LAUSR

This paper addresses the learning control problem for a group of robot manipulators with homogeneous nonlinear uncertain dynamics, where all the robots have an identical system structure but the reference signals to be tracked differ. The control objective is twofold: to track on reference trajectories and to learn/identify uncertain dynamics. For this purpose, deterministic learning theory is combined with consensus theory to find a common neural network (NN) approximation of the nonlinear uncertain dynamics for a multi-robot system. Specifically, we first present a control scheme called cooperative deterministic learning using adaptive NNs to enable the robotic agents to track their respective reference trajectories on one hand and to exchange their estimated NN weights online through networked communication on the other. As a result, a consensus about one common NN approximation for the nonlinear uncertain dynamics is achieved for all the agents. Thus, the trained distributed NNs have a better generalization capability than those obtained by existing techniques. By virtue of the convergence of partial NN weights to their ideal values under the proposed scheme, the cooperatively learned knowledge can be stored/represented by NNs with constant/converged weights, so that it can be used to improve the tracking control performance without re-adaptation. Numerical simulations of a team of two-degree-of-freedom robot manipulators were conducted to demonstrate the effectiveness of the proposed approach.