Model-Free Adaptive Containment Control for Unknown Multi-Input Multi-Output Nonlinear MASs With Output Saturation

In this work, a model-free adaptive containment control scheme is investigated for a class of multi-input multi-output nonlinear multiagent systems, where the agents’ dynamics are unknown with output saturation. Firstly, the dynamics of followers are transformed into a equivalently data model by using full form dynamic linearization technology. Secondly, the distributed containment control algorithm is proposed only using the input data and the saturated output data of followers and neighbors. Further, the boundedness of the containment error is proved by using the contraction mapping principle and mathematical induction method. It is shown that the developed scheme can ensure that the followers move into the convex hull composed of the leaders. Last, the effectiveness of the developed scheme can be verified through numerical simulations.