Tire-road friction estimation and uncertainty assessment to improve electric aircraft braking system

• URL: http://arxiv.org/abs/2211.10336v1
• Date: Mon, 14 Nov 2022 10:36:14 GMT
• Title: Tire-road friction estimation and uncertainty assessment to improve electric aircraft braking system
• Abstract summary: Online estimation of the road-friction coefficient is an essential feature for any advanced brake control system. In this study, a data-driven scheme based on a Neural Net is proposed to estimate the optimum friction coefficient as a function of windowed slip-friction measurements. Open loop and closed loop simulations of the landing phase of an aircraft on an unknown surface are used to show the potentiality and efficacy of the proposed robust friction estimation approach.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The accurate online estimation of the road-friction coefficient is an essential feature for any advanced brake control system. In this study, a data-driven scheme based on a MLP Neural Net is proposed to estimate the optimum friction coefficient as a function of windowed slip-friction measurements. A stochastic NN weights drop-out mechanism is used to online estimate the confidence interval of the estimated best friction coefficient thus providing a characterization of the epistemic uncertainty associated to the NN block. Open loop and closed loop simulations of the landing phase of an aircraft on an unknown surface are used to show the potentiality and efficacy of the proposed robust friction estimation approach.

Related papers

• Parsimonious disturbance-aware minimum-time planning with parametric uncertainty [0.0]
  This study presents and validates a minimum-lap-time planning framework for motorsport applications.<n>The methodology builds upon a prior disturbance-aware framework that propagates vehicle dynamics over a short horizon.<n>We extend the formulation to account for uncertainty in key vehicle parameters.
  arXiv  Detail & Related papers  (2026-07-14T22:32:35Z)
• Binary Road Surface Classification Using Machine Learning on Production Vehicle Signals During Cruising [0.0]
  The correlation between vehicle signals and road surface condition during cruising needs to be uncovered using machine learning.<n>A feature-based framework and an end-to-end data-driven framework are used to correlate the statistics of vehicle dynamics behavior with the condition of the road surface.<n>A sliding-window approach is adopted to batch a short buffered window of wheel speeds, wheel torques, longitudinal acceleration, steering angle, and yaw rate.
  arXiv  Detail & Related papers  (2026-06-01T18:25:53Z)
• Non-Parametric Rehearsal Learning via Conditional Mean Embeddings [88.89267783967263]
  We propose a non-parametric rehearsal learning approach for textitavoiding undesired future (AUF) problems.<n>Specifically, we use kernel machinery to reformulate the AUF objective into a unified representation that disentangles desirability modeling from action-induced distributional changes.
  arXiv  Detail & Related papers  (2026-05-09T15:30:52Z)
• High-Slip-Ratio Control for Peak Tire-Road Friction Estimation Using Automated Vehicles [9.252000699538346]
  This paper presents a high-slip-ratio control framework that enables automated vehicles to actively excite the peak friction region during empty-haul operations.<n>A binning-based statistical projection method is introduced to robustly estimate peak TRFC under noise and local sparsity.<n>The framework is validated through both closed-loop simulations and real-vehicle experiments, demonstrating its accuracy, safety, and feasibility for scalable, cost-effective roadway friction screening.
  arXiv  Detail & Related papers  (2026-03-10T01:34:00Z)
• On Minimax Estimation of Parameters in Softmax-Contaminated Mixture of Experts [66.39976432286905]
  We study the convergence rates of the maximum likelihood estimator of gating and prompt parameters.<n>We find that the estimability of these parameters is compromised when the prompt acquires overlapping knowledge with the pre-trained model.
  arXiv  Detail & Related papers  (2025-05-24T01:30:46Z)
• TULiP: Test-time Uncertainty Estimation via Linearization and Weight Perturbation [11.334867025651233]
  We propose TULiP, a theoretically-driven uncertainty estimator for OOD detection.<n>Our approach considers a hypothetical perturbation applied to the network before convergence.<n>Our method exhibits state-of-the-art performance, particularly for near-distribution samples.
  arXiv  Detail & Related papers  (2025-05-22T17:16:41Z)
• Damper-B-PINN: Damper Characteristics-Based Bayesian Physics-Informed Neural Network for Vehicle State Estimation [9.26047003556515]
  We propose a damper characteristics-based Bayesian physics-informed neural network (Damper-B-PINN) framework to estimate dynamic wheel load.<n>Results show that our Damper-B-PINN consistently outperforms existing methods across various test conditions, particularly extreme ones.<n>These findings highlight the potential of the proposed Damper-B-PINN framework to enhance the accuracy and robustness of dynamic wheel load estimation.
  arXiv  Detail & Related papers  (2025-02-28T06:46:21Z)
• Low-Order Flow Reconstruction and Uncertainty Quantification in Disturbed Aerodynamics Using Sparse Pressure Measurements [0.0]
