An improved predictive function control algorithm via wavelet neural network for urban rail train tracking control

Abstract

This study proposes a novel, effective improved predictive function control based on a wavelet neural network (IPFC-WNN) for urban rail train tracking control. Specifically, the step function and Morlet wavelet function were chosen as the base function together, and an adaptive nonlinear online adjustment function of the softening factor was proposed based on the fuzzy satisfaction of system performance and optimisation factor. The maximum and minimum softening factors for a simple straight line can also be set appropriately by a wavelet neural network according to the actual situation. To effectively improve the control performance of the predictive function control algorithm for urban rail train tracking, calculation of additional resistance with a multiparticle model was adopted, and parameters for the adaptive nonlinear online softening factor adjustment function were set using a wavelet neural network to improve the comprehensive performance quality for urban rail train tracking control. Considering the scenario of urban rail train tracking control from Bayi Road to Yongan Four Seasons, which is located in the second-phase project of Dalian Urban Rail Transit Line 13, as the hardware-in-the-loop test object, the proposed IPFC-WNN and three improved control algorithms were used for comparative verification. The test results showed that the proposed IPFC-WNN can significantly improve the performance of the control system, and quality indicators such as energy saving, precise parking, punctuality, and comfort of the system were significantly improved. Hence, the good tracking control for train operation using the proposed IPFC-WNN was verified.