Prediction and Control of Hovercraft Cushion Pressure Based on Deep Reinforcement Learning

This paper proposes a deep reinforcement learning-based predictive control scheme to address cushion pressure prediction and stabilization in hovercraft systems subject to modeling complexity, dynamic instability, and system delay. Notably, this work introduces a long short-term memory (LSTM) network with a temporal sliding window specifically designed for hovercraft cushion pressure forecasting. The model accurately captures the dynamic coupling between fan speed and chamber pressure while explicitly incorporating inherent control lag during airflow transmission. Furthermore, a novel adaptive behavior cloning mechanism is embedded into the twin delayed deep deterministic policy gradient with behavior cloning (TD3-BC) framework, which dynamically balances reinforcement learning (RL) objectives and historical policy constraints through an auto-adjusted weighting coefficient. This design effectively mitigates distribution shift and policy degradation in offline reinforcement learning, ensuring both training stability and performance beyond the behavior policy. By integrating the LSTM prediction model with the adaptive TD3-BC algorithm, a fully data-driven control architecture is established. Finally, simulation results demonstrate that the proposed method achieves high accuracy in cushion pressure tracking, significantly improves motion stability, and extends the operational lifespan of lift fans by reducing rotational speed fluctuations.