  This paper presents a novel machine-learning framework for reconstructing low-order gustencounter flow field and lift coefficients from sparse, noisy surface pressure measurements. Our study thoroughly investigates the time-varying response of sensors to gust-air interactions, uncovering valuable insights into optimal sensor placement.
  arXiv  Detail & Related papers  (2025-01-06T22:02:06Z)
• Large-Scale OD Matrix Estimation with A Deep Learning Method [70.78575952309023]
  The proposed method integrates deep learning and numerical optimization algorithms to infer matrix structure and guide numerical optimization. We conducted tests to demonstrate the good generalization performance of our method on a large-scale synthetic dataset.
  arXiv  Detail & Related papers  (2023-10-09T14:30:06Z)
• Lightweight Regression Model with Prediction Interval Estimation for Computer Vision-based Winter Road Surface Condition Monitoring [0.4972323953932129]
  This paper proposes a deep learning regression model, SIWNet, capable of estimating road surface friction properties from camera images. SIWNet extends state of the art by including an uncertainty estimation mechanism in the architecture. The model was trained and tested with the SeeingThroughFog dataset, which features corresponding road friction sensor readings and images from an instrumented vehicle.
  arXiv  Detail & Related papers  (2023-10-02T06:33:06Z)
• Estimating friction coefficient using generative modelling [0.7503129292751939]
  This work aims to formulate the problem of friction estimation as a visual perceptual learning task. The problem is broken down into detecting surface characteristics by applying semantic segmentation and using the extracted features to predict the frictional force.
  arXiv  Detail & Related papers  (2023-03-10T13:58:28Z)
• A Data-Driven Slip Estimation Approach for Effective Braking Control under Varying Road Conditions [0.0]
  A novel estimation algorithm is proposed, based on a multi-layer neural network. The training is based on a synthetic data set, derived from a widely used friction model. The experimental results and the comparison with a model based baseline show that the proposed approach can provide an effective best slip estimation.
  arXiv  Detail & Related papers  (2022-11-04T16:24:05Z)
• 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)
• Uncertainty estimation of pedestrian future trajectory using Bayesian approximation [137.00426219455116]
  Under dynamic traffic scenarios, planning based on deterministic predictions is not trustworthy. The authors propose to quantify uncertainty during forecasting using approximation which deterministic approaches fail to capture. The effect of dropout weights and long-term prediction on future state uncertainty has been studied.
  arXiv  Detail & Related papers  (2022-05-04T04:23:38Z)
• Pointwise Feasibility of Gaussian Process-based Safety-Critical Control under Model Uncertainty [77.18483084440182]
  Control Barrier Functions (CBFs) and Control Lyapunov Functions (CLFs) are popular tools for enforcing safety and stability of a controlled system, respectively. We present a Gaussian Process (GP)-based approach to tackle the problem of model uncertainty in safety-critical controllers that use CBFs and CLFs.
  arXiv  Detail & Related papers  (2021-06-13T23:08:49Z)
• Hybrid Physics and Deep Learning Model for Interpretable Vehicle State Prediction [75.1213178617367]
  We propose a hybrid approach combining deep learning and physical motion models. We achieve interpretability by restricting the output range of the deep neural network as part of the hybrid model. The results show that our hybrid model can improve model interpretability with no decrease in accuracy compared to existing deep learning approaches.
  arXiv  Detail & Related papers  (2021-03-11T15:21:08Z)
• The Aleatoric Uncertainty Estimation Using a Separate Formulation with Virtual Residuals [51.71066839337174]
  Existing methods can quantify the error in the target estimation, but they tend to underestimate it. We propose a new separable formulation for the estimation of a signal and of its uncertainty, avoiding the effect of overfitting. We demonstrate that the proposed method outperforms a state-of-the-art technique for signal and uncertainty estimation.
  arXiv  Detail & Related papers  (2020-11-03T12:11:27Z)
• Neural Contraction Metrics for Robust Estimation and Control: A Convex Optimization Approach [6.646482960350819]
  This paper presents a new deep learning-based framework for robust nonlinear estimation and control using the concept of a Neural Contraction Metric (NCM) The NCM uses a deep long short-term memory recurrent neural network for a global approximation of an optimal contraction metric. We demonstrate how to exploit NCMs to design an online optimal estimator and controller for nonlinear systems with bounded disturbances utilizing their duality.
  arXiv  Detail & Related papers  (2020-06-08T05:29:38Z)
• Learning Control Barrier Functions from Expert Demonstrations [69.23675822701357]
  We propose a learning based approach to safe controller synthesis based on control barrier functions (CBFs) We analyze an optimization-based approach to learning a CBF that enjoys provable safety guarantees under suitable Lipschitz assumptions on the underlying dynamical system. To the best of our knowledge, these are the first results that learn provably safe control barrier functions from data.
  arXiv  Detail & Related papers  (2020-04-07T12:29:06Z)

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.