论文标题
非线性输入转换的无词典Koopman模型预测控制
Dictionary-free Koopman model predictive control with nonlinear input transformation
论文作者
论文摘要
本文介绍了一种基于Koopman操作员模型预测控制的数据驱动控制方法。与退出的方法不同，该方法不需要字典并结合了非线性输入转换，从而允许更准确的预测，并且临时调整较少。除此之外，该方法还允许输入量化并利用对称性，从而降低了离线和在线的计算成本。重要的是，该方法保留了模型预测控制中解决的优化问题的凸度。数值示例与现有方法以及学习不连续提升功能的能力相比表现出卓越的性能。
This paper introduces a method for data-driven control based on the Koopman operator model predictive control. Unlike exiting approaches, the method does not require a dictionary and incorporates a nonlinear input transformation, thereby allowing for more accurate predictions with less ad hoc tuning. In addition to this, the method allows for input quantization and exploits symmetries, thereby reducing computational cost, both offline and online. Importantly, the method retains convexity of the optimization problem solved within the model predictive control online. Numerical examples demonstrate superior performance compared to existing methods as well as the capacity to learn discontinuous lifting functions.
