TDA-ACT: Temporal-Derivative-Encoded Adaptive Action Chunking with Transformer for Flight Maneuver Generation

Traditional imitation learning is prone to distribution shift and trajectory divergence in highly dynamic, strongly time-varying flight tasks. To address this, we propose a temporally adaptive action-chunking framework built on temporal-derivative encoding, termed Temporal-Derivative-Encoded Adaptive Action Chunking with Transformer (TDA-ACT). First, using state temporal-derivative features as the core representation, we construct a confidence-estimation mechanism that also incorporates the latent-variable variance of the maneuver-mode representation and the action-prediction variance produced by the decoder. Second, we develop a confidence-guided adaptive temporal-ensembling strategy that uses this confidence metric to jointly adjust the fusion scope and the fusion weights of historical predictions, enabling a dynamic trade-off between long-horizon smoothing under stable conditions and high-frequency responsiveness during aggressive maneuvers. On both the Loop and AileronRoll maneuvers in JSBSim, TDA-ACT reduces action jerk and suppresses trajectory divergence relative to ACT and other imitation-learning baselines.