曾煥雯Tzeng, Huan-Wen董倫騰TUNG, LUN TENG2019-09-032008-5-202019-09-032007http://etds.lib.ntnu.edu.tw/cgi-bin/gs32/gsweb.cgi?o=dstdcdr&s=id=%22GN0692730150%22.&%22.id.&http://rportal.lib.ntnu.edu.tw:80/handle/20.500.12235/97134本論文提出一個“可調整擺錘”之倒單擺追蹤系統的研究。所採用的控制法則，為極點配置法與自適應網路模糊推論系統(ANFIS) 設計方法，各別運用於系統的平衡定位控制與平衡追蹤控制。 依據工程力學的原理，完成“可調整擺錘”之倒單擺的數學模型推導，並將其線性化以求出輸入量與輸出量的關係式。以此關係式利用極點配置設計方法，完成了平衡定位控制的模擬。此外也針對平衡追蹤控制，運用自適應網路模糊推論系統學習產生模糊控制器，提出可行的控制的方法。 比較其結果，平衡定位控制為平衡追蹤控制的特例，但以自適應網路模糊推論系統學習所得到的模糊控制法則較極點配置法之性能為佳。以自適應網路模糊推論系統所學習而得的模糊控制器具有較佳的效率和強健性，不但可以在平衡追蹤控制有不錯的表現，亦可運用在平衡定位控制上。This research proposed a tracing controller system for an inverted pendulum with an adjustable clapper. The pole placement method and ANFIS(Adaptive Network-Based Fuzzy Inference System) are applied to position-balance control and trace-control individually. According to the principle of engineering mechanics, we’ve obtained a linearized input-output expression from the mathematics model of the controlled object. Afterwards this research could simulate balance-positioning control using pole-placement method from the derived expression. We also pin-point the balance-tracing control system using ANFIS method and produce a feasible controlling method. By comparing “balance-positioning” and “balance-tracing” methods, we conclude the ANFIS method is better than the pole-placement method. This research obtained the fuzzy controller from the learning of ANFIS, it has a superior performance and robustness, not only would it perform well in balance-tracing but also in balance-positioning as well.倒單擺可調整擺錘極點配置自適應網路模糊推論系統Adaptive Network-Based Fuzzy Inference SystemANFISpole placementinverted pendulumadjustable clapper