Adaptive Robust Interaction Force Control of a Robotic Manipulator in Uncertain Environments

This article presents a novel adaptive robust interaction force control approach that aims to achieve accurate and robust force modulation of a robot manipulator in uncertain environments. First, a novel interaction model between manipulator and environment is developed that captures both structured and unstructured uncertainties. According to the developed interaction model, a two-loop control scheme is developed, which consists of an adaptive robust interaction controller (ARIC) in the outer loop and a motion tracking controller in the inner loop. With the proposed ARIC, the unknown structured parameters of the developed interaction model are estimated online. These estimated parameters are then utilized to generate feedforward compensation actions in the mechanism of ARIC, while unstructured uncertainties are addressed through robust feedback control so that excellent interaction force tracking accuracy can be ensured. In addition, the theoretical stability analysis of the ARIC is presented and practical experiments are carried out on a robot manipulator. The experimental results confirm the superiority of the proposed approach over these typical algorithms in terms of interaction force tracking performance in different environments.