Journal of Artificial Intelligence, Machine Learning and Soft Computing

Authors: Priya Mehta, Prof. Suresh Gupta

Abstract: PID (Proportional-Integral-Derivative) controllers are widely used in industrial control systems, but tuning these controllers can be a complex and time-consuming process. This paper investigates the application of machine learning techniques to optimize PID controller parameters for various industrial processes. The proposed methods include genetic algorithms, neural networks, and reinforcement learning, which aim to automate the tuning process and improve controller performance. Case studies in process control and robotics demonstrate the effectiveness of these machine learning-driven optimizations.

Keywords: PID Controllers, Machine Learning, Genetic Algorithms, Neural Networks, Tuning Optimization