ADAPTIVE PID CONTROL OF UAV POSITION USING VISUAL DISPLACEMENT

Abstract

Visual-displacement-based control of unmanned aerial vehicles (UAVs) requires the position controller to operate directly on image-space errors while adapting to changes in motion conditions and limited control authority. This study presents an adaptive PID gain-scheduling method for UAV route following using visual displacement estimated from a downward-facing camera. The controller operates in image pixel space and adjusts the PID gains according to the current motion condition, visual-positioning confidence, route progression, cross-track error, and control-signal saturation. Saturation is handled independently for the two image-plane axes, allowing the affected axis to use a saturation-limited gain schedule while the other axis retains the configuration corresponding to the underlying motion condition. The proposed method was implemented and evaluated in a Gazebo/ArduPilot Software-in-the-Loop environment and compared with a fixed-gain PID controller. Preliminary development tests also included triangular and zigzag route geometries, but these runs were excluded from the final statistical validation. The final validation dataset consisted of 100 experimental runs, with 50 runs performed for each controller mode under the same predefined square route and simulation configuration. Performance was evaluated using route-completion rate, trajectory-error measures, cross-track error, route-completion time, and control-signal saturation occupancy. Route-completion proportions were compared using Fisher’s exact test, while continuous run-level metrics were analysed using Mann–Whitney U tests with Holm correction for multiple comparisons. The adaptive controller completed 46 of 50 validation runs, corresponding to a route-completion rate of 92%, whereas the fixed-gain controller completed 35 of 50 runs, corresponding to 70%. The difference in completion proportions was statistically significant. For successfully completed routes, no statistically significant differences were observed after Holm correction in the principal trajectory-error measures. Mean control-signal saturation occupancy decreased from 52.60% for the fixed-gain controller to 48.00% for the adaptive controller, and this difference remained statistically significant after correction for multiple comparisons. The results indicate that, under the investigated simulation conditions, the proposed adaptive PID gain-scheduling method was associated with a higher route-completion rate and reduced operation at the control limits without statistically significant deterioration in observed trajectory-following accuracy. The conclusions are limited to the investigated square-route validation and Software-in-the-Loop environment and require further evaluation using additional route geometries, disturbances, and hardware-based UAV experiments.