Leader-Follower Synchronization for Networked Euler-Lagrange Systems with Input Constraint: A Learning Approach

In this paper, iterative learning control is designed to achieve the leader-follower synchronization objective for the networked Euler-Lagrange systems with input saturation. In order to compensate for the input constraint, we propose an iterative learning control law. Based on the defined composite energy function, all the signals in the closed-loop system are proven to be bounded in each iteration. Along the iteration axis, global full-state synchronization is realized in the sense that all the agents can track the desired trajectory under a connected directed graph. Simulation results are presented to verify the effectiveness of the proposed control schemes.