Towards Improving the Performance of the RNN-based Inversion Model in Output Tracking Control
Towards Improving the Performance of the RNN-based Inversion Model in Output Tracking Control
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DOI：
10.23919/acc45564.2020.9147964
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发表时间：
2019-12
期刊：
影响因子：
--
通讯作者：
S. Xie;Juan Ren
中科院分区：
文献类型：
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作者：
S. Xie;Juan Ren
基于递归神经网络（RNN）的逆模型控制具有建模精度高、带宽大的优点，被提出用于输出跟踪。然而，在使用基于RNN的反演模型时，仍然需要解决一些问题。首先，由于RNN中的参数数量有限，它无法准确地对低频动态进行建模，因此使用了额外的线性模型，这可能会干扰高频下的跟踪控制。此外，由于控制采样速度受到RNN训练集长度的限制，控制速度和RNN建模精度不能同时提高。因此，本文重点讨论基于RNN的逆模型控制的这些局限性。具体而言，提出了一种新的建模方法，以不影响RNN实现的现有高频控制性能的方式并入线性模型。此外，还提出了一种插值方法，使采样频率加倍（与RNN训练采样频率相比）。分析所提出的新模型用于预测控制时可能出现的稳定性问题，沿着给出了确定参数以确保闭环稳定性的说明。最后，在一个商业压电驱动器上进行了实验，实验结果表明，该方法可以显着提高跟踪性能。
With the advantages of high modeling accuracy and large bandwidth, recurrent neural network (RNN) based inversion model control has been proposed for output tracking. However, some issues still need to be addressed when using the RNN-based inversion model. First, with limited number of parameters in RNN, it cannot model the low-frequency dynamics accurately, thus an extra linear model has been used, which can become an interference for tracking control at high frequencies. Moreover, the control speed and the RNN modeling accuracy cannot be improved simultaneously as the control sampling speed is restricted by the length of the RNN training set. Therefore, this article focuses on addressing these limitations of RNN-based inversion model control. Specifically, a novel modeling method is proposed to incorporate the linear model in a way that it does not affect the existing high- frequency control performance achieved by RNN. Additionally, an interpolation method is proposed to double the sampling frequency (compared to the RNN training sampling frequency). Analysis on the stability issues which may arise when the proposed new model is used for predictive control is presented along with the instructions on determining the parameters for ensuring the closed-loop stability. Finally, the proposed approach is demonstrated on a commercial piezo actuator, and the experiment results show that the tracking performances can be significantly improved.