前言

如果没有一个合适的框架，学生、工程师或研究人员很难评估参数识别方法对于给定场景的相关性。

在这里，我们提出了一个专用于机器人识别的统一基准。到目前为止实现了以下算法：

• Inverse Dynamic Identification Model with Ordinary Least Square (IDIM-OLS)
• Inverse Dynamic Identification Model with Weighted Least Square (IDIM-WLS)
• Inverse Dynamic Identification Model with Iteratively Reweighted Least Square (IDIM-IRLS)
• Inverse Dynamic Identification Model with Total Least Square (IDIM-TLS)
• Inverse Dynamic Identification Model with Maximum Likelihood (IDIM-ML)
• Inverse Dynamic Identification Model with Instrumental Variables (IDIM-IV)
• Direct and Inverse Dynamic Identification Model (DIDIM)
• Closed-Loop Output-Error (CLOE)
• Closed-Loop Input-Error (CLIE)
• Direct Dynamic Identification Model with Extended Kalman Filter (DDIM-EKF)
• Direct Dynamic Identification Model with Square-Root Extended Kalman Filter (DDIM-SREKF)
• Direct Dynamic Identification Model with Unscented Kalman Filter (DDIM-UKF)
• Direct Dynamic Identification Model with Square-Root Unscented Kalman Filter (DDIM-SRUKF)
• Direct Dynamic Identification Model with Central Difference Kalman Filter (DDIM-CDKF)
• Direct Dynamic Identification Model with Square-Root Central Difference Kalman Filter (DDIM-SRCDKF)
• Adaline Neural Network (AdaNN)
• Hopfie