Transition control of a tail-sitter UAV using recurrent neural networks

• URL: http://arxiv.org/abs/2006.16401v1
• Date: Mon, 29 Jun 2020 21:33:30 GMT
• Title: Transition control of a tail-sitter UAV using recurrent neural networks
• Abstract summary: The control strategy is based on attitude and velocity stabilization. The RNN is used for the estimation of high nonlinear aerodynamic terms. Results show convergence of linear velocities and the pitch angle during the transition maneuver.
• Score: 80.91076033926224
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents the implementation of a Recurrent Neural Network (RNN) based-controller for the stabilization of the flight transition maneuver (hover-cruise and vice versa) of a tail-sitter UAV. The control strategy is based on attitude and velocity stabilization. For that aim, the RNN is used for the estimation of high nonlinear aerodynamic terms during the transition stage. Then, this estimate is used together with a feedback linearization technique for stabilizing the entire system. Results show convergence of linear velocities and the pitch angle during the transition maneuver. To analyze the performance of our proposed control strategy, we present simulations for the transition from hover to cruise and vice versa.

Related papers

• A Neural Network Based Teleoperation for Remote Controlled Vehicles [98.38733381483934]
  Direct teleoperation of vehicles faces critical technical bottlenecks.<n> communication latency and the operator's inability to physically perceive unmodeled environmental disturbances.<n>We propose a tailored unilateral teleoperation framework.
  arXiv  Detail & Related papers  (2026-08-11T01:51:08Z)
• Coordinated Incremental Trajectory Tracking of a Tailsitter Drone [0.42970700836450487]
  This paper derives an analytical differential flatness transform for a tailsitter Unmanned Aerial Vehicle (UAV) under coordinated flight conditions using a simplified aerodynamic model.<n>The proposed framework is formulated exclusively using rotation matrices, avoiding the ambiguities inherent to Euler angle representations.
  arXiv  Detail & Related papers  (2026-07-13T15:03:44Z)
• Machine Learning-based Feedback Linearization Control of Quadrotor Subject to Unmodeled Dynamics [0.0]
  This work introduces a novel machine learning-based feedback-linearization control framework.<n>An online adaptation of the RBF NN updates the network's weights without prior training.<n>The proposed controller improves trajectory tracking with rapid convergence and reduction of position-norm and yaw orientation RMSE by more than $7.13%$ and $49.27%$ respectively.
  arXiv  Detail & Related papers  (2026-06-30T06:30:19Z)
• Constant Time-Delay Leader Following with Neural Networks and Invariant Extended Kalman Filters for Arbitrary Trajectories [0.9176056742068813]
  This paper proposes a constant time-delay trajectory tracking method for vehicle convoys operating without inter-vehicle communication.<n>The method integrates a probabilistic sequence-to-sequence (Seq2Seq) neural network with an invariant extended Kalman filter (IEKF) to warm-start the prediction process.<n>The effectiveness of the method is validated through comparisons with a pure IEKF baseline, learning-based methods, and the ground-truth trajectory in kinematic simulations.
  arXiv  Detail & Related papers  (2026-06-17T16:04:58Z)
• AcroRL: Learning Aggressive Quadrotor Inversion using Bidirectional Thrust [1.7394635819080972]
  Bidirectional thrust grants quadrotors a second equilibrium condition and increased control authority.<n>Prior geometric control approaches extend differential flatness through Hopf fibration-based attitude representations to support bidirectional thrust.<n>We propose a learning-based framework that modulates a constant reference trajectory to perform compact, position-constrained quadrotor inversions.
  arXiv  Detail & Related papers  (2026-05-23T00:02:22Z)
• A Learning-based Control Methodology for Transitioning VTOL UAVs [3.7488255458663384]
  Current control methods' decoupled control of altitude and position leads to significant vibration.<n>We propose a novel coupled transition control methodology based on reinforcement learning (RL) driven controller.<n>We validate the feasibility of applying our method in simulation and real-world environments.
  arXiv  Detail & Related papers  (2025-12-03T08:13:50Z)
• Fast Training of Recurrent Neural Networks with Stationary State Feedbacks [48.22082789438538]
  Recurrent neural networks (RNNs) have recently demonstrated strong performance and faster inference than Transformers.<n>We propose a novel method that replaces BPTT with a fixed gradient feedback mechanism.
  arXiv  Detail & Related papers  (2025-03-29T14:45:52Z)
• Parametric Nonlinear Volterra Series via Machine Learning: Transonic Aerodynamics [0.0]
  This study introduces an approach for modeling unsteady transonic aerodynamics within a parametric space. The first- and second-order Volterra kernels are derived from indicial aerodynamic responses. Results underscore the benefit of including the second-order kernel to address strong nonlinearity.
