Adaptive average consensus of heterogeneous high-order unknown nonlinear multi-agent systems under switching topologies

This paper investigates average consensus in heterogeneous unknown nonlinear multi-agent systems with switching topologies. The goal is to coordinate heterogeneous multi agents to reach a consensus on their positions, specifically the average of their starting points, while ensuring that their velocities, accelerations, and higher derivatives approach zero. Agents achieve consensus using their individual states and an auxiliary variable that they share with their neighbors. In the proposed controller, the radial basis neural network acts as a functional approximation for the unknown dynamic. The closed-loop system’s asymptotic stability is proven using Lyapunov theory. The proposed method’s effectiveness is confirmed by simulation results.