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对于具有非线性不等式状态约束的相互连接系统,事件触发的去中心化自适应的批评学习控制.
Wenqian Du1, Mingduo Lin2, Guoling Yuan3
1School of Systems Science, Beijing Normal University, Beijing, 100875, China; Traffic Management Bureau of Wuhan Public Security Bureau, Wuhan, 430030, China.
一种新的事件触发的去中心化自适应式批评学习 (ACL) 控制方法解决了相互连接系统中的非线性约束. 这种方法通过只在必要时更新控制策略来优化资源使用,确保系统稳定性.
科学领域:
- 控制系统工程 控制系统工程
- 人工智能的人工智能
- 非线性动力学是一种非线性动力学.
背景情况:
- 相互连接的系统经常面临复杂的非线性状态约束.
- 分散的控制策略对于管理大规模系统至关重要.
- 适应式批评学习 (ACL) 为最佳控制提供了一个强大的框架.
研究的目的:
- 提出一个由事件触发的去中心化自适应式批评学习 (ACL) 控制方法.
- 在相互连接的系统中处理非线性不平等状态约束.
- 为了提高计算和通信效率.
主要方法:
- 使用松函数将非线性不平等约束转换为平等约束.
- 通过地方政策代和汉密尔顿-雅各比-贝尔曼方程,制定去中心化的控制法律.
- 实施一种新的事件触发机制,以节约资源的控制更新.
主要成果:
- 对于孤立的子系统,成功地获得了事件触发的分散控制规律.
- 为整个互联系统制定了整体最佳控制策略.
- 为闭环系统和神经网络重量估计错误保证统一的最终边界性.
结论:
- 拟议的事件触发ACL方法有效地管理相互连接系统中的非线性约束.
- 该方法显著减少了计算和通信开销.
- 模拟结果验证了方法的有效性和稳定性保证.
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