Adaptive neural impedance control with extended state observer for human–robot interactions by output feedback through tracking differentiator

Abstract

Although impedance control has huge application potential in human–robot cooperation, its engineering application is still quite limited, owing to the high nonlinearity of the human–robot dynamics and disturbances. This article presents a novel adaptive neural network controller with extended state observer for the human–robot interaction using output feedback. The adaptive neural network with extended state observer integrates the adaptive neural network and extended state observer to combine their advantages. The proposed algorithm can address the challenges encountered in human–machine systems, for example, slow convergence of neural networks, internal and external disturbances. Output feedback is realized using tracking differentiator to avoid the costly measurements of certain states. The errors of the closed-loop system are proven to converge to a small compact set containing 0 by Lyapunov theory. Simulations and experiments were conducted to verify the effectiveness of the proposed controller. Results show that the proposed strategy offers superior convergence and better tracking performance compared with the adaptive neural network. The proposed controller can be widely applied in various human–machine interactions to enhance productivity and efficiency.