Adaptive Position-Loop PID Gain Tuning for UAVs Using Deep Reinforcement Learning

Abstract

Unmanned aerial vehicles (UAVs) require accurate and robust position tracking in the presence of environmental disturbances. Although outer-loop position control is typically implemented using PID regulators, manual gain tuning is timeconsuming and often leads to suboptimal performance, especially under wind uncertainty and varying operating conditions. In contrast to most existing studies that focus on attitude control adaptation, this paper proposes a reinforcement learning-based adaptive gain tuning approach for position-loop PID control of UAVs without manual intervention. A Deep Deterministic Policy Gradient algorithm is employed and evaluated under two scenarios: nominal conditions without wind and disturbed conditions with wind uncertainty. Simulation results demonstrate that the learned gains consistently outperform hand-tuned PID controllers in terms of trajectory tracking accuracy and transient response. In addition, three different approaches are investigated to further validate the effectiveness and robustness of the proposed framework.