Disturbance-Aware Flight Control of Robotic Gliding Blimp via Moving Mass Actuation

• URL: http://arxiv.org/abs/2601.21188v1
• Date: Thu, 29 Jan 2026 02:36:26 GMT
• Title: Disturbance-Aware Flight Control of Robotic Gliding Blimp via Moving Mass Actuation
• Abstract summary: Robotic blimps, as lighter-than-air (LTA) aerial systems, offer long endurance and inherently safe operation but remain highly susceptible to wind disturbances.<n>This paper addresses the lack of disturbance-aware control frameworks for LTA platforms by explicitly modeling and compensating for wind-induced effects.<n>A moving horizon estimator (MHE) infers real-time wind perturbations and provides these estimates to a model predictive controller (MPC), enabling robust trajectory and heading regulation under varying wind conditions.
• Score: 5.073162687197271
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Robotic blimps, as lighter-than-air (LTA) aerial systems, offer long endurance and inherently safe operation but remain highly susceptible to wind disturbances. Building on recent advances in moving mass actuation, this paper addresses the lack of disturbance-aware control frameworks for LTA platforms by explicitly modeling and compensating for wind-induced effects. A moving horizon estimator (MHE) infers real-time wind perturbations and provides these estimates to a model predictive controller (MPC), enabling robust trajectory and heading regulation under varying wind conditions. The proposed approach leverages a two-degree-of-freedom (2-DoF) moving-mass mechanism to generate both inertial and aerodynamic moments for attitude and heading control, thereby enhancing flight stability in disturbance-prone environments. Extensive flight experiments under headwind and crosswind conditions show that the integrated MHE-MPC framework significantly outperforms baseline PID control, demonstrating its effectiveness for disturbance-aware LTA flight.

