Model Free Adaptive Predictive Iterative Learning Cooperative Control of Multiple High-Speed Trains Operation With Disturbances

For cooperative control of multiple high-speed trains (MHSTs), a new model free adaptive predictive iterative learning cooperative control (MFAPILCC) method is proposed in this paper. Firstly, the unknown nonlinearity of the MHSTs is handled with the full form dynamic linearization (FFDL) technology along the iteration domain. Then with the predictive mechanism, the unknown bounded time-iteration-varying external disturbances are estimated and predicted, and also be incorporated into the controller design, which achieves better disturbance rejection and system stability. Specially, under the proposed MFAPILCC method, apart from the convergence of tracking error of system could be guranteed, the modeling error of system which practically makes a difference in the cooperative tracking performance of MHSTs also could be guranteed to converge to zero. The convergence of the modeling error and speed tracking error of the system under the constructed method could be guranteed through rigorous theoretical derivation, while the distance between adjacent trains is always maintained within a safety range. Finally, a simulation on the Chinese Railway High-Speed Train (CRH-3) operation control system is supplied to validate the efficacy and superiority of the proposed MFAPILCC method for cooperative control of MHSTs. It is obvious that the theoretical justification and simulation effects either reflect efficacy of the proposed MFAPILCC method.