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.
Figures & tables
Fig. 1: Conceptual overview of the proposed approach. The baseline allocator (MBNO) solves a single-step QP that is agnostic to actuator dynamics, resulting in chattering motor commands. The proposed method embeds the asymmetric motor dynamics into a receding-horizon iLQR framework, producing smooth, predictive actuator commands that anticipate and suppress oscillatory behavior.
Fig. 2: Closed-loop control architecture of the OmniOcta. The trajectory generator provides reference states and their derivatives to the controller, which computes a desired six-dimensional wrench ξ∗ . The allocator maps this wrench to individual motor commands ucmd∈R8 . Onboard sensors close the loop by providing state estimates back to the controller.
Fig. 3: Architecture of the proposed receding-horizon nullspace optimization. The lower loop represents the real-time control pipeline, where the optimal allocation policy maps the desired wrench to motor commands ucmd∈R8 . The upper block depicts the CiLQR solver, which internally forward-simulates the controller, the rigid-body dynamics, and the asymmetric first-order motor model over a short prediction horizon. By optimizing the nullspace allocation variables X∗ subject to motor limits [umin,umax] , the solver produces smooth actuator commands that are fed back to the allocation policy at each receding-horizon cycle.
Fig. 4: Reference trajectory: the OmniOcta translates from (0,0,3)m to (1,1,2)m over 60 s while executing a full rotation about the y-axis.
Fig. 5: Actual motor commands uact under the baseline MBNO allocator. Oscillatory behavior is clearly visible between t=30s and t=37s .
Fig. 6: Actual motor commands uact over the full trajectory obtained with the proposed method. The oscillations present in the MBNO baseline (Fig. 5 ) are effectively suppressed.
Fig. 7: Position ( x , y , z ) and orientation ( ξx , ξy , ξz ) tracking errors. The proposed method exhibits consistently lower error magnitudes compared to the MBNO baseline.
Fig. 8: Comparison of actuator input variations Δu per motor between the MBNO baseline and the proposed method. The proposed approach yields substantially smoother command profiles with reduced abrupt input changes.
Our Method
MBNO Method
Mean
pos [m]
0.0038
0.0095
ori [rad]
0.1027
0.1070
RMS
pos [m]
0.0046
0.0146
ori [rad]
0.5575
0.5700
TABLE I: Mean and RMS tracking errors in position and orientation for the proposed method and the MBNO baseline.
Aerial robotics studies the design, perception, control, and coordination of flying robots for navigation, inspection, transport, and physical interaction.
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.
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
Allocation schemes that greedily maximize a readiness metric over the actuator fiber bundle of an overactuated multirotor produce commands that jump between disconnected optimal strata, demanding actuator rates no motor can deliver; effort-minimizing schemes are continuous but cannot guarantee that wrench-rate authority stays above any certified level. We reconcile the two by treating authority as a forward-invariant quantity: a control barrier function on the log-determinant of the drag-aware actuator-authority co-metric, enforced at torque level by a quadratic program in the allocation null space. A single design inequality renders the certified set compact and strictly interior to the actuator box, with the readiness cost of any rotor deactivation given in closed form as ln(n/(n−m)) for symmetric designs. Tracking is sacrificed only through an explicit alignment ratio, with wrench error bounded by O(ρ−1/2) and a robust variant handles motor-parameter uncertainty with a closed-form floor shift independent of the airframe matrix. On a hexarotor and a fully-actuated octorotor the closed-form gap matches simulation to machine precision; in the authority-scarce regime greedy maximization violates the certified floor and commits wrench errors up to eighty times larger than the proposed filter, which holds invariance of the certified set at negligible tracking cost.
Full actuation is commonly valued for independent position and attitude tracking. This work establishes a complementary principle: when only a position trajectory is prescribed, minimizing a positively homogeneous allocation cost selects underactuated-like behavior on a fully actuated multirotor. This cost class includes norms and power laws, including standard minimum-norm pseudoinverse allocation. We use fiberwise infimization to transport the actuator-output cost to wrench space, yielding a homogeneous infimal allocation cost. Restriction to unit zero-moment forces defines a directional landscape whose minimizing directions are independent of force magnitude. We show that aligning any fixed minimizing direction with the required world-frame force minimizes instantaneous and integrated zero-moment allocation costs. The resulting body force remains in a fixed one-dimensional subspace without transverse components. We derive an exact optimality-gap decomposition quantifying the additional cost of every other attitude trajectory and characterizing the minimum-cost class. When a disturbance or prescribed-motion change modifies the required-force direction, a fully actuated platform can immediately generate the modified force using transverse authority and reorient toward the new minimum-cost family. A structurally underactuated platform must reorient before generating that force. Thus, homogeneous allocation-cost minimization explains why optimal nominal motion can be underactuated-like, while full actuation remains valuable for disturbance rejection and transient force generation.
Antonio Franchi, Barbara Bazzana
Robotics and Mechatronics, Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, Enschede, The Netherlands. · Department of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, Italy.