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Published in 2024 IEEE Conference on Decision and Control, 2024
A stochastic simulator that approximates the behavior of a hybrid system given in terms of a hybrid inclusion is considered. The simulator is a stochastic discrete-time system that employs a set-valued right-hand side when approximating flows. Under mild conditions on the data defining the simulator and the hybrid system, together with a non-uniform averaging condition, it is shown that almost every sample path of each solution generated by the stochastic simulator is close to a solution of the original hybrid system on compact time domains when the step size sequence is sufficiently small and converges to zero but is not summable.
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Published in arXiv Preprint, 2026
Convergence properties of model-free two-timescale asymptotic simulations of singularly perturbed hybrid inclusions are developed. A hybrid inclusion combines constrained differential and difference inclusions to capture continuous (flow) and discrete (jump) dynamics, respectively. Sufficient conditions are established under which sequences of iterates and step sizes constitute a two-timescale asymptotic simulation of such a system, with limiting behavior characterized via weakly invariant and internally chain-transitive sets of an associated boundary layer and reduced system. To illustrate the applicability of these results, conditions are given under which a two-timescale stochastic approximation of a hybrid optimization algorithm asymptotically recovers the behavior of its deterministic counterpart.
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Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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