REVIEW OF SYNERGIZING DEEP LEARNING AND NONLINEAR MODEL PREDICTIVE CONTROL

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. The review further discusses emerging trends in cyber-secure control via homomorphic encryption and the integration of Physics-Informed Neural Networks (PINNs). Our synthesis highlights persistent gaps in formal stability proofs and model interpretability, offering a strategic roadmap for future research in autonomous industrial intelligence.

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