本文以「光機電系統控制研究室」研究多年的形狀記憶合金制動器系統為基礎，針對形狀記憶合金致動器量測FOD曲線建立遲滯模型，設計一驅動電路以放大電流，提供形狀記憶合金有較大的致動量。接著利用類神經網路當作位移估測器，而閉迴路控制將運用Inverse Preisach補償、PI control與位移估測器，進行電流回授控制，並藉由階梯狀的訊號、弦波訊號與變化類型的弦波，加以測試控制性能。

關鍵字：形狀記憶合金、類神經網路、電流回授控制

This thesis is based on the research of shape-memory-alloy actuator system developed by“OME System Lab”in recent years. For modeling a shape-memory-alloy actuator, the present approach is to measure FOD curve for establishing hysteresis model. A circuit is designed and implemented to drive shape memory alloy for higher displacement output. A neural network scheme is implemented as a displacement estimator. A closed-loop control with Inverse Preisach compensator, PI control, and displacement estimator under current feedback is implemented. The control performance of staircase, sinusoidal, and varying sinusoidal responses are tested.

Keyword: shape memory alloy, neural network and current feedback control

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