Dynamic data reconciliation to enhance the performance of model free adaptive control
Dynamic data reconciliation to enhance the performance of model free adaptive control
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DOI：
10.1088/1361-6501/acbc92
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发表时间：
2023-02
影响因子：
2.4
通讯作者：
T. Xia;Zhengjiang Zhang;Zhihui Hong;Shipei Huang
中科院分区：
文献类型：
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作者：
T. Xia;Zhengjiang Zhang;Zhihui Hong;Shipei Huang
无模型自适应控制（MFAC）作为一种新型的数据驱动控制方法，对反馈信号的精度有很高的要求。然而，用于获取输出的传感器由于自身误差或外部干扰，不可避免地含有测量噪声，这可能会对控制性能产生不利影响。本文提出了动态数据协调（DDR）并与MFAC结合来提高控制性能，考虑高斯和非高斯分布式测量噪声，使用预测输出和测量数据来抑制测量噪声。 DDR与MFAC（DDR-MFAC）相结合的有效性在具有高斯和非高斯分布测量噪声的单输入单输出和多输入多输出系统中得到了证明。 DDR-MFAC也成功应用于DC-AC转换器，提高了其转换精度。
As a novel data-driven control method, model-free adaptive control (MFAC) has a high requirement for the accuracy of the feedback signal. However, the sensor used to obtain the output inevitably contains measurement noise due to its own error or external interference, which may lead to an adverse effect on the control performance. The dynamic data reconciliation (DDR) is proposed and combined with MFAC to improve the control performance in this paper, which uses predicted output and measured data to suppress measurement noise considering Gaussian and non-Gaussian distributed measurement noise. The effectiveness of the DDR combined with MFAC (DDR-MFAC) is illustrated in the single-input single-output and multiple-input multiple-output systems with Gaussian and non-Gaussian distributed measurement noise. DDR-MFAC is also successfully applied to DC–AC converter, which improves its conversion precision.