Optimal model predictive control of a commuter train system

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Commuter trains are widely used in urbane mass transit systems around the globe. Many of these trains are equipped with automatic speed controllers that enable them to autonomously move between destinations with little or no operator intervention. However, these control algorithms have often been designed so they can run on conventional on-board train computers with limited computational resources. This, in turn, has substantially restricted their performance and the their ability to adapt to changing operating conditions. This project brings together a team of researchers and engineers from McMaster University and Thales Canada Transportation Solutions (TCTS) with the aim of developing advanced automatic controllers for electric commuter trains to address some of the shortcomings of existing controllers. The new controllers will be based on the theory of optimal model predictive control and are expected to yield significant improvements in terms of performance and robustness to system uncertainty. Energy consumption, passenger comfort and ride quality, and the cost of operation are some of the performance metrics that will be optimized by these new controllers. The controllers will also be able to adapt, in real time, to any changes in the train and track characteristics to maintain their high performance and avoid potential system instability. This feature is also expected to significantly reduce the time the TCTS engineers would spend tuning the train speed control system in the field. The research team will also investigate efficient implementation of the control algorithms and Graphic Processor Unit-based parallel computing to enable real-time execution of the new train controllers computations. Canada will benefit from the research and development, and commercialization activities arising from this project, as well as from the training of a number of highly qualified personnel in areas of importance to a broad spectrum of Canadian companies. In addition, significant economic, environmental and social benefits are expected from the potential use of the resulting technologies from this research in Canada's transit systems.