Dynamic Control Allocation for Dual-Tilt UAV Platforms

• URL: http://arxiv.org/abs/2604.05677v1
• Date: Tue, 07 Apr 2026 10:27:47 GMT
• Title: Dynamic Control Allocation for Dual-Tilt UAV Platforms
• Abstract summary: This paper focuses on dynamic control allocation for a hexarotor UAV platform.<n>It is assumed that the platform is dual-tilting, meaning that it is able to tilt each propeller independently during flight, along two axis.<n>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.
• Score: 0.6999740786886536
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• QuadRocket: An Aerial Robotic Testbed for Adaptive Thrust-Vector Control of Rocket-Like Vehicles [0.8633013637160062]
  QuadRocket is a low-cost, low-risk platform for validating thrust-vector control strategies for launch vehicle-type systems.<n>Prototype consists of a cylindrical main body mounted on top of a quadrotor through a universal joint, forming a flying inverted pendulum with non-negligible inertia.<n>Control design is modeled as a single axisymmetric rigid body actuated by a vectored force applied along its longitudinal axis.
  arXiv  Detail & Related papers  (2026-07-02T17:43:09Z)
• From Articulated Kinematics to Routed Visual Control for Action-Conditioned Surgical Video Generation [43.4347713269043]
  Action-conditioned surgical video generation is a critical yet highly challenging problem for robotic surgery.<n>We propose a kinematic-to-visual lifting paradigm that converts articulated kinematics into a unified set of five image-aligned control modalities.<n>Our method consistently improves action faithfulness, visual fidelity, and cross-domain generalization over diverse baselines.
  arXiv  Detail & Related papers  (2026-05-09T05:48:51Z)
• Bi-Level Reinforcement Learning Control for an Underactuated Blimp via Center-of-Mass Reconfiguration [22.07080402182441]
  This paper focuses on a compact architecture consisting of two thrusters and a movable internal slider.<n>The proposed method consistently outperforms fixed-CoM baselines and PID-based controllers.
  arXiv  Detail & Related papers  (2026-05-02T06:55:26Z)
• What Drives Representation Steering? A Mechanistic Case Study on Steering Refusal [53.189667624047416]
  We show that different steering methodologies leverage functionally interchangeable circuits when applied at the same layer.<n>We show that steering vectors can be sparsified by up to 90-99% while retaining most performance.
  arXiv  Detail & Related papers  (2026-04-09T17:57:14Z)
• How to Model Your Crazyflie Brushless [50.54424040768484]
  In early 2025, the Crazyflie Brushless was introduced, featuring brushless motors that provide around 50% more thrust.<n>This work presents a dynamics model of the Crazyflie Brushless and identifies its key parameters.<n>We demonstrate its suitability for reinforcement learning applications by training an end-to-end neural network position controller and learning a backflip controller.
  arXiv  Detail & Related papers  (2026-03-06T06:21:39Z)
• Position: Capability Control Should be a Separate Goal From Alignment [43.64120192267]
  This paper argues that capability control should be treated as a distinct goal from alignment.<n>We organize capability control mechanisms across the model lifecycle into three layers.<n>Key challenges include the dual-use nature of knowledge and compositional generalization.
  arXiv  Detail & Related papers  (2026-02-05T00:30:59Z)
• CRoSS: A Continual Robotic Simulation Suite for Scalable Reinforcement Learning with High Task Diversity and Realistic Physics Simulation [46.950823663585425]
  Continual reinforcement learning requires agents to learn from a sequence of tasks without forgetting previously acquired policies.<n>We introduce a novel benchmark suite for CRL based on realistically simulated robots in the Gazebo simulator.
  arXiv  Detail & Related papers  (2026-02-04T18:54:26Z)
• Path Tracking with Dynamic Control Point Blending for Autonomous Vehicles: An Experimental Study [40.36217526625217]
  This paper presents a path-tracking framework for autonomous vehicles in which the lateral control command is applied to a dynamic control point along the wheelbase.<n>Instead of enforcing a fixed reference at either the front or rear axle, the proposed method continuously interpolates between both.<n>The results in closed-loop tracking and backward maneuvers show improved trajectory accuracy, smoother steering profiles, and increased adaptability compared to fixed control-point baselines.
