Applications of Machine Learning-Enhanced PID Control Across Industrial Domains: A Comprehensive Review

Proportional–Integral–Derivative (PID) controllers are a cornerstone of industrial control systems due to their simplicity and effectiveness. However, traditional PID controllers face challenges such as manual parameter tuning, lack of adaptability, and inefficiency in nonlinear or complex environments. In recent years, machine learning (ML) has emerged as a powerful tool to overcome these limitations, enabling adaptive, intelligent control mechanisms. This paper presents a comprehensive review of the integration of machine learning with PID controllers across various industries. Applications in robotics, automotive systems, process industries, aerospace, biomedical engineering, and energy systems are explored in detail. Machine learning techniques such as reinforcement learning, support vector machines, and evolutionary algorithms are discussed in terms of their role in tuning, fault detection, and optimization. Furthermore, we identify the key challenges and propose future research directions for developing robust, explainable, and real-time ML-PID systems.