Adaptive impedance control of manipulators in human-robot interaction

This study focuses on investigating an adaptive impedance control strategy for facilitating human-robot collaborative tasks. Traditional impedance control methods may lead to significant position tracking and force tracking errors when accurate environmental information is not available. A force feedback-based impedance control algorithm is proposed to enhance control accuracy. The algorithm utilizes a neural network adaptive control approach to adjust the parameters of the impedance control algorithm, as well as estimate and compensate for unknown environmental information. The proposed control approach utilizes radial basis function neural networks (RBFNN) to enhance tracking performance by compensating for dynamic uncertainties. The proposed approach was tested under various conditions to ensure its performance across different situations.