Performance assessment and design of abstracted models for stochastic hybrid systems through a randomized approach

TL;DR

This paper introduces a simulation-based randomized method for evaluating and designing abstracted models of stochastic hybrid systems, ensuring model accuracy with probabilistic guarantees without system-specific assumptions.

Contribution

It presents a versatile, simulation-driven approach to assess and design epsilon-abstracted models for stochastic hybrid systems using chance-constrained optimization.

Findings

Method effectively evaluates model accuracy over finite samples.

No specific assumptions required on the system besides simulation capability.

Provides probabilistic guarantees for model performance.

Abstract

In this paper, a simulation-based method for the analysis and design of abstracted models for a stochastic hybrid system is proposed. The accuracy of a model is evaluated in terms of its capability to reproduce the system output for all the realizations of the stochastic input except for a set of (small) probability epsilon (epsilon-abstraction). This naturally leads to chance-constrained optimization problems, which are here tackled by means of a recently developed randomized approach. The main thrust of this paper is that, by testing how close the model and system outputs are over a finite number N of input realizations only, conclusions can be drawn about the model capability as an epsilon-abstraction. The key feature of the proposed method is its high versatility since it does not require specific assumptions on the system to be approximated. The only requirement is that of being able to run multiple simulations of the system behavior for different input realizations.