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This is a Quantitative Study study.

October 16, 2025

Optimization Application of PPO Algorithm in Reinforcement Learning in Drone Attitude Balance

Key Points

PPO demonstrated superior stability and control compared to traditional PID controllers.
Performance evaluations showed PPO excelled in simulations and real-life experiments under varying wind conditions.
Reinforcement learning with PPO utilized parameters aimed at minimizing attitude errors and angular rates.
Findings highlight the potential for PPO to enhance drone performance in dynamic environments.

Abstract

Robust attitude and position control remain critical challenges for consumer drones. This studies evaluates the performance of a Reinforcement Leaning algorithm (PPO) against traditional control algorithms on drone's stability in both computer simulation and real life situations. Reinforcement Learning is trained with common parameters and rewards for small attitude error, lower angular rates, and efficient control effort. Performance were measured across level 0 to 5 wind in simulation and level 0 to 3 in real life experimentation. Results showed that PPO out-performed traditional PID controller in both computer simulation and real life experimentation. PPO showed better stability than PID with reasonable actuation. These findings indicate that PPO can produce more robust, precise control than fixed-gain controllers.

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