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GRU-Type LARC Strategy for Precision Motion Control with Accurate Tracking Error Prediction
IEEE Transactions on Industrial Electronics ( IF 7.7 ) Pub Date : 2021-01-01 , DOI: 10.1109/tie.2020.2991997
Chuxiong Hu , Tiansheng Ou , Yu Zhu , Limin Zhu

To simultaneously achieve accurate tracking error prediction, rigorous motion accuracy, and certain robustness to parameter variations and unknown disturbances, this article proposes a data-based learning adaptive robust control (LARC) strategy based on gated recurrent unit (GRU) neural network. Firstly, parameter adaptive control and robust control are utilized to guarantee the robustness against parametric uncertainties and unknown disturbances. A GRU neural network is then constructed and capable of precisely predicting the tracking error after training with data collected from a linear-motor-driven stage. Essentially, the GRU network can be viewed as a data-based model, which captures the tracking error dynamic characteristics and provides a prediction even before implementing the real trajectory. Consequently, a reference modification and a feedforward compensation part can be formed, which is the significant part of the whole LARC control structure. Comparative experimental investigation not only validates the effectiveness of the tracking error prediction ability, but also demonstrates the practically satisfactory transient/steady-state tracking performance of the proposed control strategy.

中文翻译:

具有精确跟踪误差预测的用于精密运动控制的 GRU 型 LARC 策略

为了同时实现精确的跟踪误差预测、严格的运动精度以及对参数变化和未知扰动的一定鲁棒性,本文提出了一种基于门控循环单元(GRU)神经网络的基于数据的学习自适应鲁棒控制(LARC)策略。首先,利用参数自适应控制和鲁棒控制来保证对参数不确定性和未知扰动的鲁棒性。然后构建一个 GRU 神经网络,并能够在使用从线性电机驱动阶段收集的数据进行训练后精确预测跟踪误差。从本质上讲,GRU 网络可以被视为一个基于数据的模型,它捕捉跟踪误差的动态特性,甚至在实现真实轨迹之前就提供预测。最后,可以形成参考修改和前馈补偿部分,这是整个LARC控制结构的重要部分。对比实验研究不仅验证了跟踪误差预测能力的有效性,而且证明了所提出的控制策略在实际应用中令人满意的瞬态/稳态跟踪性能。
更新日期:2021-01-01
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