Distributed Data-Driven Iterative Learning Point-to-point Consensus Tracking Control for Unknown Nonlinear Multi-Agent Systems

This paper studies the point-to-point consensus tracking problem for a class of nonlinear non-affine multi-agent systems, where the tracking target is a series of some given ideal output points rather than a complete ideal trajectory. For the unknown nonlinear dynamics of every agent, the relationship between its control input and the corresponding output signal at these given points is acquired, then a data-driven model is constructed based on adjacent-agent dynamic linearization technology. Furthermore, by optimizing two performance index functions, we design a novel point-to-point iterative learning tracking control scheme, which not only ensures the consensus tracking results of all agents only utilizing the I/O data of every agent at the given ideal output points, but also makes all agents reach consensus behavior at the rest of the time instants. The effectiveness of our algorithm is confirmed through rigorous theoretical analysis and two experimental simulations.