Predictive Suboptimal Consensus of Multiagent Systems With Nonlinear Dynamics

In this paper, a unified framework is proposed for designing distributed control laws to achieve the consensus of linear and nonlinear multiagent systems. The consensus problem is formulated as a receding-horizon dynamic optimization problem with an integral-type performance index subject to the dynamics of the considered multiagent system. Different from conventional optimal control that solves Hamilton-Jacobian-Bellman equation numerically in high dimensions, we present a suboptimal solution with analytical expressions by utilizing Taylor expansion for prediction along time and give the corresponding distributed control law in an explicit form. Theoretical analysis shows that the proposed control laws can guarantee exponential and asymptotical stability of the multiagent systems. It is also proved that the proposed suboptimal control laws tend to be optimal with time. Illustrative examples are also presented to validate the efficacy of the proposed distributed control laws and the theoretical results.