预定义时间的自适应神经网络,用于大规模互连系统的去中心化控制,具有输入歇斯底里
Xiaoli Li1, Guoju Zhang2, Yingshan Zhou2
1School of Information Science and Technology, Beijing University of Technology, Beijing 100124, China; Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing 100124, China.
ISA transactions
|January 28, 2025
概括
这项研究引入了一种新的适应神经网络控制器,用于具有歇斯底里的非线性系统,确保在设定的时间内快速和准确的跟踪. 该方法克服了复杂性,并提高了相互连接系统的控制精度.
科学领域:
- 控制理论 控制理论
- 人工智能的人工智能
- 非线性系统是非线性系统.
背景情况:
- 大规模互连的非线性系统带来了重大的控制挑战.
- 输入歇斯底里和系统不确定性降低了控制性能.
- 现有的后退方法可能会遭受复杂性爆炸和喋喋不休.
研究的目的:
- 开发一个预定义时间的自适应神经网络去中心化控制器.
- 解决大规模互连的非线性系统与输入歇斯底里.
- 为了保证在特定的结算时间内追踪错误的趋同.
主要方法:
- 使用后退技术与修改的命令过器相结合.
- 采用在线神经网络近似器用于系统不确定性.
- 引入了一种新的预定义时间错误补偿机制.
主要成果:
- 追踪错误在预定义的结算时间内汇聚到一个小的边界集.
- 控制参数调整了收时间.
- 有效地缓解了复杂性爆炸和聊天现象.
- 神经网络补偿了系统的不确定性.
结论:
- 拟议的控制器对于具有歇斯底里的非线性系统是可行的和有效的.
- 实现了精确的控制,保证了收时间.
- 为复杂的相互连接系统提供强大的解决方案.
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