Related papers

• DESCENT: Directed Edge Scene Encoding for Airport Surface Movement Prediction [54.94692733670454]
  DESCENT is a transformer-based architecture designed to handle heterogeneous dynamics and strict topological constraints.<n>Our approach features a Potential Reachable Set (PRS) context sampling mechanism that adaptively collects airfield environment context.<n>Our evaluations on the Amelia-10 benchmark demonstrate significant performance improvements over state-of-the-art baselines.
  arXiv  Detail & Related papers  (2026-08-26T16:49:50Z)
• Nonlinear Model Predictive Control for Trajectory Tracking of Differentially Flat Fixed-Wing Aerial Systems [6.655837620295535]
  Planning and control of fixed-wing Unmanned Aerial Vehicles (UAVs) are challenging due to nonlinear dynamics, aerodynamic limits, and environmental disturbances.<n>We propose a unified framework that integrates differential flatness-based trajectory generation with predictive, constraint-aware control.<n>We validate the proposed framework through extensive simulations and real-world flight experiments, demonstrating improved tracking accuracy and robustness for complex trajectories.
  arXiv  Detail & Related papers  (2026-08-21T01:25:35Z)
• Wind-Informed Rapid Flight-Planning in Complex Urban Topologies via Machine Learning and Experimental Validation [46.65676221937358]
  This work proposes a novel framework towards safe flight planning of aerial vehicles in windy urban environments.<n>A learning-based surrogate model is trained to rapidly predict flow fields from readily available information.<n>A safe, flow-informed flight trajectory is then identified through a cost-minimizing pathfinder.
  arXiv  Detail & Related papers  (2026-08-10T23:30:49Z)
• End-to-end Conditional Diffusion for Realistic and Controllable Visual Traffic Scenario Generation [59.188006170485046]
  E2E-CDiff is an end-to-end conditional diffusion framework for controllable and realistic scenario generation.<n>Differentiable guidance regulates speed, enforces drivable-area compliance, and supports collision-avoidance or collision-seeking behaviors.<n>E2E-CDiff also performs competitively as a learning-based ego planner.
  arXiv  Detail & Related papers  (2026-07-21T02:13:25Z)
• Disturbance-Aware Flight for Aerial Robots in Narrow Space [18.124392684552998]
  This paper presents a disturbance-aware planning and control framework that integrates online disturbance estimation into the planning-control loop for quadrotor flight in narrow space.<n> Experiments demonstrate that a quadrotor with a diagonal length of 0.39m can traverse straight, sloped, and curved tunnels as narrow as 0.6m, outperforming human pilots in both success rate and flight efficiency.
  arXiv  Detail & Related papers  (2026-07-20T01:58:05Z)
• Aero-World: Action-Conditioned Aerial Video Generation from Inertial Controls [57.166020875486474]
  We propose textbfAero-World, a method for converting a pretrained image-to-video diffusion model into a controllable aerial video generator.<n>A frozen latent-space Physics Probe, trained independently on real video--IMU pairs, provides differentiable inertial-consistency supervision during LoRA finetuning.<n>On AeroBench, Aero-World improves mean AAS from 57.7 to 63.6 over action-only finetuning and gives a stronger quality-control trade-off than AirScape.
  arXiv  Detail & Related papers  (2026-05-19T12:02:17Z)
• Wind-Aware Optimal Trajectory Planning for Efficient Gliding of Fixed-Wing Aerial Systems [7.0414059106180025]
  Gliding offers small fixed-wing UAVs extended endurance and silent operation.<n>Traditional Total Energy Control Systems based controllers regulate the trade between potential and kinetic energy reactively.<n>We present a nonlinear, multi-cost trajectory planner for small UAV gliders.
  arXiv  Detail & Related papers  (2026-05-15T05:01:09Z)
• WESPR: Wind-adaptive Energy-Efficient Safe Perception & Planning for Robust Flight with Quadrotors [9.15147164003286]
  We present WESPR, a fast framework that predicts how environmental geometry affects local wind conditions.<n>Our results show a 12.5-58.7% reduction in maximum trajectory deviation and a 24.6% improvement in stability compared to a wind-agnostic adaptive controller.
  arXiv  Detail & Related papers  (2026-03-10T05:09:43Z)
• Physics-infused Learning for Aerial Manipulator in Winds and Near-Wall Environments [3.895172208362693]
  Aerial manipulation (AM) expands UAV capabilities beyond passive observation to contact-based operations at high altitudes and in otherwise inaccessible environments.<n>This paper integrates a physics-based blade-element model with a learning-based residual force estimator, along with a rotor-speed allocation strategy for disturbance compensation, resulting in a unified control framework.<n>Results demonstrate improved disturbance estimation and trajectory-tracking accuracy over conventional approaches, enabling robust wall-contact execution under challenging aerodynamic conditions.
  arXiv  Detail & Related papers  (2026-03-08T22:08:15Z)
• A 26-Gram Butterfly-Inspired Robot Achieving Autonomous Tailless Flight [61.21669716392821]
  textitAirPulse is a 26-gram butterfly-inspired robot that achieves the first onboard, closed-loop controlled flight for a tailless two-winged platform at this scale.<n>It replicates key biomechanical traits of butterfly flight, utilizing low-aspect-ratio, compliant carbon-fiber-reinforced wings and low-frequency flapping that reproduces characteristic biological body undulations.
  arXiv  Detail & Related papers  (2026-02-06T15:58:50Z)
• Forecasting Fails: Unveiling Evasion Attacks in Weather Prediction Models [60.728124907335]
  This work introduces Weather Adaptive Adversarial Perturbation Optimization (WAAPO), a novel framework for generating targeted adversarial perturbations.<n>WAAPO achieves this by incorporating constraints for channel sparsity, spatial localization, and smoothness, ensuring that perturbations remain physically realistic and imperceptible.<n>Our experiments highlight critical vulnerabilities in AI-driven forecasting models, where small perturbations to initial conditions can result in significant deviations.
  arXiv  Detail & Related papers  (2025-12-09T17:20:56Z)
• Robust Optimization-based Autonomous Dynamic Soaring with a Fixed-Wing UAV [15.033246214180258]
  We propose a framework for autonomous dynamic soaring with a fixed-wing unmanned aerial vehicle.<n>The framework makes use of an explicit representation of the wind field and a classical approach for guidance and control of the UAV.
  arXiv  Detail & Related papers  (2025-12-07T01:02:46Z)
• ASTREA: Introducing Agentic Intelligence for Orbital Thermal Autonomy [51.56484100374058]
  ASTREA is the first agentic system executed on flight-heritage hardware for autonomous spacecraft operations.<n>We integrate a resource-constrained Large Language Model (LLM) agent with a reinforcement learning controller in an asynchronous architecture tailored for space-qualified platforms.
  arXiv  Detail & Related papers  (2025-09-16T08:52:13Z)
• Transformer-Based Fault-Tolerant Control for Fixed-Wing UAVs Using Knowledge Distillation and In-Context Adaptation [3.1498833540989413]
  This study presents a transformer-based approach for fault-tolerant control in fixed-wing Unmanned Aerial Vehicles (UAVs)<n>Our method directly maps outer-loop reference values into control commands using the in-student learning and attention mechanisms of transformers.<n> Experimental results demonstrate that our transformer-based controller outperforms industry-standard FCS and state-of-the-art reinforcement learning (RL) methods.
  arXiv  Detail & Related papers  (2024-11-05T10:24:45Z)
• Interpretable Stochastic Model Predictive Control using Distributional Reinforced Estimation for Quadrotor Tracking Systems [0.8411385346896411]
  We present a novel trajectory tracker for autonomous quadrotor navigation in dynamic and complex environments. The proposed framework integrates a distributional Reinforcement Learning estimator for unknown aerodynamic effects into a Model Predictive Controller. We demonstrate our system to improve the cumulative tracking errors by at least 66% with unknown and diverse aerodynamic forces.
  arXiv  Detail & Related papers  (2022-05-14T23:27:38Z)
• Neural-Fly Enables Rapid Learning for Agile Flight in Strong Winds [96.74836678572582]
  We present a learning-based approach that allows rapid online adaptation by incorporating pretrained representations through deep learning. Neural-Fly achieves precise flight control with substantially smaller tracking error than state-of-the-art nonlinear and adaptive controllers.
  arXiv  Detail & Related papers  (2022-05-13T21:55:28Z)
• Wireless-Enabled Asynchronous Federated Fourier Neural Network for Turbulence Prediction in Urban Air Mobility (UAM) [101.80862265018033]
  Urban air mobility (UAM) has been proposed in which vertical takeoff and landing (VTOL) aircraft are used to provide a ride-hailing service. In UAM, aircraft can operate in designated air spaces known as corridors, that link the aerodromes. A reliable communication network between GBSs and aircraft enables UAM to adequately utilize the airspace.
  arXiv  Detail & Related papers  (2021-12-26T14:41:52Z)

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.