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arXiv 2025年

作者： Zeyen, Elisabeth Kalweit, Sina Victoria, Marta Brown, Tom Berlin Berlin10587 Germany Department of Mechanical and Production Engineering iCLIMATE Interdisciplinary Centre for Climate Change Aarhus University Aarhus8000 Denmark Department of Wind and Energy Systems Technical University of Denmark Elektrovej 325 Lyngby2800 Denmark

In 2022, fuel combustion in road transport accounted for approximately 21% (760 million tonnes) of CO2 emissions in the European Union (EU). Road transport is the only sector with rising emissions, with an increase of 24% compared to 1990. The EU initially aimed to ban new CO2-emitting cars by 2030 but has since delayed this target to 2035, underscoring the ongoing challenges in the push for rapid decarbonisation. The pace of decarbonisation in this sector will either ease or intensify the pressure on other sectors to stay within the EU’s carbon budget. This paper explores the effects of speeding up or slowing down the transition in road transport. We reveal that a slower decarbonisation path not only drives up system costs by 126 billion e/a (6%) but also demands more than a doubling of the CO2 price from 137 to 290 e/tCO2in 2030 to trigger decarbonisation in other sectors. On the flip side, accelerating the shift to cleaner transport proves to be the most cost-effective strategy, giving room for more gradual changes in the heating and industrial sectors, while reducing the reliance on carbon removal in later years. Earlier mandates than currently envisaged by the EU can avoid stranded assets and save up to 43 billion e/a compared to current policies. © 2025, CC BY.

关键词： Low emission

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Recursive identification of structured systems: An instrumental-variable approach applied to mechanical systems

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European Journal of control 2025年 84卷

作者： Classens, Koen González, Rodrigo A. Oomen, Tom Eindhoven University of Technology Department of Mechanical Engineering Control Systems Technology Eindhoven Netherlands Delft University of Technology Department of Mechanical Engineering Delft Center for Systems and Control Delft Netherlands

Online system identification algorithms are widely used for monitoring, diagnostics and control by continuously adapting to time-varying dynamics. Typically, these algorithms consider a model structure that lacks parsimony and offers limited physical interpretability. The objective of this paper is to develop a real-time parameter estimation algorithm aimed at identifying time-varying dynamics within an interpretable model structure. An additive model structure is adopted for this purpose, which offers enhanced parsimony and is shown to be particularly suitable for mechanical systems. The proposed approach integrates the recursive simplified refined instrumental variable method with block-coordinate descent to minimize an exponentially-weighted output error cost function. This novel recursive identification method delivers parametric continuous-time additive models and is applicable in both open-loop and closed-loop controlled systems. Its efficacy is shown using numerical simulations and is further validated using experimental data to detect the time-varying resonance dynamics of a flexible beam system. These results demonstrate the effectiveness of the proposed approach for online and interpretable estimation for advanced monitoring and control applications. © 2025 The Authors

关键词： Additive system Closed-loop system identification Parameter estimation Parametric fault diagnosis Recursive identification Refined instrumental variables

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DESIGN OF COMPENSATION SCHEMES FOR A NONMINIMUM-PHASE MULTIVARIABLE PLANT

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IEE PROCEEDINGS-D control THEORY AND APPLICATIONS 1985年第2期132卷 75-85页

作者： KIDD, PT MUNRO, N WINTERBONE, DE Department of Mechanical Engineering UMIST Manchester UK Control Systems Centre UMIST Manchester UK

The design of analogue and digital controllers for a gas-turbine-powered, controllable pitch propeller marine propulsion plant, which displays nonminimum phase characteristics, is considered. Rosenbrock's multi-variable system theory is used to guide the development of control schemes that do not aggravate the non-minimum phase problems of the system.

