IFAC-PapersOnLine最新文献_第8页

Modeling human-autonomy team steering behavior in shared-autonomy driving scenarios 共享自动驾驶场景下人类自主驾驶团队转向行为建模

IFAC-PapersOnLine Pub Date : 2025-01-01 DOI: 10.1016/j.ifacol.2025.07.003

Rene E. Mai , Kara Daveron , Agung Julius , Sandipan Mishra

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引用次数: 0

Efficient and Risk-Aware Framework for Autonomous Navigation in Resource-Constrained Configurations 资源约束下自主导航的有效和风险意识框架

IFAC-PapersOnLine Pub Date : 2025-01-01 DOI: 10.1016/j.ifacol.2025.07.022

Mohamed Benrabah , Charifou Orou Mousse , Roland Chapuis , Romuald Aufrère

引用次数: 0

Learning Autonomy: Off-Road Navigation Enhanced by Human Input 学习自主性：人工输入增强的越野导航

IFAC-PapersOnLine Pub Date : 2025-01-01 DOI: 10.1016/j.ifacol.2025.07.024

Akhil Nagariya , Dimitar Filev , Srikanth Saripalli , Srikanth Saripalli , Gaurav Pandey

引用次数: 0

A compact algebraic model for electric machine losses⁎ 电机损耗的紧致代数模型

IFAC-PapersOnLine Pub Date : 2025-01-01 DOI: 10.1016/j.ifacol.2025.07.106

Lars Eriksson

引用次数: 0

Rearward Amplification Behavior in n-Trailer Kinematic Articulated Vehicles⁎ n-挂车运动铰接式车辆的后向放大行为[j]

IFAC-PapersOnLine Pub Date : 2025-01-01 DOI: 10.1016/j.ifacol.2025.07.110

Yuvraj Singh , Adithya Jayakumar , Giorgio Rizzoni

引用次数: 0

Safe Reinforcement Learning-Based Control for Hydrogen Diesel Dual-Fuel Engines 基于强化学习的氢柴油双燃料发动机安全控制

IFAC-PapersOnLine Pub Date : 2025-01-01 DOI: 10.1016/j.ifacol.2025.07.075

Vasu Sharma , Alexander Winkler , Armin Norouzi , Hongsheng Guo , Jakob Andert , David Gordon

{"title":"Safe Reinforcement Learning-Based Control for Hydrogen Diesel Dual-Fuel Engines","authors":"Vasu Sharma , Alexander Winkler , Armin Norouzi , Hongsheng Guo , Jakob Andert , David Gordon","doi":"10.1016/j.ifacol.2025.07.075","DOIUrl":"10.1016/j.ifacol.2025.07.075","url":null,"abstract":"<div><div>The urgent energy transition requirements towards a sustainable future span multiple industries and are a significant challenge facing humanity. Hydrogen promises a clean, carbon-free future, with the potential to integrate into existing transportation technologies. However, adding hydrogen to existing technologies such as diesel engines requires additional modeling effort. Reinforcement Learning (RL) enables interactive data-driven learning that eliminates the need for mathematical modeling for controller synthesis. The algorithms, however, may not be real-time capable and need large amounts of data to work in practice. This paper presents a novel approach which uses offline model learning with RL to demonstrate safe control of a 4.5 L Hydrogen Diesel Dual-Fuel (H2DF) engine. An offline H2DF model learning step facilitates the policy search in a simulated environment. The controllers are demonstrated to be constraint-compliant and can leverage a novel state-augmentation approach for sample-efficient learning. The offline policy is subsequently experimentally validated on the real engine where the control algorithm is executed on a Raspberry Pi controller and requires 6 times less computation time compared to online model predictive control optimization.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 5","pages":"Pages 19-24"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144779826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Efficiency and Performance Improvement of Energy Management for Series-type HEVs by Robust Output Predictive Control⁎ 基于鲁棒输出预测控制的串联式混合动力汽车能量管理效率与性能改进

IFAC-PapersOnLine Pub Date : 2025-01-01 DOI: 10.1016/j.ifacol.2025.07.082

K. Sakamoto , I. Mizumoto

引用次数: 0

Data-Driven Misfire Detection in Hydrogen Gen-sets using a Production Exhaust Pressure Sensor 使用生产排气压力传感器的氢气装置数据驱动失火检测

IFAC-PapersOnLine Pub Date : 2025-01-01 DOI: 10.1016/j.ifacol.2025.07.072

Aharishkumar Muswathi Babulal , Jelle Heijne , Peter Wezenbeek , Maarten Vlaswinkel , Frank Willems

{"title":"Data-Driven Misfire Detection in Hydrogen Gen-sets using a Production Exhaust Pressure Sensor","authors":"Aharishkumar Muswathi Babulal , Jelle Heijne , Peter Wezenbeek , Maarten Vlaswinkel , Frank Willems","doi":"10.1016/j.ifacol.2025.07.072","DOIUrl":"10.1016/j.ifacol.2025.07.072","url":null,"abstract":"<div><div>With the growing demand for climate-neutral powertrains, hydrogen combustion gen-sets are emerging as cleaner alternatives to diesel gen-sets. However, spark-ignited hydrogen engines are prone to misfires, impacting performance and engine lifespan. This study presents a novel approach for detecting misfires and identifying the misfiring cylinder using exhaust pressure signals from the production sensor, enabling a cost-effective, real-time diagnostic solution. Unlike complex feature extraction methods, the proposed approach is optimized for constant-speed gen-sets, ensuring computational efficiency and seamless integration within an Engine Management System. The technique utilizes exhaust pressure and crank angle signals to compute a tracking error feature—the squared deviation between the actual pressure signal and a reference signal. A common reference signal is modeled using normalized normal combustion exhaust pressure data from the training set and can be used for different loads. The method is validated at a 6° crank angle resolution in the hardware across multiple misfiring patterns, including single, continuous, and multiple cylinder misfire events, and the results demonstrated excellent performance under steady-state conditions. Finally, validation on the research engine demonstrated the method’s feasibility for real-time implementation.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 5","pages":"Pages 1-6"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144779881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Task-optimal data-driven surrogate models for eNMPC via differentiable simulation and optimization 基于可微仿真和优化的eNMPC任务最优数据驱动代理模型

IFAC-PapersOnLine Pub Date : 2025-01-01 DOI: 10.1016/j.ifacol.2025.07.119

Daniel Mayfrank , Na Young Ahn , Alexander Mitsos , Manuel Dahmen

引用次数: 0

Hybrid Optimization Methods for Parameter Estimation of Reactive Transport Systems 无功输运系统参数估计的混合优化方法

IFAC-PapersOnLine Pub Date : 2025-01-01 DOI: 10.1016/j.ifacol.2025.07.139

Marcus Johan Schytt , Halldór Gauti Pétursson , John Bagterp Jørgensen

引用次数: 0