Parameter Adjustment Based on Genetic Algorithm for Adaptive Periodic-Disturbance Observer
Parameter Adjustment Based on Genetic Algorithm for Adaptive Periodic-Disturbance Observer
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DOI：
10.1109/iecon.2019.8926764
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发表时间：
2019-10
期刊：
影响因子：
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通讯作者：
Xiao Feng;Hisayoshi Muramatsu;S. Katsura
中科院分区：
文献类型：
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作者：
Xiao Feng;Hisayoshi Muramatsu;S. Katsura
在工业生产中，机器的重复操作会产生周期性的扰动。由于周期扰动会影响加工精度，因此对周期扰动的补偿是保证机床正常工作的一个重要问题。为了消除周期扰动，提出了一种自适应周期扰动观测器（APDOB），作为一种有效的估计和补偿变频周期扰动的方法。但是，由于APDOB有6个设计参数，需要进行经验调整，因此存在设计复杂的问题。在此，我们提出了一种基于遗传算法（GA）的方法来自动调整6个设计参数。该方法可以消除传统的经验设计。此外，通过优化变异算子，lsamvy飞行可以提高遗传算法的搜索能力，遗传算法找到的包含lsamvy飞行的最优解可以提高APDOB的性能。
Periodic disturbances occur during repetitive operation of machines in industrial production. Compensation for the periodic disturbances is an important issue to realize proper machine works beacause the periodic disturbances deteriorate machining precision. In order to eliminate the periodic disturbances, an adaptive periodic-disturbance observer (APDOB) has been proposed as an effective method that can also estimate and compensate for frequency-varying periodic disturbances. However, the APDOB has a problem that design of the APDOB is complicated owing to its six design parameters, which need to be empirically adjusted. Here, we propose an approach based on a genetic algorithm (GA) including a Lévy flight to automatically adjust the six design parameters. The proposed method can remove the conventional empirical design. Moreover, the Lévy flight could improve the exploration ability of the GA by optimizing mutation operator and the best solution found by the GA including Lévy flight could improve the performance of the APDOB.