FMI기반 co-simulation에서 step size control을 위한 Markov chain을 사용한 예측 방법
A Prediction Method using Markov chain for Step Size Control in FMI based Co-simulation
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초록
FMI를 기반으로 하는 co-simulation의 마스터 알고리즘(MA)에서 시뮬레이션 결과의 정확도를 높이는 방법으로 zerocrossing 포인트를 찾기 위한 Bisectional algorithm을 사용할 수 있다. 그러나 이 알고리즘은 많은 Rollback을 야기한다. 따라서 본 논문에서는 제안하는 MA는 Bisection algorithm을 통해 zero crossing 포인트를 검출하면서도 반복되는 구간 그래프를 분석하여 그 값을 Markov chain을 적용하여 다음 구간을 예측하여 이를 step size에 적용한다. 시뮬레이션에서 실제Rollback이 발생했을 때 그래프 형태별로 변화되는 step size를 배열로 저장하고, 이룰 다음 예측 구간에 적용함으로서Rollback을 최소화하는 알고리즘을 제안한다. 시뮬레이션 결과를 통해 제안하는 알고리즘이 기존 알고리즘에 비해 최대 20%이상의 시뮬레이션 시간이 감소되는 것을 확인하였다.
Abstract
In Functional Mockup Interface(FMI)-based co-simulation, a bisectional algorithm can be used to find the zerocrossingpoint as a way to improve the accuracy of the simulation results. In this paper, the proposed masteralgorithm(MA) analyzes the repeated interval graph and predicts the next interval by applying the Markov Chain to thestep size. In the simulation, we propose an algorithm to minimize the rollback by storing the step size that changesaccording to the graph type as an array and applying it to the next prediction interval when the rollback occurs in thesimulation. Simulation results show that the proposed algorithm reduces the simulation time by more than 20% comparedto the existing algorithm.
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