Taylor-Informed Predictive Cost Adaptive Control for Quadrotors with Online Gravity-Trim Adaptation

What the math gives to ML

The paper contributes an adaptive predictive-control mechanism based on sparse first-, second-, and third-order Taylor dictionaries, row-wise recursive least-squares identification, variable-rate forgetting, and a Jacobian held fixed over each prediction horizon. Its transferable asset is a cheap local model that adapts rapidly after payload, actuator, or environment changes while preserving a simple finite-horizon predictor. A strong neural-network transfer is to combine a global neural world model with an online Taylor residual model for model-based reinforcement learning or MPC. A second transfer is to identify persistent force or acceleration biases in a separate adaptive trim channel rather than forcing the neural model to relearn them.