Title: Investigation of fast-NMPC and deep learning approach in fixed-point-based hierarchical control Show Chinese title

Title: 基于固定点的分层控制中快速NMPC和深度学习方法的研究

Abstract: 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. Show Chinese abstract

Abstract: 本文探讨了最近提出的一种分层控制框架的一些变体。 该框架专门用于控制相互连接的子系统网络，例如描述低温过程或电厂的系统。 最近的研究表明，处理约束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，即使用截断快速梯度和基于深度神经网络的控制器建模，以减少最关键子系统的计算时间。 还表明，这样做可以大幅缩短控制更新周期，并显著提高闭环性能。 因此，本文可以被视为对实时分布式NMPC设计中一些关键思想的具体实现和验证。 所有概念都通过现实且具有挑战性的实际低温冰箱示例进行了验证。