Multi-ANFIS Model Based Synchronous Tracking Control of High-Speed Electric Multiple Unit

A high-speed electric multiple unit (HSEMU) is a power-distributed train consisting of multiple trailers and motor cars, which is becoming a very handy and powerful transportation tool for our daily life. It is critical to control HSEMU synchronously for improving system performances, but challenging to be achieved due to environmental uncertainties, coupling, and nonlinearity. This paper aims to develop advanced modeling and control techniques to achieve the synchronous tracking control of each car body with harmonic motion. To characterize the dynamics of HSEMU, a multi-adaptive neurofuzzy inference systems (ANFIS) model is built through learning sample data collected from real runs. Then, a model predictive controller is designed to implement the synchronous tracking control of HSEMU on the basis of the multi-ANFIS model. A stability analysis of the closed-loop control system is given. Simulation experiments with comparisons on real HSEMU running data are carried out. Results demonstrate that our proposed modeling and control techniques significantly improve the operational performance of HSEMU in terms of safety, punctuality, comfort, and parking accuracy.