基于神经网络的动态目标封闭控制不确定非线性多代理系统在签名网络上的控制.
Weihao Li1, Jiangfeng Yue1, Mengji Shi1
1School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu, 611731, Sichuan, China; Aircraft Swarm Intelligent Sensing and Cooperative Control Key Laboratory of Sichuan Province, Chengdu, 611731, Sichuan, China.
概括
本研究介绍了一种神经网络方法,用于在多代理系统中强大的目标封闭控制. 该方法提高了预测准确度和控制稳定性,即使在不确定的目标动态和代理干扰的情况下.
科学领域:
- 机器人和控制系统 机器人和控制系统
- 人工智能的人工智能
- 网络化系统 网络化系统
背景情况:
- 多代理系统需要对目标封闭等任务进行强有力的控制.
- 估计不确定性动态对于准确的预测和控制至关重要.
- 传统方法可能需要昂贵的传感器来获得高级目标信息.
研究的目的:
- 开发一种基于神经网络的方法,用于不确定的目标封闭控制.
- 在签名网络上的多代理系统中增强控制稳定性.
- 减少对高成本传感器的依赖,以估计目标状态.
主要方法:
- 使用双边共识错误构建一个名义目标封闭控制器.
- 使用神经网络近似来估计不确定的目标动态和代理干扰.
- 根据估计的不确定性生成前控制组件.
主要成果:
- 尽管动态和干扰不确定,但实现了准确的目标封闭控制.
- 证明了对匹配和不匹配的干扰的改进强度.
- 消除了需要高成本传感器来获得目标速度和加速的需求.
结论:
- 提出的基于神经网络的控制器有效地解决了不确定的目标封闭控制.
- 该方法提高了多代理系统的稳定性和准确性.
- 它通过避免昂贵的传感器要求,提供了具有成本效益的解决方案.
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