In this paper we present the results obtained from the implementation of a robust position/force controller on a two-degree-of-freedom direct drive robot. The controller is based on the theoretical work presented in references 1 & 2, which guarantees Globally Uniformly Ultimately Bounded (GUUB) position tracking error and bounded force tracking error. The controller accomplishes this stability result in spite of robot model uncertainty and only requires; joint position and velocity measurements, end-effector force measurements, and bounds on the model parameters. Experimental results described in this paper serve to verify the theoretical claims.

This paper describes the experimental evaluation of three identification schemes to determine the dynamic parameters of a two degrees of freedom direct-drive robot. These schemes involve a recursive estimator while the regression models are formulated in continuous time. The fact that the total energy of robot manipulators can be represented as a linear relation in the inertial parameters, has motivated the suggestion in the literature of several regression models which are linear in a common dynamic parameter vector. Among them, in this paper we consider the schemes based on the filtered dynamic regression model, the supplied energy regression model and a new one proposed in this paper: the filtered power regression model. The underling recursive parameter estimator used in the experimental evaluation is the standard least-squares.