Robust neural force control scheme under uncertainties in robot dynamics and unknown environment

Abstract

The original impedance function is known to lack robustness due to unknown robot dynamic model and the environment. In order to improve that result, a new impedance function is derived which specifies a desired force directly. This results in a new robust robot force tracking impedance control scheme, which employs a neural network as a compensator to cancel out all uncertainties. The proposed neural force control scheme is capable of making the robot track a specified desired force as well as of compensating for uncertainties in environment location and stiffness, and in robot dynamics. Separate training signals for free-space motion and contact-space motion control are developed to train the neural compensator online. The design of the training signals is justified. Simulation studies with a three-link rotary robot manipulator are carried out and the results show excellent force tracking performance.