基于长序稳定的库普曼网络的非线性动态系统的模型预测控制
Qifan Wang1, Yuhong Jin1, Lei Hou1
1School of Astronautics, Harbin Institute of Technology, Harbin, 150001, PR China.
ISA transactions
|July 13, 2025
概括
本研究介绍了一种稳定深度库普曼网络与模型预测控制 (SDKN-MPC) 的非线性控制. 与现有的深度学习方法相比,SDKN-MPC方法提供了快速的融合和优越的长期预测稳定性.
科学领域:
- 控制理论 控制理论
- 机器学习 机器学习
- 非线性动力学是一种非线性动力学.
背景情况:
- 库普曼方法使用高维映射将非线性系统转换为线性系统.
- 基于深度学习的库普曼方法面临着缓慢融合和不稳定的长期预测的挑战.
研究的目的:
- 开发一个具有模型预测控制 (SDKN-MPC) 的稳定深度库普曼网络,以增强非线性控制.
- 为了解决现有的深度学习库普曼方法在合速度和预测稳定性方面的局限性.
主要方法:
- 使用一个稳定的库普曼解决算法来导出一个稳定的库普曼运算符.
- 员工交叉神经网络培训,用于嵌入功能和库普曼操作员,直到融合.
- 集成模型预测控制 (MPC) 与库普曼操作员用于高维系统控制.
- 整合了一个辅助网络来完善预测控制输入.
主要成果:
- SDKN-MPC方法显示了快速的趋同.
- 与现有的深度学习方法相比,实现了优越的长期预测性能.
- 从控制任务中成功地提取了更有效的非线性特征.
结论:
- 拟议的SDKN-MPC方法为非线性控制提供了稳定高效的方法.
- SDKN-MPC在预测性能和融合速度方面提供了显著的改进.
- 这种方法促进了库普曼方法在复杂的控制系统中的应用.
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