Regional Leader-Following Consensus of Generalized One-Sided Lipschitz Multiagents: A Monte Carlo Simulation-Based Strategy

Abstract

This article addresses the leader-following exponential consensus control problem for generalized one-sided Lipschitz (OSL) multiagents (MAs) using relative-state information under a directed spanning tree. Previous works have accomplished the consensus protocol design for the globally OSL nonlinear MA systems. Unlike the existing results, the proposed strategy employs a less conservative approach in the form of matrix inequalities and convex routines by assuming that the nonlinear MAs satisfy the OSL condition in a local region by relaxing the so-called quadratic inner-boundedness constraint. A novel algorithm is provided to incorporate the value of generalized OSL constant and to consider the regional consensus protocol computation via Monte Carlo simulations, based on uniform random numbers. The proposed approach is fully automatic, easily solvable, and presented for the consensus control of generalized (locally) OSL nonlinear systems for the first time. The proposed approach also studies the criteria for the exponential consensus convergence rate for attaining a faster convergence of the consensus error. Furthermore, the developed consensus protocol for the generalized OSL systems is then extended for the case of switching topologies. The proposed consensus protocol of locally OSL nonlinear MA systems can also be employed to the global OSL nonlinear MA systems as a special case for attaining the local performance. The effectiveness of the proposed approach is verified by the leader-following consensus of eight moving agents and a practical application of F-18 aircraft.