Probabilistic control for uncertain systems

Randa Herzallah

    Research output: Contribution to journalArticlepeer-review


    In this paper a new framework has been applied to the design of controllers which encompasses nonlinearity, hysteresis and arbitrary density functions of forward models and inverse controllers. Using mixture density networks, the probabilistic models of both the forward and inverse dynamics are estimated such that they are dependent on the state and the control input. The optimal control strategy is then derived which minimizes uncertainty of the closed loop system. In the absence of reliable plant models, the proposed control algorithm incorporates uncertainties in model parameters, observations, and latent processes. The local stability of the closed loop system has been established. The efficacy of the control algorithm is demonstrated on two nonlinear stochastic control examples with additive and multiplicative noise.
    Original languageEnglish
    Article number021018
    Number of pages7
    JournalJournal of Dynamic Systems, Measurement and Control
    Issue number2
    Publication statusPublished - 12 Jan 2012

    Bibliographical note

    ASME the original publisher. Herzallah, Randa, Journal of Dynamic Systems, Measurement, and Control. Copyright © 2012 by American Society of Mechanical Engineers.


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