Optimal Uncertain Parameter Excitation and Estimation: a Case Study on Vehicle Model Development
Date:
I was invited as the keynote speaker for the Matlab Expo 2020 in Taiwan for winning the Best Paper Award in the Matlab Thesis Competition. In this talk, I introduced my master’s thesis. This talk was pre-recorded due to Covid.
Simulation models play important roles in efficient product development cycles. The ability to improve the confidence level of models during the validation stage is also an important topic. In this research, we proposed a systematic procedure on model validation by assuming all the output errors between simulation models and real model experiments are contributed from deviations of model parameters. This procedure aims to counter the inability to create a proper and logical operation when validating dynamic models.
In this research, considering the expensive costs associated with model simulation used in complex systems, a Design and Analysis of Computer Simulation (DACE) based procedure including an optimization method for generating a proper operation which maximizes the sensitivity of uncertain parameters based on global sensitivity analysis (GSA), estimation of parameters with polynomial chaos-based Kalman filter, and model validation based on hypothesis testing, is introduced. Furthermore, two illustrative math models with scalar and dynamic output are demonstrated to verify the method, consequently proving its generality. Finally, an application on validating vehicle dynamic models is shown as an engineering case, which successfully estimates the unknown model parameters with 95\% confidence. The significance of this research is also emphasized through multiple cases.