Receding-Horizon Nullspace Optimization for Omnidirectional UAVs

Abstract

Fully actuated omnidirectional UAVs enable independent control of forces and torques along all six degrees of freedom, broadening the operational envelope for agile flight and aerial interaction tasks. However, conventional control allocation methods neglect the asymmetric dynamics of the onboard actuators, which can induce oscillatory motor commands and degrade trajectory tracking during dynamic maneuvers.

This work proposes a receding-horizon, actuation-aware allocation strategy that explicitly incorporates asymmetric motor dynamics and exploits the redundancy of over-actuated platforms through nullspace optimization. By forward-simulating the closed-loop system over a prediction horizon, the method anticipates actuator-induced oscillations and suppresses them through smooth redistribution of motor commands, while preserving the desired body wrench exactly.

The approach is formulated as a constrained optimal control problem solved online via Constrained iterative LQR. Simulation results on the OmniOcta platform demonstrate that the proposed method significantly reduces motor command oscillations compared to a conventional single-step quadratic programming allocator, yielding improved trajectory tracking in both position and orientation.

Key Highlights

Actuation-Aware

Explicitly incorporates asymmetric motor dynamics into the control allocation, anticipating actuator-induced oscillations.

Nullspace Optimization

Exploits the redundancy of over-actuated platforms through nullspace optimization to smooth motor commands while preserving the desired wrench.

Receding-Horizon

Forward-simulates the closed-loop system over a prediction horizon, solved online via Constrained iterative LQR.

Methodology

Our approach formulates the actuation-aware control allocation as a constrained optimal control problem. The key insight is to exploit the nullspace of the allocation matrix to redistribute motor commands in a way that accounts for asymmetric motor dynamics—motors spin up faster than they spin down—over a receding prediction horizon. This enables the allocator to anticipate and suppress oscillatory commands before they degrade tracking performance.

Overview of the proposed receding-horizon nullspace optimization method.

Overview of the proposed receding-horizon nullspace optimization for actuation-aware control allocation.

Experimental results

Baseline method shows an oscillatory behaviour in motor commands due to neglection of motor asymmetric dynamics

Our horizon-based method anticipate actuator induced oscillations, resulting in a smoother motor commands profile and better trajectory tracking performances

Video

BibTeX

@article{pretto2025rhno,
  author    = {Pretto, Riccardo and Hamandi, Mahmoud and Ali, Abdullah Mohamed and Alcan, Gokhan and Tzes, Anthony and Abu-Dakka, Fares},
  title     = {Receding-Horizon Nullspace Optimization for Actuation-Aware Control Allocation in Omnidirectional UAVs},
  journal   = {arXiv preprint arXiv:25XX.XXXXX},
  year      = {2026},
}