Article

Dual Gradient Consensus (DGC): An Adaptive Learning Method for Transient Out-of-Distribution Disturbances in Quadrotor Control

To address the problem that short-term out-of-distribution residuals can corrupt the learned disturbance model during quadrotor trajectory tracking, DGC improves the online update step size while retaining the random Fourier feature model, the offline BPTT policy, and least-squares adaptation (DAPL). It distinguishes adaptation states by comparing long- and short-term gradient memories and uses a confidence map to suppress unreliable updates.

Part of the event: Dual-Gradient Consensus Improves Adaptive Control of Quadrotors

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