Neural network implementation of model predictive control with stability guarantees
This work explores the use of supervised learning on data generated by a model predictive controller (MPC) to train a neural network (NN). The goal is to create an approximate control policy that can replace the MPC, offering reduced computational complexity while maintaining stability guarantees. T...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-09-01
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| Series: | Digital Chemical Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772508125000468 |
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