Adaptive Tracking Control of Cooperative Robot Manipulators With Markovian Switched Couplings

Abstract

Many cooperative robotic systems have not only modeling heterogeneity and uncertainty but also switched couplings, causing control difficulties. Here, we develop a neural network adaptive control framework for cooperative robot manipulators with unknown Euler–Lagrange dynamics and Markovian switched couplings. Second-order Markovian switching networks are used for modeling such cooperative robotic systems, which admit a hybrid neural network control with a desired tracking performance. The hybrid neural network control scheme contains a distributed adaptive controller and a hybrid adaptation law, enabling learning in the closed-loop system. The position and velocity tracking errors are shown to be practically uniformly exponentially stable in the mean-square sense, respectively, guaranteeing the second-order practical tracking. The results also suggest that the neural weight evolves with practical convergence to the ideal, showing the effect of network structure on the adaptation capacity.