Probabilistic DHP adaptive critic for nonlinear stochastic control systems

Randa Herzallah

    Research output: Contribution to journalArticlepeer-review


    Following the recently developed algorithms for fully probabilistic control design for general dynamic stochastic systems (Herzallah & Káarnáy, 2011; Kárný, 1996), this paper presents the solution to the probabilistic dual heuristic programming (DHP) adaptive critic method (Herzallah & Káarnáy, 2011) and randomized control algorithm for stochastic nonlinear dynamical systems. The purpose of the randomized control input design is to make the joint probability density function of the closed loop system as close as possible to a predetermined ideal joint probability density function. This paper completes the previous work (Herzallah & Kárnáy, 2011; Kárný, 1996) by formulating and solving the fully probabilistic control design problem on the more general case of nonlinear stochastic discrete time systems. A simulated example is used to demonstrate the use of the algorithm and encouraging results have been obtained.
    Original languageEnglish
    Pages (from-to)74-82
    Number of pages9
    JournalNeural Networks
    Early online date4 Feb 2013
    Publication statusPublished - Jun 2013

    Bibliographical note

    © 2014, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International


    • nonlinear stochastic systems
    • fully probabilistic design
    • nonlinear randomized control input design
    • adaptive critic


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