Point-to-point consensus tracking control for unknown nonlinear multi-agent systems using data-driven iterative learning

Abstract

This paper considers the point-to-point consensus tracking control for a class of nonlinear multi-agent systems with completely unknown dynamics, where the consensus is concerned with some given desired points instead of the entire desired trajectory. It is assumed that the multi-agent system executes repetitive coordination tasks in a finite time interval and the iterative learning control is also utilized to design a consensus protocol with learning ability. To deal with the unknown nonlinear agent’s dynamic, the relationship between agent’s output at these given points and agent’s control input is first derived and then a data-based model referring to the agent’s dynamic is established by utilizing the iteration-domain dynamical linearization technique. Then, a data-driven iterative learning protocol is developed by optimizing two performance indexes, which contains a control input updated algorithm, a parameter estimation algorithm and a reset algorithm. The results show that the proposed design can achieve the point-to-point consensus tracking task only by using the I/O data of the agent. Finally, simulation examples are provided to verify the effectiveness of the proposed protocol.