Event-triggered distributed optimization for multi-agent systems with quantization and disturbance rejection

In this paper, adaptive continuous-time algorithms with event-triggered mechanism are studied to solve the optimization problem. First, an event-triggered adaptive algorithm is introduced, and it is proven that this algorithm can effectively solve the optimization problem. Second, to solve the optimization problem, two event-triggered algorithms are proposed, one considering uniform quantization information and the other addressing external disturbances. It is demonstrated that the states of the multi-agent systems practically converge to the global optimal point. Three numerical cases demonstrate the effectiveness of the relevant results.