文献类型：期刊文献

英文题名：Nonlinear model predictive control for distributed parameter systems by time-space-coupled model reduction

作者：Qing, Xiangyun[1];Song, Jun[1];Jin, Jing[1];Zhao, Shuangliang[2,3]

机构：[1]East China Univ Sci & Technol, Minist Educ, Key Lab Smart Mfg Energy Chem Proc, Shanghai, Peoples R China;[2]East China Univ Sci & Technol, State Key Lab Chem Engn, Shanghai 200237, Peoples R China;[3]East China Univ Sci & Technol, Sch Chem Engn, Shanghai 200237, Peoples R China

年份：2021

卷号：67

期号：8

外文期刊名：AICHE JOURNAL

收录：;EI(收录号：20211210106219);WOS:【SCI-EXPANDED(收录号:WOS:000629271100001)】；

基金：National Natural Science Foundation of China, Grant/Award Numbers: 21878078, 91934302

语种：英文

外文关键词：deep learning; distributed parameter system; Lyapunov exponent; model predictive control; model reduction

摘要：Nonlinear high-dimensional distributed parameter systems (DPSs) described by sets of parabolic partial different equations (PDEs) exhibit a dominant, low-dimensional slow behavior that can be captured using model reduction. A time-space-coupled model reduction architecture combining encoder-decoder networks with recurrent neural networks (RNNs) was presented in our previous work, for modeling the spatiotemporal dynamics of DPSs without recourse to the governing equations. In this work, we further understand the stability of the training dynamics of the deep architecture by using the Lyapunov exponents (LEs). Subsequently, we construct nonlinear model predictive control (MPC) formulations for the DPS based on the learned, dimensional-reduced model. We use a path-integral optimal control algorithm for MPC implementation to avoid any analytic derivatives of the dynamics. The effectiveness of integration of a deep neural network-based model with MPC is demonstrated in a tubular reactor with recycle cases. The results of the simulation also show that the LE can serve as a readout of training stability for the learned dynamical model.