Finite-Iteration Consensus Tracking Control of Nonlinear Multi-agent Systems with Input Sharing

In this paper, the distributed iterative learning control (DILC) based on control input sharing is constructed for nonlinear multi-agent system to achieve finite-iteration tracking. The strategy of input sharing is implemented by using the weighted average of the agents, such that each agent shares its input data to improve the learning process of the system. Then, since it is not realistic for the system to reach consensus as the iteration steps go to infinity, the system achieves finite-iteration tracking by relaxing the control objective. The proposed algorithm is analyzed thoroughly with the help of the contraction mapping method. At last, simulation study is shown to demonstrate the effectiveness of the designed algorithm.