Fixed-Time Distributed Optimization for Multi-Agent Systems with Input Delays and External Disturbances

This study concentrates on a fixed-time distributed optimization problem for multi-agent systems (MASs) with input delay and external disturbances. First, by adopting the Artstein model reduction technique, the time-delay system is first transformed into a delay-free one, and external disturbances are then effectively eliminated by using an integral sliding mode control strategy. Second, a new centralized optimization mechanism is developed that allows all agents to reach the same state in a fixed time and then converge to the optimal value of the global objective function. Meanwhile, the optimization problem is extended to switching topologies. Moreover, as the gradient information of the global objective function is difficult to obtain in advance, we construct a decentralized optimization protocol that enables all agents to acquire the same state in a certain amount of time while also optimizing the global optimization problem. Finally, two numerical simulations are presented to validate the effectiveness and reliability of the developed control strategy.

Automated summary

This study focuses on a fixed-time distributed optimization problem for multi-agent systems with input delay and external disturbances. By using the Artstein model reduction technique and integral sliding mode control strategy, the time-delay system and external disturbances are effectively addressed. A new centralized optimization mechanism is developed, and a decentralized optimization protocol is constructed to tackle the difficulty of obtaining gradient information in advance. Numerical simulations demonstrate the effectiveness and reliability of the proposed control strategy.