Biomimetic Force and Impedance Adaptation Based on Broad Learning System in Stable and Unstable Tasks

This article presents a novel biomimetic force and impedance adaption framework based on Broad Learning System (BLS) for robot control in stable and unstable environments. Different from iterative learning control, the adaptation process is realized by a neural network (NN)-based framework, similar to BLS, to realize a varying learning rate for the feedforward force and impedance factors. The connections of NN layers and the settings of the feature nodes are related to human motor control and learning principle that is described as a relationship between feedforward force, impedance, reflex and position errors, etc., to make the NN explainable. Some comparative simulations are created and tested in five force fields to verify the advantages of the proposed framework in terms of force and trajectory tracking efficiency and accuracy, robust responses to different force situations and continuity of force application in a mixed stable and unstable environment. Finally, an experiment is taken to verify the effectiveness of the proposed method.