As an important component of semi-active suspension (SAS) systems, the built-in solenoid valve adjustable damper (BSVAD) is now widely used in the field because of its advantages of fast response, continuously adjustable damping, and lightweight design. However, only a few studies have discussed the BSVAD-modeling method. Hence, this study proposes a method for constructing an agent model using a feedforward neural network (FNN) for an equivalent BSVAD regulation mechanism. Meanwhile, a new control strategy, i.e., a fuzzy sliding mode control method based on road conditions (RC-FSMC), is proposed for calculating the required damping force. The proposed method identifies and analyzes unsprung mass acceleration characteristics using a long short-term memory network to obtain the road condition of the moving vehicle and inputs the output results into the designed fuzzy rules to adjust the sliding mode switching gain and finally realize road condition-based adaptive control. Finally, through simulation test analysis, the results show that the designed RC-FSMC strategy can effectively reduce vehicle body acceleration and vehicle dynamic load under different road conditions to improve the adaptability of the vehicle to different roads.

中文翻译：

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基于LSTM网络道路识别的悬架系统自校正模糊滑模控制方法

作为半主动悬架（SAS）系统的重要组成部分，内置电磁阀可调阻尼器（BSVAD）因其响应快、阻尼连续可调、轻量化设计等优点而在现场得到广泛应用。然而，只有少数研究讨论了 BSVAD 建模方法。因此，本研究提出了一种使用前馈神经网络（FNN）构建代理模型的方法，以实现等效的 BSVAD 调节机制。同时，提出了一种新的控制策略，即基于路况的模糊滑模控制方法（RC-FSMC）来计算所需的阻尼力。该方法利用长短期记忆网络对非簧载质量加速度特性进行识别和分析，获取行驶车辆的路况，并将输出结果输入到设计的模糊规则中，调整滑模切换增益，最终实现路况-基于自适应控制。最后，通过仿真测试分析，结果表明所设计的RC-FSMC策略能够有效降低不同路况下的车身加速度和车辆动载荷，提高车辆对不同道路的适应性。