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.
