论文标题
基于固定点的分层控制中快速-NMPC和深度学习方法的调查
Investigation of fast-NMPC and deep learning approach in fixed-point-based hierarchical control
论文作者
论文摘要
本文探讨了最近提出的分层控制框架的一些变化。该框架致力于控制一个相互联系的子系统网络，例如描述低温过程或发电厂的网络。最近的调查表明，处理限制和非线性可能会挑战该方法的实时可行性。本文研究并结合了两个成功的方向，即使用截短的基于截断的快速梯度和基于神经网络的控制器建模，以减少最关键的子系统的计算时间。还表明，通过这样做，可以大幅度缩短控制更新周期，并且闭环性能高度改进。因此，本文可以看作是实时分布式NMPC设计中一些关键想法的具体实现和验证。所有概念都使用现实生活中的低温冰箱的现实且充满挑战的例子进行了验证。
This paper explores some variations of a hierarchical control framework that has been recently proposed. The framework is dedicated to control a network of interconnected subsystems such as the ones describing cryogenic processes or power plants. Recent investigations showed that handling constraints and nonlinearities might challenge the real-time feasibility of the approach. This paper investigates and combine two successful directions, namely, the use of truncated fast gradient and deep neural networks based controller modeling in order to reduce the computation time of the most critical subsystem. It is also shown that by doing so, the control updating period can be drastically reduced and the closed-loop performances highly improved. The paper can therefore be seen as a concrete implementation and validation of some key ideas in real-time distributed NMPC design. All the concepts are validated using the realistic and challenging example of real-life cryogenic refrigerator.
