Adaptive Bilateral Control using Operator Elbow Impedance

Human arm dynamics can be used for control of human-machine interfaces in haptic applications. In this paper, a novel method for online estimation of human operator elbow impedance using a second-order quasi-linear model is presented. The proposed method uses moving window least squares method to locally identify dynamic parameters for a limited number of operating points. These points are used to train a radial basis function artificial neural network to provide online estimate of the arm dynamic parameters for other operating points in the variable space. The network online impedance estimates are used in an adaptive bilateral controller with artificial communication delay. Experimental results on a one degree-of-freedom haptic simulation system are provided