Robot compliance control algorithm based on neural network classification and learning of robot-environment dynamic models

In this paper, a new learning control algorithm based on neural network classification of unknown dynamic environment models and neural network learning of robot dynamic model is proposed. The method classifies characteristics of environments by using multilayer perceptrons, and then determines the control parameters for compliance control using the estimated characteristics. Simultaneously, using the second neural network the compensation of robot dynamic model uncertainties is accomplished. The classification capability of neural classifier is realized by efficient online training process. It is an important feature that the process of pattern classification can work in an online manner as a part of selected compliance control algorithm. Compliant motion simulation experiments have been performed in order to verify the proposed approach.