Parameter Identification of Train Basic Resistance Using Multi-Innovation Theory

Abstract

Train basic resistance is important for the design of the automatic train operation, which influences the efficiency, punctuality, stop precision, energy consumption, and the safety of the train. The multi-innovation theory is a novel concept which can improve the accuracy of parameter estimation and be used to modify the traditional recursive least squares algorithm. In this paper, we derive the regularization form of the multi-innovation least squares algorithm and apply it to the train basic resistance parameter estimation. The simulation results based on the Yizhuang Line of Beijing Subway indicate that, compared with traditional least squares algorithm, the multi-innovation least squares algorithm can provide higher estimation accuracy and robustness, and can be used for online identification.