Unified Neural Adaptive Control for Multiple Human–Robot–Environment Interactions

Abstract

To go from human demonstration to robot independent operation, there are generally three phases of interaction to undergo, including human-robot interaction (HRI), human-robot-environment interaction (HREI), and robot-environment interaction (REI). Most existing methods address problems of a single stage. In this article, a unified neural adaptive control method that organically fuses multiple interactions is proposed. HRI, REI, and their coupling effects in HREI are comprehensively considered. First, the iterative least squares method is used for robot dynamics identification based on the linearized momentum observer. The accuracy of external force observation is improved to deal with dynamic uncertainties. The human force and the environmental force are achieved and decoupled by using only a force sensor, a momentum observer, and a selection matrix S. Next, the neural adaptive control method compensating position errors caused by the model uncertainty is addressed. The control system is proved to be stable based on the Lyapunov theorem. The trajectory tracking error under the model uncertainties is reduced. Then, the adaptive admittance control method is introduced. The interaction force of HRI is minimized and the interaction force control of REI is realized. Finally, the proposed method is verified by simulations and experiments.