一类采样数据非线性系统的基于模式的学习和控制
Qinchen Yang1, Fukai Zhang1, Cong Wang1
1School of control Science and Engineering, Shandong University, Jinan, 250000, China.
概括
本研究为动态工业系统引入了一种新的基于模式的学习控制策略. 它使用神经网络和确定性学习来调整控制器以适应不断变化的条件,确保稳定和高性能运行.
科学领域:
- * 工业控制系统工程 * 工业控制系统工程
- * 适应性控制理论
- * 控制中的机器学习
背景情况:
- * 传统的控制方案在动态的工业环境中失败,系统参数不断变化.
- *采样数据系统需要先进的控制策略来管理多种操作场景.
- *复杂性源于时间变化的动态和实时适应的需要.
研究的目的:
- *为采样数据系统开发一个强大的基于模式的学习和控制策略.
- * 应对工业过程中多个动态操作场景所带来的挑战.
- * 为了提高系统稳定性和控制性能在不同的条件下.
主要方法:
- *双阶段识别:设计采样数据神经网络 (NN) 控制器,并使用确定性学习 (DL) 理论构建候选控制器库.
- *第二个识别阶段:使用估计器精确识别闭环系统动态.
- *识别和控制阶段:通过最小残余原则快速检测场景变化,并选择合适的学习控制器.
主要成果:
- *使用基于DL理论的NNs准确近似未知系统动态.
- * 在正常控制器操作下有效识别系统动态.
- * 快速准确地检测控制场景变化.
- *成功选择合适的学习控制器,确保稳定性和性能.
结论:
- * 提出的基于模式的学习控制策略有效地处理动态的工业过程.
- *这种方法确保了系统稳定性和在多种操作场景中实现高性能控制.
- *模拟结果验证了适应性控制方法的有效性.
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