对于非线性开关互连系统的基于强化学习的预定义性能控制.
IEEE transactions on cybernetics
|February 16, 2026
概括
本研究介绍了非线性交换系统的强化学习 (RL) 控制框架,允许预设性能,如合时间和准确性. 它克服了无法测量的状态和复杂的切换,用于实际应用.
科学领域:
- 控制理论 控制理论
- 人工智能的人工智能
- 系统动力学系统动力学
背景情况:
- 非线性交换互连系统由于无法测量的状态和复杂的交换行为,存在重大控制挑战.
- 现有的控制方法往往难以保证性能和适应系统变化的能力.
研究的目的:
- 为非线性开关互连系统开发基于强化学习 (RL) 的控制框架.
- 确保预定义的绩效指标,包括趋同时间和准确性.
- 为了应对无法测量的状态和群体平均停留时间切换机制所带来的挑战.
主要方法:
- 重建针对非线性和相互连接项的系统方程,通过神经网络 (NN) 进行近似.
- 基于NN的切换状态观察器的设计,用于估计无法测量的状态.
- 使用后退框架开发分布式最佳控制器,并将性能转换功能集成到成本函数中.
- 通过识别器-演员-关键架构来接近控制规律.
- 集团平均停留时间稳定性分析的概括,以实现最佳控制.
主要成果:
- 拟议的框架有效地处理无法测量的状态和组平均停留时间的切换.
- 通过参数配置,可以预设收时间和精度.
- 与现有方法相比,该方法显示了增强的可扩展性和实用性.
- 模拟示例验证了拟议方法的有效性和优越性.
结论:
- 开发的基于RL的控制框架为非线性交换互联系统提供了强大的和可适应的解决方案.
- 该方法实现了保证的性能,并克服了以前方法的关键局限性.
- 这项工作为需要精确可靠控制的现实应用提供了巨大的潜力.
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