Enhancing Quadrotor Control Robustness with Multi-Proportional–Integral–Derivative Self-Attention-Guided Deep Reinforcement Learning

Abstract

Deep reinforcement learning has demonstrated flexibility advantages in the control field of quadrotor aircraft. However, when there are sudden disturbances in the environment, especially special disturbances beyond experience, the algorithm often finds it difficult to maintain good control performance. Additionally, due to the randomness in the algorithm’s exploration of states, the model’s improvement efficiency during the training process is low and unstable. To address these issues, we propose a deep reinforcement learning framework guided by Multi-PID Self-Attention to tackle the challenges in the training speed and environmental adaptability of quadrotor aircraft control algorithms. In constructing the simulation experiment environment, we introduce multiple disturbance models to simulate complex situations in the real world. By combining the PID control strategy with deep reinforcement learning and utilizing the multi-head self-attention mechanism to optimize the state reward function in the simulation environment, this framework achieves an efficient and stable training process. This experiment aims to train a quadrotor simulation model to accurately fly to a predetermined position under various disturbance conditions and subsequently maintain a stable hovering state. The experimental results show that, compared with traditional deep reinforcement learning algorithms, this method achieves significant improvements in training efficiency and state exploration ability. At the same time, this study deeply analyzes the application effect of the algorithm in different complex environments, verifies its superior robustness and generalization ability in dealing with environmental disturbances, and provides a new solution for the intelligent control of quadrotor aircraft.