Receding-Horizon Nullspace Optimization for Actuation-Aware Control Allocation in Omnidirectional UAVs

TL;DR

This paper introduces a receding-horizon, actuation-aware control allocation method for omnidirectional UAVs that anticipates and suppresses actuator oscillations, improving trajectory tracking by explicitly considering asymmetric motor dynamics.

Contribution

It presents a novel receding-horizon control allocation strategy that incorporates actuator dynamics and nullspace optimization, advancing over traditional methods.

Findings

Reduces motor command oscillations significantly.

Enhances trajectory tracking accuracy.

Demonstrates effectiveness on OmniOcta platform.

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.