模型预测控制
过程(计算)
计算机科学
可靠性(半导体)
理论(学习稳定性)
代表(政治)
人工智能
深度学习
超调(微波通信)
过程控制
机器学习
控制理论(社会学)
控制(管理)
控制工程
工程类
操作系统
政治
物理
电信
功率(物理)
量子力学
法学
政治学
作者
Keke Huang,Wei Ke,Fanbiao Li,Chunhua Yang,Weihua Gui
出处
期刊:IEEE Transactions on Industrial Electronics
[Institute of Electrical and Electronics Engineers]
日期:2023-11-01
卷期号:70 (11): 11544-11554
被引量:14
标识
DOI:10.1109/tie.2022.3229323
摘要
Modern industrial processes often operate under different modes, which brings challenges to model predictive control (MPC). Recently, most MPC related methods would establish prediction models independently for different modes, which results in their control effect highly relying on switching strategies. Inspired by the powerful representation capabilities of deep learning, this article proposed a deep learning based MPC method. Specifically, the LSTM network is applied to predict behaviors of controlled system, which can automatically match different operation modes without switching strategy. Then combined with MPC framework, an adaptive gradient descent method is introduced to handle optimization problem and its constraints. In addition, stability and feasibility analysis have been conducted from the aspect of theory to ensure practical application of the proposed method. Experiments on a numerical simulation process and an industrial process platform show the strength and reliability of the proposed method, which reduces the overshoot by about 10 $\%$ compared to common learning-based MPC methods and improves the control accuracy effectively.
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