Probabilistic message passing control for complex stochastic switching systems

Yuyang Zhou*, Randa Herzallah

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


In this paper, we propose a general decentralised probabilistic control framework for a class of complex stochastic systems with switching modes. Probabilistic state space models are exploited to characterise the subsystems’ dynamical behaviours constituting a complex dynamical system, thus providing a complete description of the subsystems components. To address the variations in the operational modes of the subsystems, the Mixture Density Network (MDN) is applied here to identify the subsystems modes and provides estimates for the system dynamic distributions. Besides, to harmonise the actions between the subsystems, the probabilistic message passing methodology is utilised to provide communication between neighbouring subsystems. Based on the MDN model and the neighbours subsystems information via message passing, the general solution of the fully probabilistic decentralised randomised controller which minimises the Kullback-Leibler divergence (KLD) between the actual and its ideal distributions is then obtained. Moreover, a numerical example is presented to illustrate the effectiveness and the usefulness of our novel proposed framework.

Original languageEnglish
Pages (from-to)5451-5469
Number of pages19
JournalJournal of The Franklin Institute
Issue number10
Early online date4 Jun 2021
Publication statusPublished - 1 Jul 2021

Bibliographical note

Funding: This work was supported by the Leverhulme Trust under Grant RPG-2017-337.


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