  arXiv  Detail & Related papers  (2026-02-02T10:03:37Z)
• Control of a Twin Rotor using Twin Delayed Deep Deterministic Policy Gradient (TD3) [0.0]
  This paper proposes a reinforcement learning framework for stabilizing the Twin Rotor Aerodynamic System (TRAS)<n>The complex dynamics and non-linear characteristics of the TRAS make it challenging to control using traditional control algorithms.<n>Experiments on a laboratory setup were carried out to confirm the controller's effectiveness in real-world applications.
  arXiv  Detail & Related papers  (2025-12-15T14:10:04Z)
• SP-VLA: A Joint Model Scheduling and Token Pruning Approach for VLA Model Acceleration [70.72227437717467]
  Vision-Language-Action (VLA) models have attracted increasing attention for their strong control capabilities.<n>Their high computational cost and low execution frequency hinder their suitability for real-time tasks such as robotic manipulation and autonomous navigation.<n>We propose SP-VLA, a unified framework that accelerates VLA models by jointly scheduling models and pruning tokens.
  arXiv  Detail & Related papers  (2025-06-15T05:04:17Z)
• Modelling, Positioning, and Deep Reinforcement Learning Path Tracking Control of Scaled Robotic Vehicles: Design and Experimental Validation [3.807917169053206]
  Scaled robotic cars are commonly equipped with a hierarchical control acthiecture that includes tasks dedicated to vehicle state estimation and control. This paper covers both aspects by proposing (i) a federeted extended Kalman filter (FEKF) and (ii) a novel deep reinforcement learning (DRL) path tracking controller trained via an expert demonstrator. The experimentally validated model is used for (i) supporting the design of the FEKF and (ii) serving as a digital twin for training the proposed DRL-based path tracking algorithm.
  arXiv  Detail & Related papers  (2024-01-10T14:40:53Z)
• A Tricycle Model to Accurately Control an Autonomous Racecar with Locked Differential [71.53284767149685]
  We present a novel formulation to model the effects of a locked differential on the lateral dynamics of an autonomous open-wheel racecar. We include a micro-steps discretization approach to accurately linearize the dynamics and produce a prediction suitable for real-time implementation.
  arXiv  Detail & Related papers  (2023-12-22T16:29:55Z)
• Actuator Trajectory Planning for UAVs with Overhead Manipulator using Reinforcement Learning [0.3222802562733786]
  We develop a UAV equipped with a controllable arm with two degrees of freedom to carry out actuation tasks on the fly. Our solution is based on employing a Q-learning method to control the trajectory of the tip of the arm, also called end-effector. Our method achieves 92% accuracy in terms of average displacement error using Q-learning with 15,000 episodes.
  arXiv  Detail & Related papers  (2023-08-24T15:06:23Z)
• Optimal State Manipulation for a Two-Qubit System Driven by Coherent and Incoherent Controls [77.34726150561087]
  State preparation is important for optimal control of two-qubit quantum systems. We exploit two physically different coherent control and optimize the Hilbert-Schmidt target density matrices.
  arXiv  Detail & Related papers  (2023-04-03T10:22:35Z)
• Designing a Robust Low-Level Agnostic Controller for a Quadrotor with Actor-Critic Reinforcement Learning [0.38073142980732994]
  We introduce domain randomization during the training phase of a low-level waypoint guidance controller based on Soft Actor-Critic. We show that, by introducing a certain degree of uncertainty in quadrotor dynamics during training, we can obtain a controller that is capable to perform the proposed task using a larger variation of quadrotor parameters.
  arXiv  Detail & Related papers  (2022-10-06T14:58:19Z)
• Control-Aware Prediction Objectives for Autonomous Driving [78.19515972466063]
  We present control-aware prediction objectives (CAPOs) to evaluate the downstream effect of predictions on control without requiring the planner be differentiable. We propose two types of importance weights that weight the predictive likelihood: one using an attention model between agents, and another based on control variation when exchanging predicted trajectories for ground truth trajectories.
  arXiv  Detail & Related papers  (2022-04-28T07:37:21Z)
• Towards Agrobots: Trajectory Control of an Autonomous Tractor Using Type-2 Fuzzy Logic Controllers [12.015055060690742]
  In this study, a proportional-integral-derivative controller is used to control the longitudinal velocity of the tractor. For the control of the yaw angle dynamics, a proportional-derivative controller works in parallel with a type-2 fuzzy neural network. We develop a control algorithm which learns the interactions online from the measured feedback error.
  arXiv  Detail & Related papers  (2021-04-09T00:46:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.