Conditional Invertible Neural Networks for Data-Driven UAV Control: A 2-D Proof of Concept

2026-07-15

AI Summary

This study addresses the limited generalization of inverse dynamics models and inadequate uncertainty quantification in data-driven control of multirotor unmanned aerial vehicles. To this end, it introduces conditional invertible neural networks (CINNs) into this domain for the first time, leveraging incremental nonlinear dynamic inversion (INDI) as a teacher policy for supervised training. The proposed architecture employs rational quadratic spline coupling layers combined with invertible linear mixing to explicitly learn the probabilistic distribution of control inputs, thereby effectively capturing model uncertainty and revealing the critical influence of data coverage and command bandwidth on control failure. Experimental results on an X8 coaxial multirotor demonstrate an open-loop reproduction R² of 0.944 and a continuous ranked probability score (CRPS) of 0.0915; in closed-loop tests across 15 scenarios, the method achieves a position RMSE of 9.7 m and a tracking success rate of 47%, matching INDI’s performance while successfully identifying two dominant failure modes.

Abstract

We investigate conditional invertible neural networks (cINNs) as probabilistic inverse-dynamics models for multirotor control. For a planar X8 coaxial multicopter, we learn $p(u \mid s_t, c_t)$ from an incremental nonlinear dynamic inversion (INDI) teacher using rational-quadratic spline coupling and invertible linear mixing. Open-loop reproduction reaches $R^2 = 0.944$, mean CRPS 0.0915, and log-probability-error correlation $ρ= -0.60$. Over 15 closed-loop scenarios, position RMSE matches INDI (9.7 vs. 9.5 m), with 47 percent tracking acceptably; failures separate into attitude divergence under aggressive steps and phase lag under high-frequency references, isolating command bandwidth and data coverage as dominant failure mechanisms.

Problem

• UAV control
• inverse dynamics
• data-driven modeling
• control failure
• command bandwidth

Innovation

• conditional invertible neural networks
• probabilistic inverse dynamics
• data-driven UAV control
• normalizing flows
• incremental nonlinear dynamic inversion