Dynamic Control Allocation for Dual-Tilt UAV Platforms

Marcello Sorge; Federico Ciresola; Giulia Michieletto; Angelo Cenedese

cs.ROApr 7, 2026

Dynamic Control Allocation for Dual-Tilt UAV Platforms

Authors: Marcello Sorge, Federico Ciresola, Giulia Michieletto, Angelo Cenedese

Organizations: Department of Information Engineering University of Padova Padova, Italy · Department of Management and Engineering, University of Padova Padova, Italy · Department of Industrial Engineering, University of Padova, Padova, Italy

Abstract

This paper focuses on dynamic control allocation for a hexarotor UAV platform, considering a trajectory tracking task as as case study. It is assumed that the platform is dual-tilting, meaning that it is able to tilt each propeller independently during flight, along two orthogonal axis. We present a hierarchical control structure composed of a high-level controller generating the required wrench for the tracking task, and a control allocation law ensuring that the actuators produce such wrench. The allocator imposes desired first-order dynamics on the actuators set, and exploits system redundancy to optimize the actuators state with respect to a given objective function. Unlike other studies on the subject, we explicitly model actuator saturation and provide theoretical insights on its effect on control performances. We also investigate the role of propeller tilt angles, by imposing asymmetric shapes in the objective function. Numerical simulations are presented to validate the allocation strategy.

Figures & tables

Panel of Figure 1 — Fig. 1: Hierarchical control architecture showing the high-level controller and the low-level allocator

Fig. 7: Propeller spinning rates

Propellers	Amplitude $[^{\circ}]$	Amplitude $[^{\circ}]$
	Offset $[^{\circ}]$	Offset $[^{\circ}]$
	$\gamma_{j}=0$	$\gamma_{j}=10$
$[\alpha_{1},\alpha_{4}]$	$[7.5115,-7.5115]$	$[7.2708,-7.2708]$
$[\alpha_{1},\alpha_{4}]$	$[6.7093,-6.7093]$	$[0,0]$
$[\alpha_{2},\alpha_{5}]$	$[7.5229,-7.5229]$	$[7.413,-7.413]$
$[\alpha_{2},\alpha_{5}]$	$[-6.2338,6.2338]$	$[0,0]$

TABLE I: Propeller tilt angles amplitudes and offsets

Propellers	Amplitude $[^{\circ}]$	Amplitude $[^{\circ}]$
Propellers	$J_{\alpha}$	$J_{\beta}$
$[\alpha_{1},\alpha_{4}]$	$[5.4786,-5.4786]$	$[9.3793,-9.3793]$
$[\alpha_{2},\alpha_{5}]$	$[5.4786,-5.4786]$	$[9.385,-9.385]$
$[\alpha_{3},\alpha_{6}]$	$[5.4826,-5.4826]$	$[9.385,-9.385]$
$[\beta_{1},\beta_{4}]$	$[9.385,-9.385]$	$[5.4987,-5.4987]$
$[\beta_{2},\beta_{5}]$	$[-9.385,9.385]$	$[-5.4981,5.4987]$

TABLE II: Propeller tilt angles amplitudes

Propellers	Amplitude $[\frac{rad}{s}]$	Amplitude $[\frac{rad}{s}]$
	Offset $[\frac{rad}{s}]$	Offset $[\frac{rad}{s}]$
	$J_{\alpha}$	$J_{\beta}$
$[\omega_{1},\omega_{4}]$	$[0.0451,0.0451]$	$[0.0455,0.0455]$
$[\omega_{1},\omega_{4}]$	$[621.1694,-621.1694]$	$[621.3834,-621.3834]$
$[\omega_{2},\omega_{5}]$	$[-0.0454,0.0454]$	$[0.0434,-0.0435]$
$[\omega_{2},\omega_{5}]$	$[-621.1664,621.1664]$	$[-621.387,621.3872]$

TABLE III: Propeller spinning rates amplitudes and offsets

Aerial Robotics

Aerial robotics studies the design, perception, control, and coordination of flying robots for navigation, inspection, transport, and physical interaction.

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

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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.

Abstract

Aerial Robotics

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