Automatic train operation (ATO) system is one of the important components in advanced train operation control systems. Ideal controllers are expected for the automatic driving function of ATO systems. Aiming at the intelligence requirements of the systems, an NSGA-II-based parameter tuning method for the fuzzy immune PID (FI-PID) controller and a grey model GM(1,1)-based fuzzy grey immune PID (FGI-PID) controller were proposed. Taking a maglev train’s model as the control object and a velocity-time curve as the input, the feasibility of the parameter tuning method for the FI-PID controller and the applicability of the FI-PID controller and the FGI-PID controller for the ATO system were tested. The results showed that the optimized parameters were ideal, the two controllers all showed good performance on the indicators of traceability and comfort level, and the FGI-PID controller performed better than the FI-PID controller. The results exhibited the effectiveness of the proposed methods.
The safety of equipment operation is important for the operation of rail transits. The lifetime of equipment and its improvement methods have become one of the key issues in the operation and maintenance of rail transits. Based on the fuzzy comprehensive evaluation process and evaluation result data, the decision-making algorithm for improving the lifetime of the equipment was discussed, including the strategy recommendation algorithm based on assessed data and the strategy matching algorithm based on case reasoning. Taking the actual data of onboard ATC equipment of Line 11 in a city as an example, the effectiveness of the two methods was verified. The methods in the study can support the decision-making process for maintenance departments.
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