Zhang-Xuewen/Deep-DeePC
This project is source code of paper Deep DeePC: Data-enabled predictive control with low or no online optimization using deep learning by X. Zhang, K. Zhang, Z. Li, and X. Yin. The objective of this work is to learn the DeePC operator using a neural network and bypass online optimization of conventional DeePC for efficient online implementation.
This project helps operations engineers and process control specialists implement faster predictive control for industrial processes like chemical reactors. It takes historical input/output data from a system and learns to predict its future behavior, allowing for efficient control actions without complex online optimization. The output is a trained model that can quickly determine optimal control signals to maintain desired system performance.
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Use this if you need to control a dynamic system efficiently using historical data, especially when traditional model predictive control is too slow due to extensive online optimization.
Not ideal if your system lacks sufficient historical operational data or if you require full transparency and interpretability of the control decision-making process at every step.
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5
Language
Python
License
Apache-2.0
Category
Last pushed
Dec 17, 2024
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