Implementation of a neural network for non-linearities estimation in a tail-sitter aircraft

• URL: http://arxiv.org/abs/2010.06049v1
• Date: Mon, 12 Oct 2020 21:46:16 GMT
• Title: Implementation of a neural network for non-linearities estimation in a tail-sitter aircraft
• Authors: A. Flores and G. Flores
• Abstract summary: Control of a tail-sitter aircraft is a challenging task, especially during transition maneuver. We implement a Neural Network capable of estimate such nonlinearities. Experiments demonstrate that the implemented NN can be used to estimate the tail-sitter aerodynamic forces.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The control of a tail-sitter aircraft is a challenging task, especially during transition maneuver where the lift and drag forces are highly nonlinear. In this work, we implement a Neural Network (NN) capable of estimate such nonlinearities. Once they are estimated, one can propose a control scheme where these forces can correctly feed-forwarded. Our implementation of the NN has been programmed in C++ on the PX4 Autopilot an open-source autopilot for drones. To ensure that this implementation does not considerably affect the autopilot's performance, the coded NN must be of a light computational load. With the aim to test our approach, we have carried out a series of realistic simulations in the Software in The Loop (SITL) using the PX4 Autopilot. These experiments demonstrate that the implemented NN can be used to estimate the tail-sitter aerodynamic forces, and can be used to improve the control algorithms during all the flight phases of the tail-sitter aircraft: hover, cruise flight, and transition.

Related papers

• Planning with Adaptive World Models for Autonomous Driving [50.4439896514353]
  Motion planners (MPs) are crucial for safe navigation in complex urban environments. nuPlan, a recently released MP benchmark, addresses this limitation by augmenting real-world driving logs with closed-loop simulation logic. We present AdaptiveDriver, a model-predictive control (MPC) based planner that unrolls different world models conditioned on BehaviorNet's predictions.
  arXiv  Detail & Related papers  (2024-06-15T18:53:45Z)
• Angle Robustness Unmanned Aerial Vehicle Navigation in GNSS-Denied Scenarios [66.05091704671503]
  We present a novel angle navigation paradigm to deal with flight deviation in point-to-point navigation tasks. We also propose a model that includes the Adaptive Feature Enhance Module, Cross-knowledge Attention-guided Module and Robust Task-oriented Head Module.
  arXiv  Detail & Related papers  (2024-02-04T08:41:20Z)
• DATT: Deep Adaptive Trajectory Tracking for Quadrotor Control [62.24301794794304]
  Deep Adaptive Trajectory Tracking (DATT) is a learning-based approach that can precisely track arbitrary, potentially infeasible trajectories in the presence of large disturbances in the real world. DATT significantly outperforms competitive adaptive nonlinear and model predictive controllers for both feasible smooth and infeasible trajectories in unsteady wind fields. It can efficiently run online with an inference time less than 3.2 ms, less than 1/4 of the adaptive nonlinear model predictive control baseline.
  arXiv  Detail & Related papers  (2023-10-13T12:22:31Z)
• Learning a Single Near-hover Position Controller for Vastly Different Quadcopters [56.37274861303324]
  This paper proposes an adaptive near-hover position controller for quadcopters. It can be deployed to quadcopters of very different mass, size and motor constants. It also shows rapid adaptation to unknown disturbances during runtime.
  arXiv  Detail & Related papers  (2022-09-19T17:55:05Z)
• Neural Moving Horizon Estimation for Robust Flight Control [6.023276947115864]
  Estimating and reacting to external disturbances is crucial for robust flight control of quadrotors. We propose a neural moving horizon estimator (NeuroMHE) that can automatically tune the MHE parameters modeled by a neural network. NeuroMHE outperforms the state-of-the-art estimator with force estimation error reductions of up to 49.4%.
  arXiv  Detail & Related papers  (2022-06-21T13:43:24Z)
• Data-Efficient Deep Reinforcement Learning for Attitude Control of Fixed-Wing UAVs: Field Experiments [0.37798600249187286]
  We show that DRL can successfully learn to perform attitude control of a fixed-wing UAV operating directly on the original nonlinear dynamics. We deploy the learned controller on the UAV in flight tests, demonstrating comparable performance to the state-of-the-art ArduPlane proportional-integral-derivative (PID) attitude controller.
  arXiv  Detail & Related papers  (2021-11-07T19:07:46Z)
• An Adaptive Digital Autopilot for Fixed-Wing Aircraft with Actuator Faults [0.0]
  This paper develops an adaptive digital autopilot for a fixed-wing aircraft. It compares its performance with a fixed-gain autopilot. It is tested in simulation, and the resulting performance improvements are examined.
  arXiv  Detail & Related papers  (2021-10-21T18:17:36Z)
• Evolved neuromorphic radar-based altitude controller for an autonomous open-source blimp [4.350434044677268]
  In this paper, we propose an evolved altitude controller based on an SNN for a robotic airship. We also present an SNN-based controller architecture, an evolutionary framework for training the network in a simulated environment, and a control strategy for ameliorating the gap with reality.
  arXiv  Detail & Related papers  (2021-10-01T20:48:43Z)
• DikpolaSat Mission: Improvement of Space Flight Performance and Optimal Control Using Trained Deep Neural Network -- Trajectory Controller for Space Objects Collision Avoidance [0.0]
  This paper shows how the controller demonstration is carried out by having the spacecraft follow a desired path. The obstacle avoidance algorithm is built into the control features to respond spontaneously using inputs from the neural network. Multiple algorithms for optimizing flight controls and fuel consumption can be implemented using knowledge of flight dynamics in trajectory.
  arXiv  Detail & Related papers  (2021-05-30T23:35:13Z)
• Efficient and Robust LiDAR-Based End-to-End Navigation [132.52661670308606]
  We present an efficient and robust LiDAR-based end-to-end navigation framework. We propose Fast-LiDARNet that is based on sparse convolution kernel optimization and hardware-aware model design. We then propose Hybrid Evidential Fusion that directly estimates the uncertainty of the prediction from only a single forward pass.
  arXiv  Detail & Related papers  (2021-05-20T17:52:37Z)
• Transition control of a tail-sitter UAV using recurrent neural networks [80.91076033926224]
  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.
  arXiv  Detail & Related papers  (2020-06-29T21:33:30Z)

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.