对有数据丢失的扰乱网络非线性系统进行反向增强学习
IEEE transactions on neural networks and learning systems
|November 7, 2025
概括
本研究介绍了非线性网络控制系统 (NCS) 的反向强化学习 (IRL) 控制. 这些算法有效地模仿目标轨迹,尽管数据丢失和干扰,提高系统性能.
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
- 机器学习 机器学习
背景情况:
- 网络控制系统 (NCS) 面临数据丢失和外部干扰的挑战.
- 模仿目标系统轨迹对于NCS性能至关重要,但在未知动态的情况下很难.
研究的目的:
- 为非线性NCS开发反向强化学习 (IRL) 控制算法.
- 为了应对数据丢失和轨迹模拟中的外部干扰的挑战.
- 在部分模型知识下实现有效控制.
主要方法:
- 开发了一个基于模型的IRL算法,集成了用于干扰拒绝和不确定性管理的$H_{\infty}$控制.
- 提出了一个基于神经网络的,数据驱动的IRL算法,从可用的数据中推断成本函数和控制策略.
- 解决了轨迹中的数据丢失,状态反和控制输入数据传输.
主要成果:
- 证明了拟议的IRL算法的有效轨迹模仿能力.
- 展示了对随机数据丢失和外部干扰的稳定性.
- 通过全面的模拟研究验证了性能.
结论:
- 开发的IRL算法能够在非线性NCS中进行强大的轨迹模拟.
- 这些方法减少了对完整系统模型的依赖,提供了实用优势.
- 尽管存在重大运行不确定性,但仍可实现成功的轨迹模拟.
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