Pid-kontrollü yüz takip iha'sinin performans analizi

This thesis examines closed-loop performance of a vision-based tracking controller on a DJI Tello micro-UAV using classical Proportional–Integral–Derivative (PID) control. We perform step-response system identification for four axes—yaw, lateral (left–right), longitudinal (forward–backward), and vertical (up–down)—by logging camera-derived error, controller outputs (RC commands), and vehicle states. Axis-wise SISO models indicate that yaw behaves as a pure integrator, while the translational axes are well captured by type-1 (integrator-with-lag) dynamics. We implement P, PI, PD, and PID controllers under identical test scripts and evaluate percent overshoot and settling time within a ±5% band from flight logs sampled at 30 Hz. Results show PD provides the best trade-off on translational motion—reduced overshoot with shorter settling—whereas yaw is adequately regulated by proportional action alone. Integral action without anti-windup causes saturation and oscillation; adding back-calculation anti-windup and a first-order derivative filter improves robustness. Contributions are: (i) reproducible axis-wise models and tests, (ii) a unified logging/plotting pipeline for fair controller comparisons, and (iii) practical guidance on derivative filtering and anti-windup for small UAVs with vision feedback. These findings establish a quantitative baseline for transitioning from classical PID to more advanced control on resource-constrained aerial platforms.