关键词： digital controllers multivariable plant Rosenbrock's multivariable system theory marine systems multivariable control systems Multivariable control systems nonminimum phase characteristics controllable pitch propeller marine propulsion plant gas turbines digital control control system synthesis analogue controllers control of heat systems control engineering computing compensation Marine system control propulsion control system analysis and synthesis methods

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Recursive Identification of Structured systems: An Instrumental-Variable Approach Applied to mechanical systems

arXiv

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arXiv 2025年

关键词： Channel estimation

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AR/VR Digital Twin for Simulation and Data Collection of Robotic Environments

AR/VR Digital Twin for Simulation and Data Collection of Rob...

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IEEE International Conference on Autonomous Robot systems and Competitions (ICARSC)

作者： João G. Martins Karle Nutonen Paulo Costa Vladimir Kuts Tauno Otto Armando Sousa Marcelo R. Petry CRIIS - Centre for Robotics in Industry and Intelligent Systems INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto Portugal Faculty of Engineering University of Porto (FEUP) Porto Portugal ViruTech - Virumaa innovation center of digitalization and green technologies Taltech Virumaa College Kohtla-Jarve Estonia Department of Mechanical and Indusrial Taltech Tallinn Estonia

ISBN: (数字)9798331538606

ISBN: (纸本)9798331538613

Digital twins enable real-time modeling, simulation, and monitoring of complex systems, driving advancements in automation, robotics, and industrial applications. This study presents a large-scale digital twin-testing facility for evaluating mobile robots and pilot robotic systems in a research laboratory environment. The platform integrates high-fidelity physical and environmental models, providing a controlled yet dynamic setting for analyzing robotic behavior. A key feature of the system is its comprehensive data collection framework, capturing critical parameters such as position, orientation, and velocity, which can be leveraged for machine learning, performance optimization, and decision-making. The facility also supports the simulation of discrete operational systems, using predictive modeling to bridge informational gaps when real-time data updates are unavailable. The digital twin was validated through a matrix manufacturing system simulation, with an Augmented Reality (AR) interface on the HoloLens 2 to overlay digital information onto mobile platform controllers, enhancing situational awareness. The main contributions include a digital twin framework for deploying data-driven robotic systems and three key AR/VR integration optimization methods. Demonstrated in a laboratory setting, the system is a versatile tool for research and industrial applications, fostering insights into robotic automation and digital twin scalability while reducing costs and risks associated with real-world testing.

关键词： Service robots Navigation Metaverse Databases Scalability Digital representation Real-time systems Digital twins Mobile robots Robots

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Endogenous transformation of land transport in Europe for different climate targets

arXiv

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arXiv 2025年

作者： Kalweit, Sina Zeyen, Elisabeth Victoria, Marta Department of Mechanical and Production Engineering Katrinebjergvej 89 Aarhus8200 Denmark Novo Nordisk Foundation CO2 Research Center Gustav Wieds Vej 10 Aarhus8000 Denmark Berlin10587 Germany Department of Mechanical and Production Engineering iCLIMATE Interdisciplinary Centre for Climate Change Aarhus8000 Denmark Department of Wind and Energy Systems Technical University of Denmark Elektrovej 325 Lyngby2800 Denmark

Road transport is responsible for about a quarter of Europe’s greenhouse gas emissions, making its transformation a crucial part of Europe’s overall decarbonization goals. Current European policies promote decarbonizing the transport sector and passenger car sales show an increased adoption of electric vehicles. Full electrification of land transport will significantly increase the average electricity demand but the use of smart charging and vehicle-to-grid could provide additional flexibility to balance wind and solar generation. In this study, we find cost-optimal transition pathways of the European land transport sector embedded in the sector-coupled open energy model PyPSA-Eur. We consider fossil-fueled, hydrogen-fueled, and electric cars using a 3-hour time resolution for a full year and covering 33 interconnected European countries. We analyze a transition path from 2025 to 2050 under different carbon budgets corresponding to a 1.7 ◦C and 2 ◦C temperature increase. Our results show that rapid electrification of road transport reduces the total system cost, even in the absence of climate targets. We see a clear preference for rapidly decommissioning internal combustion engine vehicles and using electric vehicles in all countries and under all carbon budgets. Allowing smart charging of electric vehicles decreases the total system cost by 1.6% because it reduces the need to install stationary batteries by almost 40%. © 2025, CC BY-NC-SA.

