ABSTRACT The operational complexity of modern industrial processes demands control frameworks that are both mathematically rigorous and computationally agile. This paper provides a systematic review of the transformative advances in Nonlinear Model Predictive Control (NMPC) integrated with Deep Learning (DL) methodologies between 2020 and 2026. We categorize the state-of-the-art into three technical pillars: neural-based system identification, computational acceleration via latent-space optimization, and robust architectures for uncertain environments. By analyzing applications across chemical engineering, fusion energy maintenance, and bionic robotics, we evaluate how hybrid frameworks-such as LSTM-based estimators and autoencoder-driven reduced-order models-mitigate the traditional trade-offs between model fidelity and real-time feasibility.
| Mualliflar | Tuyboyov, Oybek |
|---|---|
| Jurnal | Al-Farg'oniy avlodlari |
| Nashr sanasi | 2026-03-06 |
| Son | 1 |
| Betlar | 160-166 |
| Til | Rus |
Artificial intelligence, ; data processing methods, accuracy
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