
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Publication ISSN: 1941-0093
Last Modified: 29 Nov 2023 10:12
Date Deposited: 13 Dec 2011 11:05
Full Text Link: http://ieeexplo ... rnumber=4479861
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2008-05
Authors: Herzallah, Randa
Lowe, David



Version: Accepted Version

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