Distributed average tracking control for a class of unknown heterogeneous nonlinear second-order multiagent systems with external disturbances

In this paper, the distributed average tracking control problem is addressed for a class of unknown heterogeneous nonlinear second-order multiagent systems subjected to the external disturbances. Under local interaction constraints where each agent accesses only its neighborhood information, a distributed estimator with a smooth operator is proposed for each agent to estimate the average value of all reference signals, ensuring stable tracking while eliminating chattering phenomena. Additionally, due to the system uncertainties and external disturbances, it is challenging to acquire an accurate system model and design corresponding control algorithm, hindering the closed-loop stability analysis. To address this challenge, an approximation module assisted by a radial basis function neural network and a disturbance observer are integrated into an adaptive control algorithm, ensuring that each agent can track the averaged reference signals. Moreover, the stability of the proposed control algorithm is rigorously proven through Lyapunov-based theoretical analysis. Finally, a numerical example is presented to validate the feasibility of the proposed algorithm.