Unknown Parameter Excitation and Estimation for Complex Systems With Dynamic Performances
| Autor: | Yi-Ping Chen, Kuei-Yuan Chan |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Estimation
Estimation theory Computer science Mechanical Engineering Complex system 020101 civil engineering 02 engineering and technology Computer Graphics and Computer-Aided Design 0201 civil engineering Computer Science Applications CHAOS (operating system) 020303 mechanical engineering & transports 0203 mechanical engineering Mechanics of Materials Statistical physics Excitation |
| Zdroj: | Journal of Mechanical Design. 143 |
| ISSN: | 1528-9001 1050-0472 |
| Popis: | Simulation models play crucial roles in efficient product development cycles, therefore many studies aim to improve the confidence of a model during the validation stage. In this research, we proposed a dynamic model validation to provide accurate parameter settings for minimal output errors between simulation models and real model experiments. The optimal operations for setting parameters are developed to maximize the effects by specific model parameters while minimizing interactions. To manage the excessive costs associated with simulations of complex systems, we propose a procedure with three main features: the optimal excitation based on global sensitivity analysis (GSA) is done via metamodel techniques, for estimating parameters with the polynomial chaos-based Kalman filter, and validating the updated model based on hypothesis testing. An illustrative mathematical model was used to demonstrate the detail processes in our proposed method. We also apply our method on a vehicle dynamic case with a composite maneuver for exciting unknown model parameters such as inertial and coefficients of the tire model; the unknown model parameters were successfully estimated within a 95% credible interval. The contributions of this research are also underscored through multiple cases. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|