A Bayesian perspective on stochastic neurocontrol

Randa Herzallah, David Lowe

    Research output: Contribution to journalArticlepeer-review


    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.
    Original languageEnglish
    Pages (from-to)914-924
    Number of pages11
    JournalIEEE Transactions on Neural Networks and Learning Systems
    Issue number5
    Publication statusPublished - May 2008

    Bibliographical note

    © 2008 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.


    • Bayesian perspective
    • control design
    • discrete-time systems
    • information extraction
    • input-dependent noise
    • loss function
    • nonlinear simulation
    • optimal control law
    • probabilistic control
    • probabilistic framework
    • stochastic neurocontrol
    • stochastic uncertain nonlinear systems
    • system dynamics


    Dive into the research topics of 'A Bayesian perspective on stochastic neurocontrol'. Together they form a unique fingerprint.

    Cite this