本論文研究工業用機械手臂之動態系統鑑別方法，主要可分為系統動態方程式推導、測試軌跡產生以及系統參數鑑別方法等三個部分。首先，推導出機械手臂之動態方程式，再將鑑別參數從中分離，化簡為重心參數型式；接著，利用重心參數型式中之逆向動態模型計算條件數，以此條件數優化測試軌跡數學模型之參數，得到具有動態激發能力之測試軌跡；最後，以測試軌跡為機械手臂各軸位置命令，根據所量測的機械手臂各軸角度及角速度等資訊，利用系統參數鑑別方法估算出系統參數。傳統系統參數鑑別方法為最小平方法，但此方法需耗費較多時間、資源進行鑑別資訊前處理。直到近年來於多篇文獻中給出了多種改良式的線上系統參數鑑別方法，如直接逆向動態模型法、兩階段最小平方法與工具變數法等，此類方法除了可以提升鑑別流程速度之外，對於量測誤差及雜訊也有良好的抑制效果，進而有效提升鑑別的準確度。在實作方面，將以二軸機械手臂為實驗平台，根據參數收斂性、參數穩定性及鑑別準確性等三項性能指標，針對上述各類鑑別方法之優劣進行比較。

This thesis mainly focuses on the problem of system identification of industrial manipulators. In this master thesis, system identification of robot manipulators can be divided into three parts: derivation of system dynamic equation for an industrial manipulator, generation of exciting trajectory used in parameter identification, and system parameter identification. At first, the dynamic equation needs to be derived and represented as a linear form of system parameters which are called the barycentric parameters. Second, the dynamic characteristics of a robot manipulator will be excited by the exciting trajectory which is optimized by the condition number of the inverse dynamic model. Finally, the exciting trajectory is adopted as the position command of each axis of the robot manipulator. Based on the measured angular position and measured angular velocity for all axes, a system identification method is employed to estimate the parameter values of the robot manipulator. The most common identification method is the least square method, but it takes much time and resources to deal with data. Recently, new on-line identification methods are proposed; these methods take less time and resources to acquire identification results faster. In addition to that, the two-stage least square method and instrumental variable method can both suppress the disturbance of noise and measurement errors, and obtain consistent identification results. A 2 DOF planar robot is used as the experimental platform to compare the performance of the aforementioned identification methods. The performance indices used in the comparison include convergence of parameter values, accuracy of the estimated parameter values, etc.

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