一类具有输出和的非线性系统的数据驱动点对点有限代学习控制
IEEE transactions on cybernetics
|August 28, 2025
概括
本研究介绍了在未知的非线性系统中精确点对点 (PTP) 追踪的数据驱动算法. 新的有限代学习控制 (FILC) 方法可以有效地确保边界追踪错误.
科学领域:
- 控制系统工程
- 非线性动力学
- 机器学习
背景情况:
- 控制具有输出和的未知非线性离散时间系统存在重大挑战.
- 现有的控制方法通常需要准确的系统模型,而这些模型在许多现实应用中是不可用的.
研究的目的:
- 在未知的非线性离散时间系统中开发数据驱动的精确点对点 (PTP) 追踪控制算法.
- 在没有先前系统知识的情况下,在有限的代数内实现边界追踪错误.
主要方法:
- 提出了一个新的数据驱动的有限代学习控制 (FILC) 算法.
- 该算法利用时间域中的递归演变来推导系统输出关系.
- 代域动态线性化技术建立了一个动态数据驱动模型.
- 一个基于分数错误的有限代学习策略确保了趋同.
主要成果:
- 拟议的FILC算法有效地解决了未知的非线性离散时间系统的PTP跟踪控制.
- 在有限数量的代中实现了有限的跟踪错误.
- 理论证明证实了算法的有限代趋同.
- 模拟结果验证了拟议方法的实际有效性.
结论:
- 数据驱动的FILC方法为未知系统模型的复杂控制问题提供了可靠的解决方案.
- 这种方法提供了一种在输出和下实现精确跟踪控制的高效和理论上的有效方法.
- 这项研究表明了数据驱动技术在推进非线性系统的控制工程方面的潜力.
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