基于模型预测的事件触发多智能体系统设计

Based on model predictive event-triggered multi-agent systems design

多智能体系统在很多领域内都得到了广泛关注,但在分布式模型预测控制方面依然较为薄弱。针对系统状态未知的多智能体系统,引入动态Luenberger观测器估计系统状态。然后,根据离散李亚普诺夫稳定性,采用反步法设计一种新的事件触发策略,以获取通信带宽利用率和控制性能之间的权衡。最后,通过仿真验证算法的有效性。

Multi-agent systems(MAS)have received much attention within many fields,but are still weak in distributed model predictive control(DMPC).For MAS with unknown system states,a dynamic Luenberger observer is introduced to estimate the system states.Then,based on the discrete Lyapunov stability,a new event-triggering strategy is designed using backstepping to obtain a trade-off between communication bandwidth utilization and control performance.Finally,the effectiveness of the algorithm is verified by simulation.