Distributed Robust Event‐Triggered Optimization for Multi‐Agent Systems With Time‐Varying Objective Functions

This paper investigates a distributed robust time‐varying optimization problem for first‐order multi‐agent systems with an event‐triggered mechanism. Different from the existing results of time‐varying optimization algorithms with continuous‐time communication, an event‐triggered time‐varying optimization algorithm is proposed, which reduces communication costs and avoids Zeno behavior. To compensate for bounded disturbances and enhance system robustness, an integral sliding mode control strategy is proposed. It can be proved that bounded disturbances are compensated in a fixed time and each agent's output asymptotically tracks the optimal trajectory. A simulation example validates the effectiveness of the proposed control strategy.