Neural Network-based Adaptive Scaled Consensus Control of Uncertain Fractional-order Multi-agent Systems

This paper investigates the more general coordination behavior "scaled consensus" of multi-agent systems, which includes the traditional average consensus, bipartite consensus, and group consensus as the special cases. Theoretically, an adaptive scaled consensus controller is designed for uncertain fractional-order multi-agent systems. By adopting the neural network-based method, the uncertainties of each agent are approximated and compensated signals are generated for the robust scaled consensus controller design. Then, the fractional-order adaptive update laws of neural network parameters are constructed by using the Lyapunov stability theory. To this end, the robustness of the multi-agent system is enhanced by adopting the neural networks technique. Finally, some numerical simulations are performed to verify the effectiveness of the proposed control algorithm.