Adaptive Neural Impedance Control of a Robotic Manipulator With Input Saturation

TLDR

The paper develops adaptive impedance control for an n‑link robotic manipulator with input saturation using neural networks. The authors design an adaptive neural impedance controller that uses a radial basis function neural network to approximate uncertainties, an auxiliary system to handle input saturation, Lyapunov-based stability analysis, and state/output feedback, validated through extensive simulations.

Abstract

In this paper, adaptive impedance control is developed for an n-link robotic manipulator with input saturation by employing neural networks. Both uncertainties and input saturation are considered in the tracking control design. In order to approximate the system uncertainties, we introduce a radial basis function neural network controller, and the input saturation is handled by designing an auxiliary system. By using Lyapunov's method, we design adaptive neural impedance controllers. Both state and output feedbacks are constructed. To verify the proposed control, extensive simulations are conducted.