Memory-augmented adaptive flocking control for multi-agent systems subject to uncertain external disturbances

Abstract

This paper presents a novel flocking algorithm based on a memory-enhanced disturbance observer. To compensate for external disturbances, a filtered regressor for the double integrator model subject to external disturbances is designed to extract the disturbance information. With the filtered regressor method, the algorithm has the advantage of eliminating the need for acceleration information, thus reducing the sensor requirements in applications. Using the information obtained from the filtered regressor, a batch of stored data is used to design an adaptive disturbance observer, ensuring that the estimated values of the parameters of the disturbance system equation and the initial value converge to their actual values. The result is that the flocking algorithm can compensate for external disturbances and drive agents to achieve the desired collective behavior, including virtual leader tracking, inter-distance keeping, and collision avoidance. Numerical simulations verify the effectiveness of the algorithm proposed in the present study.