Genetic Algorithm Based Energy-Saving ATO Control Algorithm for CBTC
Keywords:CBTC, Energy-Saving, Genetic Algorithm, Automatic Train Operation
AbstractTo improve their carrying capacities, multiple trains can operate on one line. Urban rail transit employs a Communication-Based Train Control (CBTC) system to realize a movable block, which is applied to decrease the headway. In a CBTC system, trains only know the speed limit within the scope of the Movement Authority Limit (MAL). An energy-saving Automatic Train Operation (ATO) control algorithm based on a genetic algorithm (GA) is proposed to control multi-train movements with incomplete information about speed limits. This algorithm is composed of two layers: a search layer that applies a GA to search for the optimal control solution and a protection layer that helps trains prevent overspeed. The GA in this paper tends to achieve optimal solutions using variable length chromosomes and a novel fitness function. The simulation results indicate that the proposed algorithm achieves optimal energy-saving benefits compared with other control strategies.
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