关键词： Vehicle-to-grid

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Digital phenotyping from heart rate dynamics: Identification of zero-poles models with data-driven evolutionary learning

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Computers in Biology and Medicine 2025年 186卷 109596-109596页

作者： Patrascu, Adrian Ion, Andreea Vislapuu, Maarja Husebo, Bettina S. Tache, Irina Andra Reithe, Haakon Patrascu, Monica Centre for Interdisciplinary Research in Physical Education and Sport Babes-Bolyai University Cluj-Napoca Romania Complex Systems Laboratory University Politehnica of Bucharest Bucharest Romania Centre for Elderly and Nursing Home Medicine University of Bergen Bergen Norway Department of Nursing VID Specialized University Bergen Norway Neuro-SysMed Center University of Bergen Bergen Norway Department of Automatic Control and Systems Engineering University Politehnica of Bucharest Bucharest Romania Department of Image Fusion and Analytics Siemens SRL Brasov Romania

Heart rate response to physical activity is widely investigated in clinical and training practice, as it provides information on a person's physical state. For emerging digital phenotyping approaches, there is a need for individualized model estimation. In this study, we propose a zero-poles model and a data-driven evolutionary learning method for identification. We also perform a comparison with existing first and second order models and gradient descent identification methods. The proposed model is based on a five-phase description of heart rate dynamics. Data was collected from 30 healthy participants using a treadmill and a thoracic sensor in two protocols (static and dynamic), for increasing and decreasing activity. Results show that the zero-poles model is a good fit for heart rate response to exercise (Pearson's coefficient ρ>.95), while first and second order models are also suitable (ρ>.92). The evolutionary learning method shows excellent results for fast model identification, in comparison with least-squares methods (p © 2024 The Author(s)

关键词： Contrastive Learning

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Performance and emissions of gas turbine engines fueled with karanja oil-based biofuel blends: a machine learning approach using Lasso regression

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Aircraft engineering and Aerospace Technology 2025年

作者： Aldehim, Ghadah Allafi, Randa Alkharashi, Abdulwhab A, Sagai Francis Britto J, Vijayalakshmi A, Sumaiya Begum M.C, Sashikkumar Velusamy, Priya Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh Saudi Arabia Department of Computer Science College of Science Northern Border University Arar Saudi Arabia Department of Computer Science College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia Department of Mechanical Engineering Rohini College of Engineering and Technology Palkulam India Department of Electronics and Communication Engineering Kongu Engineering College Perundurai India Department of Electronics and Communication Engineering R.M.D. Engineering College Chennai India Department of Civil Engineering Centre for Water Resources Anna University Chennai India Department of Civil Engineering GMR Institute of Technology Rajam India

Purpose: This study aims to investigate the performance and emission characteristics of gas turbine engines operating on biofuel blends derived from karanja oil as a potential alternative to conventional Jet-A fuel. Design/methodology/approach: The study tested three biofuel blends: JA20 (20% karanja oil, 80% Jet-A), JA30 (30% karanja oil, 70% Jet-A) and JA40 (40% karanja oil, 60% diesel). Engine performance parameters, including thrust output, thrust-specific fuel consumption (TSFC) and turbine inlet temperature (TIT), were measured at engine speeds ranging from 30,000 to 80,000 rpm. Exhaust emissions of carbon monoxide (CO), carbon dioxide (CO2) and nitrogen oxides (NOx) were analyzed using a gas analyzer. Additionally, a Lasso regression model was used to predict micro gas turbine (MGT) performance and emissions based on experimental data. Findings: Increasing the karanja oil content in the fuel blends reduced overall thrust output, increased TSFC and lowered TIT compared to Jet-A fuel. The JA40 blend exhibited the most significant performance reduction, with a 20% decrease in thrust output, a 7.5% increase in TSFC and a 4.1% decrease in TIT. However, biofuel blends consistently resulted in lower CO, CO2 and NOx emissions compared to Jet-A fuel, with reductions of up to 36%, 6.9% and 13.6%, respectively, for the JA40 blend. The Lasso regression model effectively captured the influence of engine speed and fuel blend composition on emissions, achieving an R² of 0.95 for CO2 and 0.94 for CO predictions. Originality/value: This study provides insights into the feasibility of using karanja oil-based biofuels in gas turbine engines, demonstrating their potential to reduce emissions while highlighting tradeoffs in performance. The use of a Lasso regression model for predicting emissions offers a novel approach to assessing biofuel performance in MGTs. © 2025, Emerald Publishing Limited.

