适应性代学习 不重复性系统的可靠控制,具有多个代变化的参数不确定性
IEEE transactions on cybernetics
|February 13, 2026
概括
本研究介绍了针对非重复性系统的自适应代学习控制方案,克服了以前的局限性. 新方法有效地处理不确定性,执行器故障和状态延迟,以改善实际应用.
科学领域:
- 控制工程 控制工程 控制工程
- 适应性控制系统 适应性控制系统
- 非线性系统是非线性系统.
背景情况:
- 代学习控制 (ILC) 被其重复性要求所阻碍.
- ILC的实际应用受到非重复性系统和不确定性所限制.
- 现有的控制方法与同时发生的执行器故障和状态延迟作斗争.
研究的目的:
- 提出一个新的自适应代学习可靠控制 (ILRC) 方案.
- 处理具有代变化的参数不确定性的非重复系统.
- 在ILRC设计中同时考虑执行器故障和状态延迟.
主要方法:
- 利用类- $k_{infty }$ 函数和神经网络来管理未建模的动态.
- 实施控制信号转换以补偿执行器低效率.
- 开发了一种创新的参数估计机制,其中包含了超标触函数和非重复不确定性的辅助序列.
主要成果:
- 实现了系统输出零错误的融合.
- 成功地弥补了执行器故障和状态延迟.
- 证明有效处理代变化的参数不确定性.
结论:
- 拟议的ILRC方案比现有方法提供了更好的性能和实用性.
- 这种方法需要对系统动态的弱假设和对不确定性的初步知识是最小的.
- 控制器表现出强大的学习能力,使其适合复杂,不重复的系统.
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