对于多代理系统的神经适应共识学习:一种渐进式方法,用于非严格的纯反控制
Shuting Wang1, Jinsha Li1, Junmin Li1
1School of Mathematics and Statistics, Xidian University, Xi'an, 710126, China.
ISA transactions
|December 4, 2025
概括
本研究提出了一种新的适应性学习共识控制多代理系统,克服了神经网络的代数循环. 该方法确保在复杂的分布式系统中提供稳健的性能和高效的学习.
科学领域:
- 控制工程 控制工程 控制工程
- 人工智能的人工智能
- 系统科学 系统科学
背景情况:
- 多代理系统 (MAS) 在分布式学习和共识控制方面存在挑战.
- 非严格的纯反结构由于固有的代数循环使控制器设计复杂化.
- 现有的方法经常在计算复杂性和稳定性方面扎.
研究的目的:
- 开发一个统一的自适应学习共识控制框架,用于非严格的纯反MAS.
- 用神经网络近似来解决和解决代数循环问题.
- 为了提高控制方案的稳定性和减少计算开销.
主要方法:
- 整合后退技术与神经网络近似进行统一的框架.
- 开发基于神经网络的解决方案,以规避代数循环问题.
- 实施增量适应机制,以实现高效的参数更新和减少复杂性.
主要成果:
- 拟议的控制方案有效地简化了控制器架构.
- 增量适应显著降低了计算开销.
- 理论分析证实了规定的跟踪性能和闭环信号的统一界限.
结论:
- 开发的分布式学习共识控制对非严格的纯反MAS有效.
- 基于神经网络的方法提供了一个强大的和计算效率高的解决方案.
- 数字模拟验证了算法的性能和学习能力.
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