ADVANCED MACHINE LEARNING FRAMEWORKS FOR OPTIMIZATION AND INTELLIGENT CONTROL IN LARGE-SCALE ENGINEERING SYSTEMS

Abstract

Large-scale engineering systems spanning power grids, industrial manufacturing networks, autonomous transportation infrastructures, and smart city platforms present optimization and control challenges of a complexity and dimensionality that fundamentally exceed the capacity of classical analytical methods, necessitating the development of advanced machine learning frameworks capable of learning adaptive control policies from high-dimensional system data while guaranteeing the safety, stability, and robustness properties that safety-critical engineering deployments demand. This paper presents a comprehensive unified framework integrating deep reinforcement learning, Gaussian process-based Bayesian optimization, physics-informed neural networks, and multi-agent coordination architectures into a hierarchical intelligent control system evaluated across three large-scale engineering benchmarks: a 118-bus power grid with 54 controllable generation units, a 12-stage chemical process plant with coupled nonlinear dynamics, and a 200-node urban traffic network with mixed autonomous and human-driven vehicles. The proposed Hierarchical “Adaptive Machine Learning Control (HAMLC)” framework achieves mean control performance improvements of 23.4 percent over model predictive control baselines, 31.7 percent over classical proportional-integral-derivative control, and 12.1 percent over single-algorithm machine learning benchmarks, while maintaining constraint violation rates below 0.3 percent across all test scenarios. Formal stability guarantees derived from Lyapunov function approximation theory are validated computationally, and the framework demonstrates robust performance degradation under sensor noise, communication delays, and partial observability conditions representative of real-world large-scale engineering deployments. The integration of physics-informed neural network components is shown to reduce the training data requirements by 67 percent compared to purely data-driven baselines, addressing a critical bottleneck for practical deployment in engineering systems where operational data collection is constrained by safety and cost considerations.