基於增益餘量與相位餘量之非穩定系統PID控制器的調整方法：利用模糊類神經網路

Abstract

在本論文中，我們提出一個根據增益邊際與相位邊際的規格，用模糊類神網路經來決定PID控制器的參數。過去PID控制器已經很廣泛地應用在穩定系統中，但對於開迴路的非穩定系統的PID控制器卻較少提到。在本文中，我們針對開迴路非穩定系統，先用模糊類神經網路去訓練增益邊際與相位邊際的規格與PID控制器參數間的關係之後，再利用已訓練過的網路去得到一組符合使用者所需求的增益邊際與相位邊際的規格之PID控制器參數，而不需靠任何的數值分析或作圖法來決定。此網路即使給的規格可能不合理，它依舊會給我們一組合理卻又離規格比較近的PID控制器參數。從模擬中可知模糊類神經網路可以有效率地達到所要求的規格。

In the thesis, we present a PID tuning method for unstable processes using Fuzzy Neural Network based on gain and phase margin (FNGP) specifications. PID tuning methods were widely used to control stable processes. However, PID control for unstable processes is less common. A fuzzy neural network approach is proposed to identify the relationship between the gain-phase margin specifications and the PID controller parameters. Then, the FNN is used to automatically tune the PID controller parameters for different gain and phase margin specifications so that neither numerical methods nor graphical methods need be used. Even though for some of the unreasonable specifications, the FNN still can find a suitable PID controllers' parameters close to the specifications. Simulation results show that the FNN can achieve the specified values efficiently.