Abstract
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.
Highlights
Railways have the advantages of large volume, speed, safety, and low pollution
Where the first optimization object is integrated absolute error (IAE) [10, 53], which considers the overall error ; the second optimization object is integral of time’s reciprocal multiplied by squared error (ITRSE), which focuses on the error in the initial stage, and less considers the error in the later stage; the third optimization object is the integral of time multiplied by squared error (ITSE) [9, 10], which less considers the error in the initial stage, but focuses on errors in later stage; m is the length of an error sequence, e(t) is the tth error in error sequence, and m and t are all integers in the study
The simulations were used to analyze the applicability of the fuzzy immune PID (FI-PID) controller and the fuzzy grey immune PID (FGI-PID) controller for the automatic train operation (ATO) system. e applicability of the two controllers for ATO system had been analysed from the aspect of the traceability and comfort based on output results
Summary
Railways have the advantages of large volume, speed, safety, and low pollution. E railway technology worldwide tends to develop in the directions of seriation, specialization, and diversification. For improving comfort and stopping accuracy, and energy saving, a driverless train needs a very stable ATO system. E speed curve and the speed curve tracking ability are two keys to optimize ATO systems [1,2,3]. E accuracy of speed curve tracking is related to the adopted control method. E proportion-integration-differentiation (PID) controller is widely used in industrial control, due to its advantages of simple control structure, wide application, and easy to implement. Most industrial processes, including the ATO systems, have dynamic characteristics. Once the control parameters are fixed, the ideal stopping accuracy and comfort cannot be completely guaranteed. Once the control parameters are fixed, the ideal stopping accuracy and comfort cannot be completely guaranteed. erefore, the tuning and self-tuning of the PID controller or other controllers have attracted much attention
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