Event-triggered predefined-time distributed optimization algorithm based on zero-gradient-sum

Abstract

This paper presents an event-triggered distributed optimization (DO) algorithm for consensus constraints and strongly convex local cost functions. The algorithm is derived by means of the zero-gradient-sum mechanism and sliding mode control, which is free from any initial conditions or local minimization. In order to avoid continuous communication and measurement, our algorithm enables the interaction between agents solely at event-triggered instants, resulting in conserving communication resources. Importantly, the proposed method drives system to the optimal consensus state within a predefined time. Simulation results demonstrate the effectiveness of our approach.