Biomimetic Force and Impedance Adaptation Based on Broad Learning System in Stable and Unstable Tasks: Creating an Incremental and Explainable Neural Network With Functional Linkage

Abstract

This article presents a novel biomimetic force and impedance adaption framework based on the 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 a BLS, to realize a varying learning rate for feedforward force and impedance factors. The connections of NN layers and settings of the feature nodes are related to the human motor control and learning principle that is described as a relationship among feedforward force, impedance, reflex and position errors, and so on to make the NN explainable. Some comparative simulations are created and tested in five force fields to verify 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 conducted to verify effectiveness of the proposed method.