Probabilistic control for uncertain systems


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.

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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: ASME the original publisher. Herzallah, Randa, Journal of Dynamic Systems, Measurement, and Control. Copyright © 2012 by American Society of Mechanical Engineers.
Publication ISSN: 1528-9028
Full Text Link: http://dynamics ... ticleid=1415274
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PURE Output Type: Article
Published Date: 2012-01-12
Authors: Herzallah, Randa (ORCID Profile 0000-0001-9128-6814)



Version: Accepted Version

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