A Neuro-Adaptive Controller for the Force/Position Tracking of a Robot Manipulator under Model Uncertainties in Compliance and Friction

In this work, we consider the force and position trajectory tracking for a robot manipulator in compliant contact with a surface in the presence of unknown stiffness and dynamic friction. A novel neuro-adaptive controller is proposed that exploits the approximation capabilities of the linear in the weights neural networks and the uniform ultimate boundedness of force and position error is proved. Simulation results illustrate the performance of the proposed controller