在离散时间系统中加速ILC的噪音错误适应权衡策略
IEEE transactions on cybernetics
|June 18, 2025
概括
本研究介绍了一种适应性重权和和策略,以加快代学习控制 (ILC) 的融合. 该方法提高了跟踪精度和控制系统中对随机噪声的稳定性.
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:
- 代学习控制 (ILC) 对于重复性任务至关重要,但可能很慢地趋同.
- 随机噪声往往会降低ILC系统的性能和稳定性.
- 传统的ILC方法难以平衡融合速度和噪声弹性.
研究的目的:
- 开发一种新的战略,以加快ILC的融合.
- 为了增强ILC对随机噪声的稳定性.
- 为了提高ILC系统的整体跟踪准确性和性能.
主要方法:
- 对错误信号进行自适应重量调整,以便在更新过程中优先考虑较大的错误.
- 引入一个和机制,以减轻噪声对重量计算的影响.
- 开发一个收定理来分析和参数的影响.
主要成果:
- 与传统的ILC相比,拟议的战略显著加速了融合.
- 持续观察到对随机噪声的强度提高.
- 通过模拟和实验,在各种ILC实现中证明了增强的跟踪准确性.
结论:
- 适应性重权和和策略有效地加速了ILC的融合.
- 该方法可以保持或提高跟踪精度,同时提高噪声稳定性.
- 这种方法为改善噪音环境中的ILC性能提供了一个实际的解决方案.
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