Intelligent Control Strategy of Vehicle Active Suspension Based on Deep Reinforcement Learning

Zhu, Xia; Chen, Zhaoqun; Zhang, Sheng; Zhang, Cirun

doi:10.1109/cac57257.2022.10054782

Cited by 1 publication

(1 citation statement)

References 15 publications

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“…Using the MLP as part of the DQN model allows the system to use the features extracted by the MLP to evaluate the potential value of taking different actions in the current state. By learning and utilizing the information contained in the current signal, the agent can continuously optimize its action strategy to deal with various abnormal situations [29]. In addition, the continuous learning and adaptation process enables the system to cope with new or unforeseen abnormal patterns, improving the generalization ability and practical value of the method.…”

Section: Model Optimizationmentioning

confidence: 99%

Health Monitoring Analysis of an Urban Rail Transit Switch Machine

Wang,

Sun,

Zhou

et al. 2024

Sustainability

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This paper discusses the health evaluation of an urban rail transit switch machine. In this paper, the working current data of the S700K switch machine are processed, and four common abnormal operating current curves are obtained through the existing data. Then, the MLP is used as the feature extractor of the action current curve to analyze the input action current data, learn and capture deep features from raw current data as Q-networks, and build MLP-DQN models. The monitoring of the abnormal state operation current of the switch machine is optimized by learning and optimizing the model weight through repeated experience. The experimental results show that the training accuracy of this model is stable at about 96.67%. Finally, the Fréchet distance was used to analyze the abnormal motion current curve, combined with the occurrence frequency and repair complexity of the abnormal type curve, the calculated results were analyzed, and the health of the switch machine was evaluated, which proved the high efficiency and superiority of the MLP-DQN method in the fault diagnosis of the switch machine equipment. The good health evaluation function of the switch machine can effectively support the maintenance of the equipment, and it has an important reference value for the intelligent operation and maintenance of subway signal equipment. The research results mark the maintenance of key equipment of urban rail transit systems, represent a solid step towards intelligent and automated transformation, and provide strong technical support for the safe operation and intelligent management of future rail transit systems.

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Section: Model Optimizationmentioning

confidence: 99%