LSTM-MPC: A Deep Learning Based Predictive Control Method for Multimode Process Control
Keke Huang, Ke Wei, Fanbiao Li, Chunhua Yang, Weihua Gui
Abstract
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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> compared to common learning-based MPC methods and improves the control accuracy effectively.