Neural Control for Human-Robot Interaction with Human Motion Intention Estimation

Abstract

In this article, a proactive control strategy is developed for robots interacting with humans by integrating the estimation of human partner’s motion intention. A human control model is used and a least square-based observer is employed to estimate the human control input without a force sensor. Using the estimate of the human intention, a neural network (NN)-enhanced robot controller is designed to make the robot actively follow the human trajectory. NNs are integrated into robot controller to approximate and compensate the system uncertainties, so that a tracking performance can be guaranteed. Rigorous analysis based on Lyapunov theory proves that all the error signals are uniformly ultimately bounded. Implementations show that the proposed control method has adaptive properties.