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Tuning of PID controllers based on gain and phase margin specifications using fuzzy neural network

机译:基于增益和相位裕度规格的PID控制器的模糊神经网络整定

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摘要

We propose a new PID tuning method is fuzzy neural networks for a given gain and phase margin specifications (FNGP). We use fuzzy knurl networks to determine the PID controller parameters. Because the definitions of gain and phase margin equations are complex, an analytical tuning method for achieving specified gain and phase margins is not available yet. In this paper, a fuzzy neural modeling method is first proposed to identify the relationship between the gain-phase margin specifications and the PID controller parameters. Then, the FNGP is used to automatically tune the PID controllers parameter for different gain and phase margin specifications so that neither numerical methods nor graphical methods need be used. Simulation results show that the FNGP can achieve the specified values much more efficiently than other methods.
机译:对于给定的增益和相位裕度规范(FNGP),我们提出了一种新的PID调节方法,即模糊神经网络。我们使用模糊滚花网络来确定PID控制器参数。由于增益和相位裕度方程的定义很复杂,因此尚无法使用用于实现指定增益和相位裕度的分析调整方法。本文首先提出了一种模糊神经建模方法来确定增益相位裕量指标与PID控制器参数之间的关系。然后,FNGP用于针对不同的增益和相位裕度规格自动调整PID控制器参数,因此无需使用数值方法或图形方法。仿真结果表明,与其他方法相比,FNGP可以更有效地达到指定值。

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