Adaptive Optimal Control for Designing Automatic Train Regulation for Metro Line


Automatic train regulation (ATR) plays an necessary role in maintaining the service quality of a metro in relation to schedule and headway adherence. However, maintaining service quality in an optimal approach with less capability utilization of infrastructure, significantly in an setting where disturbances occur frequently, could be a challenge. Intrinsically, designing ATR is a real time optimal management problem with high nonlinearity, serious constraints, and stochastic characteristics. Twin heuristic programming (DHP) was successfully utilized to design ATR; but, the influence of traffic modeling error on the ATR performance was observed. During this paper, the adaptive optimal control (AOC) method is developed to improve the DHP style in regard to modeling errors also optimality, and an ATR designed using AOC technique was developed and evaluated. The analysis shows that the AOC methodology is in a position to search out a near-optimal resolution more rapidly and accurately than the DHP method. Moreover, the ATR designed using the AOC technique improves both schedule and headway adherence with less capability utilization and is a lot of strong against disturbances as well as traffic modeling errors ensuing from passenger flow fluctuations.

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