模型预测控制
自编码
控制理论(社会学)
非线性系统
循环神经网络
计算机科学
人工智能
李雅普诺夫函数
还原(数学)
人工神经网络
数学
控制(管理)
物理
几何学
量子力学
作者
Tianyi Zhao,Yingzhe Zheng,Jie Gong,Zhe Wu
标识
DOI:10.1016/j.cherd.2022.02.005
摘要
In this work, we develop a model predictive control scheme for nonlinear systems using autoencoder-based reduced-order machine learning models. First, an autoencoder is developed for model order reduction by projecting the process states onto a low-dimensional space using data generated from open-loop simulations of the nonlinear system in the original high-dimensional space. Subsequently, reduced-order recurrent neural networks (RNN) are developed to capture the dominant dynamics of the nonlinear system using the low-dimensional data. Lyapunov-based model predictive control (MPC) scheme using RNN models in low-dimensional space is developed to stabilize the nonlinear system. Finally, a diffusion-reaction process example is used to demonstrate the effectiveness of the proposed reduced-order RNN modeling approach and RNN-based predictive control method.
科研通智能强力驱动
Strongly Powered by AbleSci AI