Preliminary Evaluation of Composite Learning Tracking Control on 7-DoF Collaborative Robots ?

摘要

With the increase of computational power, adaptive control becomes more and more attractive for industrial robots due to the ability to handle parametric uncertainty and the potential to achieve higher tracking accuracy. This paper implements a recently developed adaptive control scheme termed composite learning robot control (CLRC) for tracking control of a 7-degree-of-freedom industrial collaborative robot named Franka Emika Panda. The desired trajectory is specifically designed to evaluate the performance of the CLRC compared with the standard adaptive robot control and composite adaptive robot control via both simulations and experiments. Comparative results demonstrate that the CLRC meets the requirement of real-time computation and outperforms the other two control schemes with respect to tracking accuracy and parameter convergence.