基于频域的非线性规范化的代学习控制,用于三维球螺杆驱动系统
1College of Information Science and Engineering, Huaqiao University, Xiamen, 361002, China.
ISA transactions
|January 1, 2025
概括
本研究引入了一种新的数据驱动的代学习控制 (ILC) 方法,可以提高未知系统的跟踪性能,而不需要植物模型. 这种方法提高了瞬态性能,并降低了计算负载.
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
- 机器学习 机器学习
背景情况:
- 代学习控制 (ILC) 在重复任务中表现出色,但通常需要名义工厂模型,导致由于模型不匹配而导致性能下降.
- 现有的ILC方法通常由于依赖系统识别而受到限制,这阻碍了它们在未知或不断变化的动态场景中的应用.
研究的目的:
- 为代学习控制 (ILC) 开发一种新的,无模型的规范化概念,适用于未知的离散时间系统.
- 为了提高ILC的短暂跟踪性能,用于代不变和代变化的轨迹跟踪任务.
- 为了减少计算复杂性,并防止过度输入放大在ILC系统.
主要方法:
- 使用数据驱动的频域方法来开发规范化概念,消除了对显式系统模型信息的需求.
- 拟议的方法使输入-输出比率正常化,以有效地管理系统动态.
- 该方法通过在三维球螺杆驱动系统上的实验应用来验证.
主要成果:
- 新的规范化概念成功地为未知的离散时间系统实现了无模型的ILC.
- 与传统基于模型的ILC方法相比,观察到暂时跟踪性能显著改善.
- 该方法在代不变和代变化的轨迹跟踪方面都表现出有效性,展示了其多功能性.
- 实现了减少计算复杂性和控制输入放大.
结论:
- 拟议的数据驱动,无模型的ILC方法为在未知的系统中精确跟踪提供了强大的解决方案,克服了传统方法的局限性.
- 这种规范化技术提高了控制性能和效率,特别是在复杂的现实应用中,如机器人系统.
- 这些发现为将ILC应用于更广泛的动态系统开辟了新的途径,在这些动态系统中,精确的建模具有挑战性或不可能.
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