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Updated: Jul 16, 2025

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通过强化学习来为多代理系统的适应性事件触发的双边形态形成
IEEE transactions on neural networks and learning systems
|September 20, 2023
概括
本研究介绍了多代理系统 (MAS) 的在线学习框架,以实现能源效率高的双方形成控制,尽管存在未知的动态和对抗性相互作用,确保系统稳定性.
科学领域:
- 控制理论 控制理论
- 人工智能的人工智能
- 机器人技术 机器人技术 机器人技术
背景情况:
- 研究非线性离散时间多代理系统 (MAS) 的挑战,包括未知的动态和对抗性相互作用.
- 解决了在复杂的MAS环境中对能源效率控制和形成控制的需求.
研究的目的:
- 为MAS开发一个在线学习和节能控制策略.
- 实现对MAS具有未知的动态和对抗性相互作用的双边形成控制.
- 设计一个适应性事件触发机制,以实现高效的通信和计算.
主要方法:
- 使用符号图形理论制定了一个分布式组合测量误差函数,以将双边形成转换为共识问题.
- 使用动态线性化技术开发了一种增强的线性化控制器模型.
- 提出了一个在线学习适应性事件触发 (ET) 演员关键神经网络 (AC-NN) 框架,具有优化的NN和适应性ET机制.
主要成果:
- 成功地将两党组建问题转化为共识问题.
- 使用Lyapunov函数证明了拟议的形成控制框架的趋同.
- 通过模拟和实验研究验证了算法的有效性.
结论:
- 拟议的在线学习适应性ET AC-NN框架有效地解决了非线性离散时间MAS中的能源效率高的双方形成控制.
- 该方法对未知动力学和对抗性相互作用具有强大耐用性.
- 该方法为需要协调多代理行为的实际应用提供了有前途的解决方案.
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