Background: Industrial automation systems face increasingly complex operational demands requiring real-time performance optimization under dynamic and uncertain conditions. Traditional model predictive control (MPC) strategies, while effective for constrained optimization, often struggle with computational burden and model inaccuracies in highly nonlinear industrial processes. Simultaneously, data-driven machine learning (ML) approaches offer powerful pattern recognition capabilities but frequently lack the interpretability and constraint-handling guarantees essential for safety-critical industrial applications.

Objective: This study proposes and validates a novel hybrid framework that synergistically integrates machine learning algorithms with model predictive control to achieve superior real-time performance optimization in industrial automation systems. The framework, termed ML-MPC (Machine Learning-Enhanced Model Predictive Control), leverages deep neural networks for system identification and disturbance prediction while maintaining the rigorous constraint satisfaction and stability guarantees inherent to MPC formulations.

Methods: A multi-layer architecture was developed comprising: (i) a Long Short-Term Memory (LSTM) network for dynamic system identification and state estimation, (ii) a convolutional neural network (CNN) module for real-time disturbance pattern recognition, (iii) an adaptive MPC controller utilizing the ML-derived models for optimization, and (iv) a supervisory reinforcement learning agent for meta-parameter tuning. The framework was implemented and validated on three industrial case studies: a continuous chemical reactor, a multi-axis robotic manufacturing cell, and a smart grid energy management system. Experimental validation was conducted using both high-fidelity simulation environments and real-world pilot plant data spanning 18 months of operational records.

Results: The proposed ML-MPC framework demonstrated statistically significant improvements over conventional MPC and standalone ML approaches across all three case studies. Specifically, the hybrid approach achieved a 23.7% reduction in energy consumption (p < 0.001), a 31.2% improvement in tracking accuracy (p < 0.001), a 45.8% reduction in computational time per optimization cycle (p < 0.01), and a 18.4% increase in overall equipment effectiveness (OEE). The framework maintained robust constraint satisfaction with a violation rate below 0.02% across all experimental conditions, representing a five-fold improvement over benchmark ML-only approaches.

Conclusions: The ML-MPC framework represents a significant advancement in industrial automation optimization by effectively combining the adaptability and predictive power of machine learning with the theoretical guarantees of model predictive control. The results demonstrate that hybrid approaches can overcome the individual limitations of both paradigms, offering a practical and scalable solution for real-time industrial performance optimization. The framework's modular architecture facilitates deployment across diverse industrial domains, contributing to the broader advancement of Industry 4.0 intelligent manufacturing systems