  arXiv  Detail & Related papers  (2024-10-25T12:40:10Z)
• AdaOcc: Adaptive Forward View Transformation and Flow Modeling for 3D Occupancy and Flow Prediction [56.72301849123049]
  We present our solution for the Vision-Centric 3D Occupancy and Flow Prediction track in the nuScenes Open-Occ dataset challenge at CVPR 2024. Our innovative approach involves a dual-stage framework that enhances 3D occupancy and flow predictions by incorporating adaptive forward view transformation and flow modeling. Our method combines regression with classification to address scale variations in different scenes, and leverages predicted flow to warp current voxel features to future frames, guided by future frame ground truth.
  arXiv  Detail & Related papers  (2024-07-01T16:32:15Z)
• Irregular Traffic Time Series Forecasting Based on Asynchronous Spatio-Temporal Graph Convolutional Network [46.409567044630116]
  We propose an Asynchronous Spatio-tEmporal graph convolutional nEtwoRk (ASeer) tailored for irregular traffic time series forecasting. We first propose an Asynchronous Graph Diffusion Network to capture the spatial dependency between asynchronously measured traffic states regulated by adaptive traffic signals. Then, we propose a Transformable Time-aware Convolution Network, which adapts meta-filters for time-aware convolution on the sequences with inconsistent temporal flow.
  arXiv  Detail & Related papers  (2023-08-31T15:49:21Z)
• Time-to-Green predictions for fully-actuated signal control systems with supervised learning [56.66331540599836]
  This paper proposes a time series prediction framework using aggregated traffic signal and loop detector data. We utilize state-of-the-art machine learning models to predict future signal phases' duration. Results based on an empirical data set from a fully-actuated signal control system in Zurich, Switzerland, show that machine learning models outperform conventional prediction methods.
  arXiv  Detail & Related papers  (2022-08-24T07:50:43Z)
• Physics-Inspired Temporal Learning of Quadrotor Dynamics for Accurate Model Predictive Trajectory Tracking [76.27433308688592]
  Accurately modeling quadrotor's system dynamics is critical for guaranteeing agile, safe, and stable navigation. We present a novel Physics-Inspired Temporal Convolutional Network (PI-TCN) approach to learning quadrotor's system dynamics purely from robot experience. Our approach combines the expressive power of sparse temporal convolutions and dense feed-forward connections to make accurate system predictions.
  arXiv  Detail & Related papers  (2022-06-07T13:51:35Z)
• 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)
• Stochastic Deep Model Reference Adaptive Control [9.594432031144715]
  We present a Deep Neural Network-based Model Reference Adaptive Control. Deep Model Reference Adaptive Control uses a Lyapunov-based method to adapt the output-layer weights of the DNN model in real-time. A data-driven supervised learning algorithm is used to update the inner-layers parameters.
  arXiv  Detail & Related papers  (2021-08-04T14:05:09Z)
• Offset-free setpoint tracking using neural network controllers [0.0]
  We present a method to analyze local and global stability in offset-free setpoint tracking using neural network controllers. We provide ellipsoidal inner approximations of the corresponding region of attraction.
  arXiv  Detail & Related papers  (2020-11-23T20:13:13Z)
• RSINet: Rotation-Scale Invariant Network for Online Visual Tracking [7.186849714896344]
  Most network-based trackers perform the tracking process without model update, and cannot learn targetspecific variation adaptively. In this paper, we propose a novel Rotation-Scale Invariant Network (RSINet) to address the above problem. Our RSINet tracker consists of a target-distractor discrimination branch and a rotation-scale estimation branch, the rotation and scale knowledge can be explicitly learned by a multi-task learning method in an end-to-end manner. In addtion, the tracking model is adaptively optimized and updated undertemporal energy control, which ensures model stability and reliability, as well as high tracking
  arXiv  Detail & Related papers  (2020-11-18T08:19:14Z)
• Power Control for a URLLC-enabled UAV system incorporated with DNN-Based Channel Estimation [82.16169603954663]
  This letter is concerned with power control for ultra-reliable low-latency communications (URLLC) enabled unmanned aerial vehicle (UAV) system incorporated with deep neural network (DNN) based channel estimation.
  arXiv  Detail & Related papers  (2020-11-14T02:31:04Z)
• Federated Learning in the Sky: Joint Power Allocation and Scheduling with UAV Swarms [98.78553146823829]
  Unmanned aerial vehicle (UAV) swarms must exploit machine learning (ML) in order to execute various tasks. In this paper, a novel framework is proposed to implement distributed learning (FL) algorithms within a UAV swarm.
  arXiv  Detail & Related papers  (2020-02-19T14:04:01Z)

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.