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Neural network compensation of semi-active control for magneto-rheological suspension with time delay uncertainty

机译：具有时滞不确定性的磁流变悬架半主动控制的神经网络补偿

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摘要

This study presents a new intelligent control method, human-simulated intelligent control (HSIC) based on the sensory motor intelligent schema (SMIS), for a magneto-rheological (MR) suspension system considering the time delay uncertainty of MR dampers. After formulating the full car dynamic model featuring four MR dampers, the HSIC based on eight SMIS is derived. A neural network model is proposed to compensate for the uncertain time delay of the MR dampers. The HSIC based on SMIS is then experimentally realized for the manufactured full vehicle MR suspension system on the basis of the dSPACE platform. Its performance is evaluated and compared under various road conditions and presented in both time and frequency domains. The results show that significant gains are made in the improvement of vehicle performance. Results include a reduction of over 35% in the acceleration peak-to-peak value of a sprung mass over a bumpy road and a reduction of over 24% in the root-mean-square (RMS) sprung mass acceleration over a random road as compared to passive suspension with typical original equipment (OE) shock absorbers. In addition, the semi-active full vehicle system via HSIC based on SMIS provides better isolation than that via the original HSIC, which can avoid the effect of the time delay uncertainty of the MR dampers.
机译：这项研究提出了一种新的智能控制方法，即基于感觉电机智能模式（SMIS）的仿人智能控制（HSIC），用于考虑MR阻尼器时滞不确定性的磁流变（MR）悬架系统。在制定了具有四个MR阻尼器的全车动态模型后，得出了基于八个SMIS的HSIC。提出了一种神经网络模型来补偿MR阻尼器的不确定时间延迟。然后，在dSPACE平台的基础上，针对制造的整车MR悬架系统，通过实验实现基于SMIS的HSIC。在各种路况下对它的性能进行评估和比较，并在时域和频域中进行介绍。结果表明，在改善车辆性能方面取得了显著成就。结果包括：在崎road不平的道路上，悬吊质量的加速度峰峰值降低35％以上，而在随机道路上，悬架质量的均方根（RMS）悬架质量加速度降低超过24％。与具有典型原始设备（OE）减震器的被动悬架相比。另外，通过基于SMIS的HSIC的半主动全车系统比通过原始HSIC的半主动全车系统具有更好的隔离性，这可以避免MR阻尼器的时延不确定性的影响。

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