🤖 AI Summary
Linear controllers fail under strong gusts due to nonlinear flow-field interactions. Method: This paper proposes a Transformer-based deep reinforcement learning (DRL) framework for nonlinear aerodynamic lift control using sparse surface pressure feedback. It innovatively integrates expert-policy pretraining—using a linear controller as prior knowledge—with task-level transfer learning (from single- to multi-gust scenarios). The framework identifies that pitch control at the quarter-chord point dominantly modulates added-mass effects, enabling low-energy, high-precision lift regulation. Results: The learned policy significantly outperforms optimal proportional control, with performance gains increasing with gust count. It achieves zero-shot generalization to arbitrary-length gust sequences and demonstrates broad applicability and robustness across diverse airfoils and inflow conditions.
📝 Abstract
A linear flow control strategy designed for weak disturbances may not remain effective in sequences of strong disturbances due to nonlinear interactions, but it is sensible to leverage it for developing a better strategy. In the present study, we propose a transformer-based reinforcement learning (RL) framework to learn an effective control strategy for regulating aerodynamic lift in gust sequences via pitch control. The transformer addresses the challenge of partial observability from limited surface pressure sensors. We demonstrate that the training can be accelerated with two techniques -- pretraining with an expert policy (here, linear control) and task-level transfer learning (here, extending a policy trained on isolated gusts to multiple gusts). We show that the learned strategy outperforms the best proportional control, with the performance gap widening as the number of gusts increases. The control strategy learned in an environment with a small number of successive gusts is shown to effectively generalize to an environment with an arbitrarily long sequence of gusts. We investigate the pivot configuration and show that quarter-chord pitching control can achieve superior lift regulation with substantially less control effort compared to mid-chord pitching control. Through a decomposition of the lift, we attribute this advantage to the dominant added-mass contribution accessible via quarter-chord pitching. The success on multiple configurations shows the generalizability of the proposed transformer-based RL framework, which offers a promising approach to solve more computationally demanding flow control problems when combined with the proposed acceleration techniques.