Analysis of Centrifugal Clutches in Two-Speed Automatic Transmissions with Deep Learning-Based Engagement Prediction

• URL: http://arxiv.org/abs/2409.09755v2
• Date: Wed, 25 Sep 2024 00:43:03 GMT
• Title: Analysis of Centrifugal Clutches in Two-Speed Automatic Transmissions with Deep Learning-Based Engagement Prediction
• Abstract summary: The study systematically examines various clutch configurations effects on transmission dynamics. A Deep Neural Network (DNN) model predicts clutch engagement using parameters such as spring preload and shoe mass.
• Score: 0.18416014644193066
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper presents a comprehensive numerical analysis of centrifugal clutch systems integrated with a two-speed automatic transmission, a key component in automotive torque transfer. Centrifugal clutches enable torque transmission based on rotational speed without external controls. The study systematically examines various clutch configurations effects on transmission dynamics, focusing on torque transfer, upshifting, and downshifting behaviors under different conditions. A Deep Neural Network (DNN) model predicts clutch engagement using parameters such as spring preload and shoe mass, offering an efficient alternative to complex simulations. The integration of deep learning and numerical modeling provides critical insights for optimizing clutch designs, enhancing transmission performance and efficiency.

Related papers

• Closing the Reality Gap: Zero-Shot Sim-to-Real Deployment for Dexterous Force-Based Grasping and Manipulation [17.63466252699545]
  Human-like dexterous hands offer human-level manipulation capabilities but remain difficult to train the control policies that can deploy on real hardware.<n>We present a sim-to-real reinforcement learning method that leverages dense tactile feedback combined with joint torque sensing.<n>This is the first demonstration of controllable grasping on a multi-finger dexterous hand trained entirely in simulation and transferred zero-shot on real hardware.
  arXiv  Detail & Related papers  (2026-07-06T11:15:36Z)
• Friction Characterization of a Cable-Driven Differential Actuation System for Lower-Limb Exoskeletons [42.628421392139]
  Lower-limb exoskeletons require actuation systems that can provide accurate joint torque control.<n> Conventional architectures often rely on independently actuated joints and joint-level torque sensors.<n>This paper presents a novel differential actuation architecture for hip-knee flexion/extension.
  arXiv  Detail & Related papers  (2026-06-14T19:47:32Z)
• Driver-WM: A Driver-Centric Traffic-Conditioned Latent World Model for In-Cabin Dynamics Rollout [55.19244743715421]
  Driver-WM is a driver-centric latent world model that rolls out in-cabin dynamics causally conditioned on out-cabin traffic context.<n>It unifies physical kinematics forecasting with auxiliary behavioral and emotional semantic recognition.<n>Driver-WM yields robust long-horizon geometric forecasting for reactive high-motion maneuvers.
  arXiv  Detail & Related papers  (2026-05-06T16:30:48Z)
• Physics-Informed Reinforcement Learning of Spatial Density Velocity Potentials for Map-Free Racing [73.88859384645264]
  Out-Of-Distribution (OOD) generalization to various racetracks utilizes Machine Learning (ML) to encode the mathematical relation between sensor data and vehicle actuation for end-to-end control.<n>This paper presents a DRL method that parameterizes nonlinear vehicle dynamics from the spectral distribution of depth measurements with a non-geometric, physics-informed reward, to infer vehicle time-optimal and overtaking racing controls with an Artificial Neural Network (ANN)<n>The policy outperforms human demonstrations by 12% in OOD tracks on proportionally scaled hardware, by maximizing the friction circle with tire dynamics that resemble an empirical Pacejka tire model.
  arXiv  Detail & Related papers  (2026-04-10T17:12:07Z)
• Learning Quadrupedal Locomotion for a Heavy Hydraulic Robot Using an Actuator Model [7.275864116517311]
  This work is the first demonstration of a successful transfer of stable and robust command-tracking locomotion with reinforcement learning (RL) on a heavy hydraulic quadruped robot.<n>The model predicts joint torques for all 12 actuators in under 1 microsecond, allowing rapid processing in RL environments.
  arXiv  Detail & Related papers  (2026-01-16T10:01:09Z)
• Closing the Reality Gap: Zero-Shot Sim-to-Real Deployment for Dexterous Force-Based Grasping and Manipulation [12.509181374985936]
  Human-like dexterous hands with multiple fingers offer human-level manipulation capabilities.<n>But training control policies that can directly deploy on real hardware remains difficult due to contact-rich physics.<n>We present a practical framework that utilizes dense tactile feedback combined with joint torque sensing to regulate physical interactions.
  arXiv  Detail & Related papers  (2026-01-06T07:26:39Z)
• Load-Based Variable Transmission Mechanism for Robotic Applications [4.340505929849356]
  This paper presents a Load-Based Variable Transmission (LBVT) mechanism designed to enhance robotic actuation.<n>Unlike existing variable transmission systems that require additional actuators for active control, the proposed LBVT mechanism passively modify the transmission ratio.<n>The effectiveness of the LBVT mechanism is evaluated through simulation-based analyses.
  arXiv  Detail & Related papers  (2025-12-17T13:45:22Z)
• STAR-RIS-assisted Collaborative Beamforming for Low-altitude Wireless Networks [58.13757830013997]
  Wireless networks based on uncrewed aerial vehicles (UAVs) offer high mobility, flexibility, and coverage for urban communications.<n>They face severe signal attenuation in dense environments due to obstructions.<n>To address this critical issue, we consider introducing collaborative beam of UAVs and omni-directional re-altitude beamforming.
  arXiv  Detail & Related papers  (2025-10-25T01:28:37Z)
• Drivetrain simulation using variational autoencoders [0.0]
