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多飞行器的分布式优化研究现状与展望

发布日期: 2021-06-10 | 浏览次数: 323
多飞行器的分布式优化研究现状与展望

姜霞1,2, 曾宪琳1, 孙健1,2, 陈杰1,3                          

1. 北京理工大学 自动化学院, 北京 100081;
2. 北京理工大学重庆创新中心, 重庆 401120;
3. 同济大学 电子与信息工程学院, 上海 200082


Research status and prospect of distributed optimization for multiple aircraft

JIANG Xia1,2, ZENG Xianlin1, SUN Jian1,2, CHEN Jie1,3        

1. School of Automation, Beijing Institute of Technology, Beijing 100081, China;
2. Beijing Institute of Technology Chongqing Innovation Center, Chongqing 401120, China;
3. School of Electronic and Information Engineering, Tongji University, Shanghai 200082, China

 

摘要: 航空领域的多个飞行器协同搜救、区域监控、编队飞行等实际任务具有个体数量多、信息分散、任务指标复杂等特点,分布式优化是实现上述任务中多飞行器有效协同的重要保证,具有重要的理论意义和显著的应用价值。从优化任务的问题模型、研究框架和典型优化算法3个方面对分布式优化的研究现状进行了概述。根据不同的优化问题,从无约束的分布式凸优化、集合约束的分布式凸优化、不等式约束的分布式凸优化和分布式非凸优化这4个方面对分布式优化领域典型的研究成果进行了概述,并讨论了分布式优化研究的共性难点问题,对未来的分布式优化方向进行了展望。

关键词: 多飞行器, 分布式优化, 控制决策, 协同合作, 通信与计算平衡

Abstract: In the aviation field, practical tasks such as coordinated search and rescue, area monitoring, and formation control of multiple aircraft are conducted by many individuals with distributed information and complex task objectives. Distributed optimization is an important guarantee for the effective coordination of multiple aircraft in the above tasks, having significant theoretical and practical value. A brief introduction to classical distributed optimization tasks in the field of aviation is presented, and a research overview of distributed optimization work is conducted from three perspectives: problem models, research frameworks and classical algorithms of optimization problems. According to the optimization problems, the classical research works in the field of distributed optimization are summarized from four aspects: unconstrained distributed optimization, distributed optimization with set constraints, distributed optimization with inequality constraints, and distributed non-convex optimization. In addition, common difficulties in distributed optimization study and the future research directions of distributed optimization work are also discussed.

Key words: multiple aircraft, distributed optimization, control and decision, collaboration, trade-off between communication and computation


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