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

2026-03-06

AI Summary

This work addresses the limitations of conventional control allocation methods, which neglect the asymmetric actuator dynamics of omnidirectional drones, leading to motor command oscillations and degraded trajectory tracking during aggressive maneuvers. To overcome this, the paper proposes a receding-horizon control allocation strategy that, for the first time, integrates constrained iterative Linear Quadratic Regulator (Constrained iLQR) with null-space optimization. This approach explicitly models actuator asymmetry and leverages actuation redundancy to simultaneously satisfy the required generalized body forces precisely while smoothing the future sequence of motor commands. Simulation results demonstrate that, compared to traditional single-step quadratic programming allocators, the proposed method significantly suppresses command oscillations and enhances both position and attitude tracking accuracy.

Technology Category

• Intelligent Robots: State Estimation
• Search and Optimization: Mixed Discrete/Continuous Search
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• control allocation
• asymmetric actuator dynamics
• omnidirectional UAVs
• motor command oscillations
• trajectory tracking

Innovation

• Receding-horizon optimization
• Nullspace optimization
• Actuation-aware control
• Over-actuated UAVs
• Constrained iLQR