  This work proposes variational autoencoders (VAEs) to predict a vehicle's jerk from a given torque demand. We implement unconditional and conditional VAEs to generate jerk signals that integrate features from different drivetrain scenarios.
  arXiv  Detail & Related papers  (2025-01-29T13:37:32Z)
• QNN-VRCS: A Quantum Neural Network for Vehicle Road Cooperation Systems [2.7985570786346745]
  This research integrates quantum computing techniques to enhance Vehicle Road Cooperation Systems (VRCS) We propose an optimized Quantum Neural Network (QNN) to better handle the complexities of traffic data processing. Empirical evaluations on two traffic datasets show that our model achieves superior classification accuracies of 97.42% and 84.08%.
  arXiv  Detail & Related papers  (2024-12-17T09:20:08Z)
• Semantic Communication for Cooperative Perception using HARQ [51.148203799109304]
  We leverage an importance map to distill critical semantic information, introducing a cooperative perception semantic communication framework. To counter the challenges posed by time-varying multipath fading, our approach incorporates the use of frequency-division multiplexing (OFDM) along with channel estimation and equalization strategies. We introduce a novel semantic error detection method that is integrated with our semantic communication framework in the spirit of hybrid automatic repeated request (HARQ)
  arXiv  Detail & Related papers  (2024-08-29T08:53:26Z)
• Function Approximation for Reinforcement Learning Controller for Energy from Spread Waves [69.9104427437916]
  Multi-generator Wave Energy Converters (WEC) must handle multiple simultaneous waves coming from different directions called spread waves. These complex devices need controllers with multiple objectives of energy capture efficiency, reduction of structural stress to limit maintenance, and proactive protection against high waves. In this paper, we explore different function approximations for the policy and critic networks in modeling the sequential nature of the system dynamics.
  arXiv  Detail & Related papers  (2024-04-17T02:04:10Z)
• Learning-based adaption of robotic friction models [48.453527255659296]
  We introduce a novel approach to adapt an existing friction model to new dynamics using as little data as possible. Our proposed estimator outperforms the conventional model-based approach and the base neural network significantly. Our method does not rely on data with external load during training, eliminating the need for external torque sensors.
  arXiv  Detail & Related papers  (2023-10-25T14:50:15Z)
• Leveraging Low-Rank and Sparse Recurrent Connectivity for Robust Closed-Loop Control [63.310780486820796]
  We show how a parameterization of recurrent connectivity influences robustness in closed-loop settings. We find that closed-form continuous-time neural networks (CfCs) with fewer parameters can outperform their full-rank, fully-connected counterparts.
  arXiv  Detail & Related papers  (2023-10-05T21:44:18Z)
• Differentiable Hybrid Traffic Simulation [29.86954929536965]
  We introduce a novel differentiable hybrid traffic simulator, which simulates traffic using a hybrid model of both macroscopic and microscopic models. This is the first differentiable traffic simulator for macroscopic and hybrid models that can compute gradients for traffic states across time steps and inhomogeneous lanes.
  arXiv  Detail & Related papers  (2022-10-14T18:20:15Z)
• Network-wide traffic signal control optimization using a multi-agent deep reinforcement learning [20.385286762476436]
  Inefficient traffic control may cause numerous problems such as traffic congestion and energy waste. This paper proposes a novel multi-agent reinforcement learning method, named KS-DDPG, to achieve optimal control by enhancing the cooperation between traffic signals.
  arXiv  Detail & Related papers  (2021-04-20T12:53:08Z)
• Federated Learning on the Road: Autonomous Controller Design for Connected and Autonomous Vehicles [109.71532364079711]
  A new federated learning (FL) framework is proposed for designing the autonomous controller of connected and autonomous vehicles (CAVs) A novel dynamic federated proximal (DFP) algorithm is proposed that accounts for the mobility of CAVs, the wireless fading channels, and the unbalanced and nonindependent and identically distributed data across CAVs. A rigorous convergence analysis is performed for the proposed algorithm to identify how fast the CAVs converge to using the optimal controller.
  arXiv  Detail & Related papers  (2021-02-05T19:57:47Z)
• Multi-intersection Traffic Optimisation: A Benchmark Dataset and a Strong Baseline [85.9210953301628]
  Control of traffic signals is fundamental and critical to alleviate traffic congestion in urban areas. Because of the high complexity of modelling the problem, experimental settings of current works are often inconsistent. We propose a novel and strong baseline model based on deep reinforcement learning with the encoder-decoder structure.
  arXiv  Detail & Related papers  (2021-01-24T03:55:39Z)
• Optimization of Operation Strategy for Primary Torque based hydrostatic Drivetrain using Artificial Intelligence [0.0]
  A new primary torque control concept for hydrostatics mobile machines was introduced in 2018. While working in a Y cycle, the primary torque-controlled wheel loader has worse performance in efficiency compared to secondary controlled earthmover. In this paper, we propose a series of CRDNNs, which combine CNN, RNN, and DNN, to precisely detect Y cycles.
  arXiv  Detail & Related papers  (2020-03-22T22:16:39Z)
• Traffic Data Imputation using Deep Convolutional Neural Networks [2.7647400328727256]
  We show that a well trained neural network can learn traffic speed dynamics from time-space diagrams. Our results show that with vehicle penetration probe levels as low as 5%, the proposed estimation method can provide a sound reconstruction of macroscopic traffic speeds.
  arXiv  Detail & Related papers  (2020-01-21T12:52:58Z)

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.