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基于改进模糊神经网络PID控制的两级半主动ISD悬架研究

Research on Two-Stage Semi-Active ISD Suspension Based on Improved Fuzzy Neural Network PID Control.

作者信息

Jin Linhao, Fan Jingjing, Du Fu, Zhan Ming

机构信息

School of Electrical and Control Engineering, North China University of Technology, Beijing 100144, China.

School of Mechanical and Vehicular Engineering, Beijing Institute of Technology, Beijing 100081, China.

出版信息

Sensors (Basel). 2023 Oct 11;23(20):8388. doi: 10.3390/s23208388.

DOI:10.3390/s23208388
PMID:37896482
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10611147/
Abstract

To better improve the ride comfort and handling stability of vehicles, a new two-stage ISD semi-active suspension structure is designed, which consists of the three elements, including an adjustable damper, spring, and inerter. Meanwhile, a new semi-active ISD suspension control strategy is proposed based on this structure. Firstly, the fuzzy neural network's initial parameters are optimized using the grey wolf optimization algorithm. Then, the fuzzy neural network with the optimal parameters is adjusted to the PID parameters. Finally, a 1/4 2-degree-of-freedom ISD semi-active suspension model is constructed in Matlab/Simulink, and the dynamics simulation is carried out for the three schemes using PID control, fuzzy neural network PID control, and improved fuzzy neural network PID control, respectively. The results show that compared with adopting PID control and fuzzy neural network PID control strategy, the vehicle body acceleration and tire dynamic loads are significantly reduced after using the grey wolf optimized fuzzy neural network PID control strategy, which shows that the control strategy proposed in this paper can significantly improve the vehicle smoothness and the stability of the handling.

摘要

为了更好地提高车辆的行驶舒适性和操纵稳定性，设计了一种新型的两级ISD半主动悬架结构，它由可调阻尼器、弹簧和惯性器这三个元件组成。同时，基于该结构提出了一种新的半主动ISD悬架控制策略。首先，利用灰狼优化算法对模糊神经网络的初始参数进行优化。然后，将具有最优参数的模糊神经网络调整为PID参数。最后，在Matlab/Simulink中构建了1/4两自由度ISD半主动悬架模型，并分别对采用PID控制、模糊神经网络PID控制和改进的模糊神经网络PID控制的三种方案进行了动力学仿真。结果表明，与采用PID控制和模糊神经网络PID控制策略相比，采用灰狼优化模糊神经网络PID控制策略后，车身加速度和轮胎动载荷显著降低，表明本文提出的控制策略能显著提高车辆的平顺性和操纵稳定性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2f9a/10611147/6be2082d01ab/sensors-23-08388-g014.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2f9a/10611147/8ba537d7d02d/sensors-23-08388-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2f9a/10611147/c6d9e5c1e34d/sensors-23-08388-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2f9a/10611147/eed4375f8695/sensors-23-08388-g011.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2f9a/10611147/6be2082d01ab/sensors-23-08388-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2f9a/10611147/5e0d95a79ccc/sensors-23-08388-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2f9a/10611147/122a312875fe/sensors-23-08388-g002.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2f9a/10611147/76d18886f0b3/sensors-23-08388-g005.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2f9a/10611147/4db4ce923584/sensors-23-08388-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2f9a/10611147/8ba537d7d02d/sensors-23-08388-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2f9a/10611147/c6d9e5c1e34d/sensors-23-08388-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2f9a/10611147/eed4375f8695/sensors-23-08388-g011.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2f9a/10611147/6be2082d01ab/sensors-23-08388-g014.jpg

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