Università degli Studi del Sannio, Dipartimento e Facoltà di Ingegneria Palazzo Dell’Aquila Bosco Lucarelli, Corso Garibaldi 107, 82100 Benevento, Italia; Institut für Mathematik und Wissenschaftliches Rechnen, Karl-Franzens-Universität Graz Heinrichstr. 36, 8010 Graz, Austria
A Fokker‐Planck framework for the formulation of an optimal control strategy of stochastic processes is presented. Within this strategy, the control objectives are defined based on the probability density functions of the stochastic processes. The optimal control is obtained as the minimizer of the objective under the constraint given by the Fokker‐Planck model. Representative stochastic processes are considered with different control laws and with the purpose of attaining a final target configuration or tracking a desired trajectory. In this latter case, a receding‐horizon algorithm over a sequence of time windows is implemented.
Supported in part by the Austrian Science Fund FWF project F3205‐N18 “Fast Multigrid Methods for Inverse Problems”.
Annunziato, M., & Borzì, A. (2010). Optimal control of probability density functions of stochastic processes. Mathematical Modelling and Analysis, 15(4), 393-407. https://doi.org/10.3846/1392-6292.2010.15.393-407
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