Precision positioning in free-space optical communication systems via PID control tuned by RL
Accurate positioning of optical components is essential for maintaining beam alignment in free-space optical (FSO) communication systems. This work investigates reinforcement-learning-assisted tuning of cascaded position and velocity PID controllers for an optical deflector that moves the end of an optical fiber in the focal plane of an optical system. A Deep Deterministic Policy Gradient (DDPG) agent adjusts six PID coefficients through interaction with a physical experimental stand. The stand supports target-coordinate updates of up to $12$ kHz, while the agent and the controlled device are located approximately $200$ km apart and exchange data over UDP. After $5000$ training sessions, two fixed coefficient sets are selected and compared with a manually tuned baseline. For a pseudo-random target trajectory, the best RL-tuned set reduces the range of the radial positioning error from $119$ to $82$, corresponding to a $31\%$ reduction, and decreases its standard deviation from $15$ to $12$. For a constant zero target, the RL-tuned sets do not improve the radial error range. The results demonstrate the potential of DDPG for experimental PID tuning in dynamic positioning tasks and indicate the need for multi-regime optimization to achieve consistent performance under different operating conditions.