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Acta Metallurgica Sinica(English Letters) 2023年第9期36卷 1511-1522页

作者： Kun Yi Siqi Xiang Mengcheng Zhou Xinfang Zhang Furui Du School of Metallurgical and Ecological Engineering University of Science and Technology BeijingBeijing100083China State Key Laboratory of Automatic Control Technology for Mining and Metallurgy Process Beijing102628China

Residual stress in high-carbon steel affects the dimensional accuracy, structural stability, and integrity of components. Although the evolution of residual stress under an electric field has received extensive attention, its elimination mechanism has not been fully clarified. In this study, it was found that the residual stress of high-carbon steel could be effectively relieved within a few minutes through the application of a low density pulse current. The difference between the current pulse treatment and traditional heat treatment in reducing residual stress is that the electric pulse provides additional Gibbs free energy for the system, which promotes dislocation annihilation and carbon atom diffusion to form carbides, thus reducing the free energy of the system. The electroplastic and thermal effects of the pulse current promoted the movement of dislocations under the electric field, thus eliminating the internal stress caused by dislocation entanglement. The precipitation of carbides reduced the carbon content of the steel matrix and lattice shrinkage, thereby reducing the residual tensile stress. Considering that a pulsed current has the advantages of small size, small power requirement, continuous output, and continuously controllable parameters, it has broad application prospects for eliminating residual stress.

关键词： Residual stress Pulsed electric current Carbide precipitation

New Hysteresis control Block for Shunt Active Power Filter for Harmonics Compensation Generated by CFLs with PQ Theory 1

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2nd International Conference on Green Energy Conversion System, ICGECS 2023

作者： Hajjej, Mohamed Sbita, Lassaad El Aroudi, Abdelali Mandal, Kuntal Research Laboratory of Process Energies Environment and Electrical Systems National Engineering School of Gabes University of Gabes Zrig Eddakhlania6029 Tunisia Department of Electronics Electrical Engineering and Automatic Control Universitat Rovira i Virgili Tarragona Spain

ISBN: (数字)9789819761487

ISBN: (纸本)9789819761470

This paper proposes a New Modulation Hysteresis control block for a three phase four wire Shunt Active Power Filter (SAPF) for currents harmonics compensation generated by Compact Fluorescents Lamps (CFLs). This study employs the PQ power control strategy. The performance of the SAPF is evaluated under both simple hysteresis controller and under the new modulation hysteresis controller conditions using the Matlab/Simulink platform. The findings simulation results demonstrate that the proposed new technique can improve the power quality of the main, and the total harmonic distortion (THD) optimization, which also confirms the effectiveness of the approach. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Harmonic analysis

Distributed State Estimation for Linear Time-Varying Systems with Sensor Network Delays

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Distributed State Estimation for Linear Time-Varying Systems...

European control Conference (ECC)

作者： Sanjay Chandrasekaran Vishnu Varadan Siva Vignesh Krishnan Florian Dörfler Mohammad H. Mamduhi Automatic Control Laboratory ETH Zurich Department of Mechanical & Process Engineering ETH Zurich

Distributed sensor networks often include a multitude of sensors, each measuring parts of a process state space or observing the operations of a system. Communication of measurements between the sensor nodes and estimator(s) cannot realistically be considered delay-free due to communication errors and transmission latency in the channels. We propose a novel stability-based method that mitigates the influence of sensor network delays in distributed state estimation for linear time-varying systems. Our proposed algorithm efficiently selects a subset of sensors from the entire sensor nodes in the network based on the desired stability margins of the distributed Kalman filter estimates, after which, the state estimates are computed only using the measurements of the selected sensors. We provide comparisons between the estimation performance of our proposed algorithm and a greedy algorithm that exhaustively selects an optimal subset of nodes. We then apply our method to a simulative scenario for estimating the states of a linear time-varying system using a sensor network including 2000 sensor nodes. Simulation results demonstrate the performance efficiency of our algorithm and show that it closely follows the achieved performance by the optimal greedy search algorithm

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Simulation Analysis and Model Modification of the LWR Traffic Flow Theory

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Simulation Analysis and Model Modification of the LWR Traffi...

