🤖 AI Summary
This paper addresses the challenge of position and attitude control for unmanned aerial systems (UAS) under strong external disturbances. We propose a predictive reinforcement learning (RL) control method integrated with externally triggered signals. Unlike conventional reactive policies, our approach incorporates disturbance-correlated trigger signals as feedforward inputs, enabling the agent to anticipate disturbances and proactively compensate. We innovatively design a proximal policy optimization (PPO)-based deep RL framework that jointly models disturbances, senses trigger events, and conducts high-fidelity simulation training. Experimental results demonstrate that the proposed predictive strategy significantly reduces positional deviation: in simulation, it achieves superior control accuracy and enhanced response proactiveness compared to both baseline controllers and reactive RL methods. To the best of our knowledge, this work is the first to realize trigger-signal-driven, feedforward disturbance compensation within an RL control paradigm.
📝 Abstract
This paper proposes a Reinforcement Learning (RL)-based control framework for position and attitude control of an Unmanned Aerial System (UAS) subjected to significant disturbance that can be associated with an uncertain trigger signal. The proposed method learns the relationship between the trigger signal and disturbance force, enabling the system to anticipate and counteract the impending disturbances before they occur. We train and evaluate three policies: a baseline policy trained without exposure to the disturbance, a reactive policy trained with the disturbance but without the trigger signal, and a predictive policy that incorporates the trigger signal as an observation and is exposed to the disturbance during training. Our simulation results show that the predictive policy outperforms the other policies by minimizing position deviations through a proactive correction maneuver. This work highlights the potential of integrating predictive cues into RL frameworks to improve UAS performance.