Identification of influential uncertain parameters from monte carlo simulation data

Monte Carlo simulation is powerful and practical tool for evaluating non-linear systems. Its advantage is that it allows the influences of various combinations of uncertainties to be taken into account. When the result of a Monte Carlo simulation is unsatisfactory, further investigations of both the system model and control system are necessary and it is important to identify those influential uncertain parameters that significantly affect the simulation result. However, this is usually difficult because multiple uncertain parameters are incorporated into a simulation simultaneously. This paper presents a method of identifying influential parameters in Monte Carlo simulations using test input vectors of uncertain parameters and a statistical test. The method is applied to the simulation results of an unmanned flight system, demonstrating its effectiveness in a practical application.