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Study on Fuzzy-Neural Network for Inverted Plasma Arc Cutting Power Supply

机译：逆变等离子弧切割电源的模糊神经网络研究

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摘要

A variable interval fuzzy quantification algorithm with self-adjustable factor in full domain is proposed in this paper. It focuses on digital inverted plasma arc cutting power and studies strong nonlinearity and uncertainty of power. The neural network is also introduced to decouple cutting parameters variables in the multi-parameters coupling cutting process. This algorithm avoids complex nonlinear system modeling and realizes real-time and effective online control of cutting process by combining advantages of fuzzy control and neural network control. Furthermore, the optimized fuzzy control improves steady-state precision and dynamic performance of system simultaneously. The experimental result shows that this control improves precision, ripples, finish and other comprehensive index of work piece cut, and plasma arc cutting power supply based on fuzzy-neural network has excellent control performance.
机译：提出了一种全域自调整因子的可变区间模糊量化算法。它着重于数字反向等离子弧切割功率，并研究强大的非线性和功率不确定性。还引入了神经网络以在多参数耦合切削过程中使切削参数变量解耦。该算法避免了复杂的非线性系统建模，并结合了模糊控制和神经网络控制的优点，实现了切削过程的实时有效在线控制。此外，优化的模糊控制可同时提高系统的稳态精度和动态性能。实验结果表明，该控制方法提高了工件切割的精度，波纹度，光洁度等综合指标，基于模糊神经网络的等离子弧切割电源具有优良的控制性能。

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