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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.