Parameter Estimation of a Valve-Controlled Cylinder System Model Based on Bench Test and Operating Data Fusion

Abstract

Abstract The accurate estimation of parameters is the premise for establishing a high-fidelity simulation model of a valve-controlled cylinder system. Although bench test data are convenient to obtain in model parameter estimation, there is a need for the load data to conform to the actual working conditions. Although the operating data include the actual load information, it is not easy to collect the control valve operating data. This paper proposes a model parameter estimation method based on bench test and operating data fusion to solve the above problems. The proposed method is based on Bayesian theory, and its core is a pool fusion of prior information from bench test and operating data. First, a system model is established, and the parameters in the model are analysed. Then, the bench test and operating data of the system are collected, and the model parameters and weight coefficients are estimated using the data fusion method. Finally, the estimated effects of the data fusion method, Bayesian method, and PSO algorithm on system model parameters are compared. The research shows that the parameter estimation result based on the data fusion method is accurate. The weight coefficient represents the contribution of different prior information to the parameter estimation result. The effect of parameter estimation based on the data fusion method is better than that of the Bayesian method and the PSO algorithm. The more complex the load is, the worse the model's accuracy, which verifies the influence of the load on the valve-controlled cylinder system model and proves that the data fusion method plays an essential role in parameter estimation studies.