Distributed Optimization of Multiagent Systems Against Unmatched Disturbances: A Hierarchical Integral Control Framework

This article investigates a distributed optimization problem of double-integrator multiagent systems with unmatched constant disturbances. Instead of involving an internal model or a disturbance observer to deal with the disturbances as in existing works, a two-layer control framework is presented based on state-integral feedback control (SIFC) and adaptive control techniques. The upper layer uses a virtual system to generate a global optimal consensus trajectory which is shared by the agents via a communication network. The lower layer includes an SIFC controller to guarantee asymptotic tracking of the given trajectory. Also in this layer, a model reference adaptive controller is introduced to enhance the dynamic tracking performance of the SIFC controller. This framework enables distributed optimization with time-triggered communication and mild requirements on the team objective. The method yields an interesting co-design algorithm of the control parameters and the communication intervals, which is proved to be convergent using Lyapunov stability theory. The effectiveness and advantages of the method are illustrated by numerical simulations.

Automated summary

This article investigates a distributed optimization problem of double-integrator multiagent systems with unmatched constant disturbances, proposing a two-layer control framework based on SIFC and adaptive control techniques. The framework enables distributed optimization with time-triggered communication and mild requirements on the team objective, and the co-design algorithm of control parameters and communication intervals is proven to be convergent using Lyapunov stability theory. The method's effectiveness and advantages are illustrated through numerical simulations.