Since batch algorithms suffer from lack of proficiency in confronting model mismatches and disturbances, this contribution proposes an adaptive scheme based on continuous Lyapunov function for online robot dynamic identification. This paper suggests stable updating rules to drive neural networks inspiring from model reference adaptive paradigm. Network structure consists of three parallel self-driving neural networks which aim to estimate robot dynamic terms individually. Lyapunov candidate is selected to construct energy surface for a convex optimization framework. Learning rules are driven directly from Lyapunov functions to make the derivative negative. Finally, experimental results on 3-DOF Phantom Omni Haptic device demonstrate efficiency of the proposed method.

翻译：由于批量算法缺乏应对模型不匹配和扰动的能力,这一贡献提出了一个基于连续的 Lyapunov 功能的适应性计划,用于在线机器人动态识别。本文建议稳定更新规则,以驱动从模型参考适应范式中得到启发的神经网络。网络结构由三个平行的自我驱动神经网络组成,目的是单独估计机器人动态术语。选择Lyapunov 候选者为康韦克斯优化框架建造能源表面。学习规则直接由 Lyapunov 函数驱动,使衍生物为负。最后,3-DOF Phiantom Omni Haptic 装置的实验结果显示了拟议方法的效率。