基于数据的强大的跟踪控制学习系统在干扰观察员的学习系统
IEEE transactions on cybernetics
|November 25, 2025
概括
本研究介绍了一种强大的跟踪控制方法,用于面临代变化干扰的代学习控制 (ILC) 系统,而不需要准确的模型. 干扰观察员 (DOB) 估计干扰和不确定性,改善跟踪性能.
科学领域:
- 控制系统工程 控制系统工程
- 机器人技术 机器人技术 机器人技术
- 信号处理 信号处理
背景情况:
- 高精度跟踪对于代学习控制 (ILC) 系统至关重要.
- 代变化的干扰对实现可靠的跟踪构成重大挑战.
- 准确的系统模型信息通常无法在实际的ILC应用中获得.
研究的目的:
- 为具有代变化的干扰和未知模型的ILC系统开发一个强大的跟踪控制策略.
- 为了提高ILC系统的跟踪精度,尽管不可预测,不断变化的干扰.
- 在不确定的环境中解决传统ILC方法的局限性.
主要方法:
- 使用测试代的输入输出数据构建一个名义ILC系统模型.
- 建立一个干扰观察员 (DOB) 来估计代变化的干扰和模型不确定性.
- 制定基于DOB的ILC更新法,包括扰乱和不确定性估计.
主要成果:
- 拟议的基于DOB的ILC更新法显著改善了在代变化的干扰下跟踪性能.
- 追踪误差被证明是连续依赖于干扰的第二阶变化率的边界.
- 当代变化的干扰具有收的变化速率时,可以实现完美的跟踪.
结论:
- 开发的基于DOB的ILC方法有效地处理代变化的干扰,而不需要准确的系统模型.
- 该方法仅依赖于测试代的输入和输出数据来设计DOB和更新法律.
- 这种方法为提高ILC系统的稳定性和复杂场景中的跟踪精度提供了实用解决方案.
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