An Online Admittance Control for Asymmetric Teleoperated Arm Robot Interacting with Unknown Environment

In many telerobotic tasks, robot manipulators interact with unknown environments. Moreover, the manipulators are high-coupled Asymmetric nonlinear multi-degrees of freedom (DOF) systems that present uncertainties and errors in modeling, which may affect safety and performance responses. In this paper, a novel adaptive control scheme is designed to achieve a trajectory and force tracking performance based on a three-channel teleoperation control framework. The adopted approach is based on an admittance control law combined with an inverse dynamics control strategy that avoids the use of force control loop and permits to deal with nonlinear terms. To cope with robots' uncertainties, neural network compensators (NNC) are implemented on both sides. Whereas the integration of weighted recursive least squares (WRLS) estimation method permits the identification of dynamic impedance of an unknown environment. Human in the loop experiment using a real Omni phantom, remote virtual PUMA560 and an environment show the effectiveness of the proposed approach.