本論文旨在將類神經網路應用於原子力顯微鏡探針系統的控制器參數調整，期望能改善以往需要人工以經驗或試誤法調整的方式，以能減少參數調整的時間以及增加系統控制上的精確度為目標。首先由漢米頓定理(Hamilton’s principle)推導出系統動態方程式，經由拉普拉斯轉換(Laplace transform)求出系統的開迴路轉移函數，利用將致動器與感測器置於相同位置的共點控制，系統的極零點會呈現相互交錯的分佈特性，藉由根軌跡法(Root locus method)分析設計系統控制器。結果顯示比例微分控制器能將系統穩定且具有不產生最大超越量的性能，而採用類神經網路作為參數調整的自調式類神經比例微分控制器除了具有上述優點之外，亦能減少系統的安定時間，有效的提升系統性能。

The objective of this thesis is to use neural network for tuning the controller parameters of the atomic force microscopy probe measurement system, with the aim to improve the traditional approach, which replies on experiences and trial-and-error in industrial application. In this thesis, an improved neuro-PD controller is provided to reduce the time in tuning parameters and increase the precision of the control system. First, the dynamic equations of this system are derived based on Hamilton’s principle, and the open loop transfer function is obtained by using the Laplace transform. The poles and zeros of system have the characteristic of interlace due to collocated control. Moreover, this system can be analyzed and designed by using the root locus method. The computer simulate results show that the PD controller can stabilize the system and reduce the overshoot. Also, the self-tuning neuro-PD controller not only has this advantage but also can reduce the settling time of the system, and effectively enhance the system performance.

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