Distributed Optimization for MASs Subject to Disturbances: An Event-Triggered Derivative Feedback Approach With Minimum Inter-Event Time

The distributed optimization for multi-agent systems (MASs) is investigated in this letter. In the system, the unknown time-varying disturbances affecting each agent are taken into account. A distributed event-triggered controller is designed for each agent to solve the optimization problem, meeting several demands, including eliminating the effects of disturbances, discrete communication and controller updates, avoiding using agents’ continuous-time actual states, and ensuring the existence of a positive minimum inter-event time (MIET) between consecutive events. Existing approaches in the literature only satisfy one or two of these demands. The proposed event-triggered controller not only conserves communication and computational resources but also reduces the frequency of controller updates for agents. With mild assumptions on local cost functions, the communication network among agents and disturbances, sufficient conditions for the convergence of agents’ states are derived. It is also proven that a positive MIET exists. Finally, simulation examples are provided to validate the performance of the proposed controller.