Adhesion Force Estimation of Railway Vehicles using Intelligent Square Root Cubature Kalman Filter

Railway traction vehicles transfer forces between rails and wheels through an adhesion ‎coefficient. In order to prevent wheel locking and shorten stopping distances, ‎estimating the adhesion conditions between rails and wheels is an essential task in ‎railway operations. Since the adhesion condition is influenced thru many factors, its ‎estimation technique is complex. This paper presents an intelligent square root ‎cubature Kalman filter (ISRCKF) to estimate adhesion force. The proposed method ‎has the advantage that it does not require to know the noise statistics. This method ‎integrates the differential evolution (DE) algorithm to tune the SRCKF by solving the ‎optimal values of the covariance matrix Q and measurement noise matrix R. It can ‎also decrease the error because of unknown noise, and increase the accuracy. ‎Furthermore, it exhibits a consistent enhancement in numerical stability due to the ‎assurance that all resultant covariance matrices remain positive semi-definite. This ‎innovative approach plays an active role in optimizing the utilization of the current ‎adhesion while reducing wheel wear by mitigating high creep values. The outcomes ‎demonstrate that the suggested approach yields superior estimation accuracy and ‎exhibits a swifter convergence rate in comparison to alternative methods.‎

کلیدواژه ها:

Adhesion force ، Square root ‎cubature Kalman filter ، ‎Railway vehicles ، Differential ‎evolution (DE)‎