Proportional Integral Derivative

Proportional-Integral-Derivative (PID) control is a widely used feedback control algorithm aiming to minimize the error between a desired setpoint and a measured process variable by adjusting a manipulated variable. Current research focuses on improving PID controller performance through techniques like integrating it with neural networks, reinforcement learning algorithms (such as Proximal Policy Optimization), and evolutionary algorithms to optimize controller parameters, particularly in applications involving autonomous vehicles, robotics, and industrial processes. These advancements enhance PID's robustness, adaptability, and efficiency across diverse domains, leading to improved control precision and automation in various systems. The impact spans from optimizing industrial processes to enabling more sophisticated control in autonomous systems.