本文以「光機電系統控制研究室」研究多年的微夾持系統為基礎，針對類神經、解析、模糊三種Inverse Preisach補償方法，分析比較其優劣。透過對影像雜訊進行量測，濾波來降低雜訊對Inverse Preisach補償以及控制上的負面效果。並量測系統之模型誤差，以及分析其原因，並從控制結構與控制策略上，有效降低模型誤差影響。而控制器部分，由於系統具有時變性，因此分別設計自調式控制，模糊控制器，並且比較其優劣，最後將夾持器致動器與控制器，三方面整合，作定位控制，以及夾持物件。

This thesis is based on the research of shape-memory-alloy (SMA) actuator system developed by “OME System Lab” in recent years. For the inverse Preisach compensation, we compared the performance of three methods including analytic method, neural network method, and fuzzy method and choosed the suitable one for compensating our actuator. Noise and modeling error were measured in the filter design and control structure design to improve the compensation accuracy by inverse Preisach model. In addition, fuzzy and self-tuning controller were designed for the SMA system to improve positioning performance. At last through integrating the actuator, controller and micro-gripper, the micro-gripper system performed precise positioning and gripping micro objects.

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