A new expert system for active vibration control (AVC) for high-speed train moving on a flexible structure and PID optimization using MOGA and NSGA-II algorithms
Özet
As the maximum operating speeds of high-speed trains (HST) increase, the vibrations transmitted to the vehicle body increase as well. If the HST is moving on a structure such as a bridge, resonant frequencies due to train-bridge interaction (TBI) increase these vibrations. This study deals with the use of an active suspension system (ASS), artificial intelligence techniques (AIT) and PID optimization and control to reduce vibrations, which disturbs the comfort for passenger travel that occurs in HST traveling on a bridge. For this purpose, a railway bridge was modeled based on Euler–Bernoulli theory. The moving train on the bridge is modeled as 10-DOF. A linear actuator (LA) is installed in the secondary suspension systems of the train to reduce the disturbing vertical and angular accelerations in the train body. PID controller is used to control these actuators. To optimize the coefficients of the designed PID controller, a multi-objective genetic algorithm (MOGA) with six objective functions was used, taking into account passenger comfort (PC) and rail holding (RH). NSGA-II algorithm was used to find Pareto optimal front representing the solution space of the system and optimal solutions were determined. Finally, six different scenarios consisting of two different cases were evaluated in accordance with PC, France-SNCF, Eurocode and ISO 2631–1 (1997) standards to test the performance of ASS. As a result, a significant success was achieved in the development of the PC by reducing the RMS value calculated by the weighted frequency of the comfort disturbing acceleration on the passengers by 87%. © 2022, The Author(s), under exclusive licence to The Brazilian Society of Mechanical Sciences and Engineering.
Kaynak
Journal of the Brazilian Society of Mechanical Sciences and EngineeringCilt
44Sayı
4Koleksiyonlar
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