Genetic Algorithm-Based Proportional, Integral, Derivative (PID) Gain Optimization in an Offset-Compensated Optical Tracking System

This paper presents a genetic algorithm (GA)-based optimization method for tuning proportional, integral, derivative (PID) gains in an offset-compensated optical tracking system. The system is designed for high-resolution optical identification of unknown space objects and employs initial orbit determination (IOD) to estimate the target trajectory. The controller continuously compensates for image-based center offsets in real time. While prior studies used manually tuned PID gains, this work applies GA to automatically search for gain combinations that minimize root mean square (RMS) tracking errors. The GA was implemented in a lightweight custom framework, and simulation results demonstrate improved performance over the manually selected gains in terms of both RMS error and tracking stability. The proposed approach provides a foundation for automated and adaptive PID tuning in future optical tracking scenarios under varying conditions.