According to the method, T-S fuzzy logic and improved heuristic empirical sample reinforcement learning are combined, a hydraulic model is selected for modeling a semi-active suspension system, T-S fuzzy is utilized for nonlinear system modeling, and a fuzzy model of the suspension system is established through fuzzy reasoning output. The suspension data is preprocessed, the problem of low sampling rate in the early stage of reinforcement learning is improved, and the vehicle can still keep good suspension performance under the condition of personnel change.

    本发明将T‑S模糊逻辑与改进的启发式经验样本强化学习相结合,选用液压模型建模半主动悬架系统,并利用T‑S模糊进行非线性系统建模,通过模糊推理输出建立悬架系统的模糊模型。对悬架数据进行预处理,改进强化学习前期采样速率低的问题,使得车辆在有人员变动的情形下依然能保持良好的悬架性能。


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    Title :

    Semi-active hydraulic suspension multi-objective optimization reinforcement learning control method based on T-S fuzzy


    Additional title:

    一种基于T-S模糊的半主动液压悬架多目标优化强化学习控制方法


    Contributors:
    HAN SHIYUAN (author) / HE GUANGMING (author) / LIANG TONG (author) / WANG YALI (author) / ZHOU JING (author) / SONG JINXIANG (author) / YANG JUN (author)

    Publication date :

    2024-02-20


    Type of media :

    Patent


    Type of material :

    Electronic Resource


    Language :

    Chinese


    Classification :

    IPC:    B60G VEHICLE SUSPENSION ARRANGEMENTS , Radaufhängungen und Federungen für Fahrzeuge / G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen



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