Receding-Horizon Nullspace Optimization for Actuation-Aware Control Allocation in Omnidirectional UAVs
Riccardo Pretto, Mahmoud Hamandi, Abdullah Mohamed Ali, Gokhan Alcan, Anthony Tzes, Fares Abu-Dakka
International Conference on Unmanned Aircraft Systems, ICUAS 2026
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.
Highlights of the paper:
- 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.
[Project Website] – [Pre-print]
