<p>In a dynamic natural environment, system performance often changes with the environmental conditions. Especially in non-cooperative systems, the performance function often has an uncertain part, which makes the conventional optimization methods based on the exact performance function difficult to use. This paper will propose a real-time generated method and will produce a dynamic solution to solve the cooperative optimize problem with unknown dynamic performance function. On this basis, a multi-node formation cooperative surround control algorithm which takes into account the group performance and formation constraints is designed. The online optimization scheme presented in this paper offers an effective solution to ensure the system’s overall performance remains dynamically optimal in real-time. Simulations demonstrate the algorithm’s adaptability and discuss its potential applications in real-time formation optimization.</p>

错误:搜索内容不能为空,请输入英文关键词
错误:关键词超出字数限制,请精简
高级检索

An Online Cooperative Optimization Algorithm for Dynamic Performance Functions With Unknown Terms: A Case Study of Dynamic Formation Surround Measurement

  • Zilun Hu,
  • Chao Ni

摘要

In a dynamic natural environment, system performance often changes with the environmental conditions. Especially in non-cooperative systems, the performance function often has an uncertain part, which makes the conventional optimization methods based on the exact performance function difficult to use. This paper will propose a real-time generated method and will produce a dynamic solution to solve the cooperative optimize problem with unknown dynamic performance function. On this basis, a multi-node formation cooperative surround control algorithm which takes into account the group performance and formation constraints is designed. The online optimization scheme presented in this paper offers an effective solution to ensure the system’s overall performance remains dynamically optimal in real-time. Simulations demonstrate the algorithm’s adaptability and discuss its potential applications in real-time formation optimization.