Abstract: A train control system uses artificial intelligence for maintaining synchronization between centralized and distributed train control models. A machine learning engine receives training data from a data acquisition hub, a first set of output control commands from a centralized virtual system modeling engine, and a second set of output control commands from a distributed virtual system modeling engine. The machine learning engine compares the first set of output control commands and the second set of output control commands, and trains a learning system using the training data to enable the machine learning engine to safely mitigate any difference between the first and second sets of output control commands using a learning function including at least one learning parameter.

Abstract: A train control system includes independent virtual in-train forces modelling engines onboard each of a plurality of locomotives in a train. Each of the plurality of locomotives may also include an analytics engine and a calibration engine configured to assimilate, analyze, and calibrate real time information from other locomotives and from draft gears and couplers interconnecting the locomotives with determinations made by the independent virtual in-train forces modelling engine onboard the respective locomotive, with the plurality of locomotives of the train being configured to operate collectively and coordinate their own acceleration values based on a common goal of minimizing in-train forces without being dependent on a command from a lead locomotive or central command.

Abstract: A train control system uses artificial intelligence for maintaining synchronization between centralized and distributed train control models. A machine learning engine receives training data from a data acquisition hub, a first set of output control commands from a centralized virtual system modeling engine, and a second set of output control commands from a distributed virtual system modeling engine. The machine learning engine compares the first set of output control commands and the second set of output control commands, and trains a learning system using the training data to enable the machine learning engine to safely mitigate any difference between the first and second sets of output control commands using a learning function including at least one learning parameter.