Haptic Identification by ELM-Controlled Uncertain Manipulator
提出一种基于极限学习机的控制方案，让不确定的机器人机械臂通过多次尝试调整参考点和前馈力，从而识别未知物体的几何形状和刚度。
This paper presents an extreme learning machine (ELM)-based control scheme for uncertain robot manipulators to perform haptic identification. ELM is used to compensate for the unknown nonlinearity in the manipulator dynamics. The ELM enhanced controller ensures that the closed-loop controlled manipulator follows a specified reference model, in which the reference point as well as the feedforward force is adjusted after each trial for haptic identification of geometry and stiffness of an unknown object. A neural learning law is designed to ensure finite-time convergence of the neural weight learning, such that exact matching with the reference model can be achieved after the initial iteration. The usefulness of the proposed method is tested and demonstrated by extensive simulation studies.