以重力補償為基礎之上肢復健機器人

Abstract

本論文呈現一個用人體手臂動態模型去模擬受試者意志活動的控制器。為了去達成這個目的，首先我們必須先分析從力量/扭力感測器所量測到的資訊進而得到真正受試者各關節所施的扭力。由得到的真正受試者施的扭力乘上一個增益，這個控制器可以去實現一些物理治療的訓練，包括主動模式、輔助模式以及阻抗模式在我們自製的上肢復健機器手臂。另外，藉由改變手臂動態模型的重力項的比例，這個控制器更可以達到不同程度的上肢手臂重力補償。最後，透過幾個實際的實驗來驗證此控制器的效能及成果。

This thesis presents a control scheme using human arm dynamics to simulate the subject's volitional movement. To achieve this goal, first, it has to obtain the torques purely generated from the subject from force/torque sensors. With this result, the controller can implement the therapeutic exercises, consisting of the active range of motion (AROM), the active-assistive range of motion (A-AROM), and the resistive range of motion (RROM) modes on our self-built upper limb rehabilitation robot arm by feeding back the torques purely generated from the subject with different gains to the associated arm controller. Besides, by modifying the gravity term in human arm dynamics, this controller can achieve different levels of gravity compensation of the upper limb. To validate our design, some realistic experiments are conducted and the performance of our design is demonstrated.