对于具有状态约束的高阶非线性不确定的多代理系统的双方封闭控制的分布式凸优化
Yuhang Yao1, Jiaxin Yuan1, Tao Chen2
1School of Air Transportation, Shanghai University of Engineering Science, Shanghai 201620, China.
Mathematical biosciences and engineering : MBE
|November 3, 2023
概括
本研究介绍了一种新的基于惩罚的分布式优化算法,用于控制复杂的多代理系统. 该方法确保代理保持在状态约束范围内,同时实现最佳性能,增强系统稳定性和可靠性.
科学领域:
- 控制工程 控制工程 控制工程
- 优化理论 优化理论
- 人工智能的人工智能
背景情况:
- 高级非线性不确定的多代理系统带来了重大控制挑战.
- 在状态约束下实现分布式优化对于实际应用至关重要.
研究的目的:
- 开发一种基于惩罚的分布式优化算法,用于双边封闭控制.
- 在不确定的多代理系统中解决状态约束和不可测量的状态.
主要方法:
- 设计了一个将全球目标和共识约束结合在一起的惩罚功能.
- 观察者被用来估计无法测量的状态.
- 辐射基函数神经网络 (RBFNN) 接近未知的非线性.
- 采用了具有屏障莱普诺夫功能的自适应后退控制 (BLF).
主要成果:
- 拟议的控制战略有效地管理了国家制约.
- 所有代理的输出轨迹都在异常地汇聚到全球最佳信号.
- 该系统在不确定性下表现出强的性能.
结论:
- 基于处罚的分布式优化算法成功实现了双方封闭控制.
- 整合RBFNN,DSC和BLF为复杂系统提供了有效的解决方案.
- 该方法确保在定义的状态边界内稳定和最佳运行.
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