分布式优化是多智能体系统协调控制领域的研究热点。多智能体系统往往受外部干扰和内部参数或结构不确定等干扰因素的影响，因此，研究受扰情况下的分布式优化控制问题十分重要。然而，该方面已有结果局限于受扰一阶和匹配受扰二阶多智能体系统，且闭环系统局限于渐近稳定。为突破上述瓶颈，并提升闭环系统抗干扰性能、鲁棒性和收敛性能，本项目将对多类受扰多智能体系统的分布式优化控制问题展开研究，并提出主动抗干扰分布式优化控制解决方案，包括：不匹配受扰二阶多智能体系统的主动抗干扰分布式优化控制；受扰高阶多智能体系统的主动抗干扰分布式优化控制；受扰二阶和高阶多智能体系统的有限时间主动抗干扰分布式优化控制；上述方法在多移动机器人优化聚集、编队控制和多无人直升机优化编队、姿态协调控制中的应用设计与验证。本项目研究将进一步丰富分布式优化控制理论和方法体系，促进主动抗干扰分布式优化控制方法的工程应用，有重要理论和应用价值。

Distributed optimization is a hot topic in the field of distributed cooperative control for multi-agent systems. Multi-agent systems generally suffer from external disturbances and internal disturbances (e.g., model parametric or structural uncertainties), so research on distributed optimization control problems with the presence of disturbances is very important. However, results on this topic are limited to disturbed first-order systems and second-order systems subject to matched disturbances, and the stability of the closed-loop systems is limited to asymptotic stability. To improve the aforementioned bottlenecks and improve the disturbance rejection, robustness and convergence properties of the closed-loop systems, this project will systematically investigate the distributed optimization control problems for several kinds of multi-agent systems with disturbances, and propose active anti-disturbance distributed optimization control schemes, including: active anti-disturbance distributed optimization control of second-order multi-agent systems with mismatched disturbances; active anti-disturbance distributed optimization control of higher-order multi-agent systems with disturbances; finite-time active anti-disturbance distributed optimization control of second-order and higher-order multi-agent systems with disturbances; applications of the above designed control methods to optimized rendezvous and formation control of multi-mobile robot systems, and optimized formation flight and attitude coordination control of multi-unmanned helicopter systems, and simulational and experimental demonstrations on these systems. This research will enrich the theory and method framework of distributed optimization control, and promote engineering applications of the proposed distributed optimization control methods, and it has great theoretical and practical application values.