关键词： Polynomial regression

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Switching Multiplicative Watermark Design Against Covert Attacks

arXiv

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arXiv 2025年

作者： Gallo, Alexander J. Anand, Sribalaji C. Teixeira, Andre M.H. Ferrari, Riccardo M.G. Department of Electronics Information and Bioengineering Politecnico di Milano Milano Italy School of Electrical Engineering and Computer Science and Digital Futures KTH Royal Institute of Technology Sweden Department of Information Technology Uppsala University PO Box 337 UppsalaSE-75105 Sweden Delft Center for Systems and Control Mechanical Engineering TU Delft Delft Netherlands

Active techniques have been introduced to give better detectability performance for cyber-attack diagnosis in cyber-physical systems (CPS). In this paper, switching multiplicative watermarking is considered, whereby we propose an optimal design strategy to define switching filter parameters. Optimality is evaluated exploiting the so-called output-to-output gain of the closed loop system, including some supposed attack dynamics. A worst-case scenario of a matched covert attack is assumed, presuming that an attacker with full knowledge of the closed-loop system injects a stealthy attack of bounded energy. Our algorithm, given watermark filter parameters at some time instant, provides optimal next-step parameters. Analysis of the algorithm is given, demonstrating its features, and demonstrating that through initialization of certain parameters outside of the algorithm, the parameters of the multiplicative watermarking can be randomized. Simulation shows how, by adopting our method for parameter design, the attacker’s impact on performance diminishes. © 2025, CC BY-NC-ND.

关键词： Networked control systems

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Constrained Optimization with Decision-Dependent Distributions

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IEEE Transactions on Automatic control 2025年

作者： Wang, Zifan Liu, Changxin Parisini, Thomas Zavlanos, Michael M. Johansson, Karl H. KTH Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Sweden Digital Futures StockholmSE-10044 Sweden East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education Shanghai200237 China Imperial College London Department of Electrical and Electronic Engineering LondonSW7 2AZ United Kingdom Aalborg University Department of Electronic Systems Denmark University of Trieste Department of Engineering and Architecture Italy Duke University Department of Mechanical Engineering and Materials Science DurhamNC United States

In this paper we deal with stochastic optimization problems where the data distributions change in response to the decision variables. Traditionally, the study of optimization problems with decision-dependent distributions has assumed either the absence of constraints or fixed constraints. This work considers a more general setting where the constraints can also dynamically adjust in response to changes in the decision variables. Specifically, we consider linear constraints and analyze the effect of decision-dependent distributions in both the objective function and constraints. Firstly, we establish a sufficient condition for the existence of a constrained equilibrium point, at which the distributions remain invariant under retraining. Moreover, we propose and analyze two algorithms: repeated constrained optimization and repeated dual ascent. For each algorithm, we provide sufficient conditions for convergence to the constrained equilibrium point. Furthermore, we explore the relationship between the equilibrium point and the optimal point for the constrained decision-dependent optimization problem. Notably, our results encompass previous findings as special cases when the constraints remain fixed. To show the effectiveness of our theoretical analysis, we provide numerical experiments on both a market problem and a dynamic pricing problem for parking based on real-world data. © 1963-2012 IEEE.

关键词： Optimization algorithms

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