IEEE Workshop on Advanced Robotics and its Social Impacts, ARSO

作者： Yang Chen Jun Fu Anton Selivanov State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China Department of Automatic Control and Systems Engineering University of Sheffield UK

ISBN: (数字)9798350344639

ISBN: (纸本)9798350344646

The surge in urbanization and ensuing traffic congestion underscores the imperative for contemporary traffic flow control models. This paper explores the Lighthill-Whitham-Richards (LWR) theory in the macroscopic domain of traffic flow control. It evaluates the accuracy and mathematical logic of simulation performance of related models, namely Greenshields, Newell-Daganzo, Greenberg, Underwood, and California. This paper employs thorough simulations that utilize a holistic three-dimensional(3D) analysis based on time, location, and density. It provides a comprehensive comparison and discusses specific features of each model. Additionally, a modification to the macroscopic model is proposed. The modified model overcomes the shortcomings of performing well only in some specific short ranges and achieves a full range of application effects. The study offers valuable guidance for real-life scenario selections and applications in the traffic domain.

关键词： Analytical models Accuracy Urban areas Transportation Predictive models Mathematical models Numerical models

Distributed State Estimation for Linear Time-Varying Systems with Sensor Network Delays

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arXiv 2023年

作者： Chandrasekaran, Sanjay Varadan, Vishnu Krishnan, Siva Vignesh Dörfler, Florian Mamduhi, Mohammad H. The Automatic Control Laboratory ETH Zürich Switzerland The Department of Mechanical & Process Engineering ETH Zürich Switzerland

Distributed sensor networks often include a multitude of sensors, each measuring parts of a process state space or observing the operations of a system. Communication of measurements between the sensor nodes and estimator(s) cannot realistically be considered delay-free due to communication errors and transmission latency in the channels. We propose a novel stability-based method that mitigates the influence of sensor network delays in distributed state estimation for linear time-varying systems. Our proposed algorithm efficiently selects a subset of sensors from the entire sensor nodes in the network based on the desired stability margins of the distributed Kalman filter estimates, after which, the state estimates are computed only using the measurements of the selected *** provide comparisons between the estimation performance of our proposed algorithm and a greedy algorithm that exhaustively selects an optimal subset of nodes. We then apply our method to a simulative scenario for estimating the states of a linear time-varying system using a sensor network including 2000 sensor nodes. Simulation results demonstrate the performance efficiency of our algorithm and show that it closely follows the achieved performance by the optimal greedy search algorithm. Copyright © 2023, The Authors. All rights reserved.

关键词： Sensor nodes

Physics-informed neural networks for parameter learning of wildfire spreading

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arXiv 2024年

作者： Vogiatzoglou, Konstantinos Papadimitriou, C. Bontozoglou, V. Ampountolas, K. System Dynamics Laboratory Department of Mechanical Engineering University of Thessaly Volos38334 Greece Transport Processes & Process Equipment Laboratory Department of Mechanical Engineering University of Thessaly Volos38334 Greece Automatic Control & Autonomous Systems Laboratory Department of Mechanical Engineering University of Thessaly Volos38334 Greece

Wildland fires pose a terrifying natural hazard, underscoring the urgent need to develop datadriven and physics-informed digital twins for wildfire prevention, monitoring, intervention, and response. In this direction of research, this work introduces a physics-informed neural network (PiNN) designed to learn the unknown parameters of an interpretable wildfire spreading model. The considered modeling approach integrates fundamental physical laws articulated by key model parameters essential for capturing the complex behavior of wildfires. The proposed machine learning framework leverages the theory of artificial neural networks with the physical constraints governing wildfire dynamics, including the first principles of mass and energy conservation. Training of the PiNN for physics-informed parameter identification is realized using synthetic data on the spatiotemporal evolution of one- and two-dimensional firefronts, derived from a high-fidelity simulator, as well as empirical data (ground surface thermal images) from the Troy Fire that occurred on June 19, 2002, in California. The parameter learning results demonstrate the predictive ability of the proposed PiNN in uncovering the unknown coefficients of the wildfire model in one- and two-dimensional fire spreading scenarios as well as the Troy Fire. Additionally, this methodology exhibits robustness by identifying the same parameters even in the presence of noisy data. By integrating this PiNN approach into a comprehensive framework, the envisioned physics-informed digital twin will enhance intelligent wildfire management and risk assessment, providing a powerful tool for proactive and reactive strategies. Copyright © 2024, The Authors. All rights reserved.

关键词： Risk assessment

Layer-to-Layer Closed-Loop Feedback control Application for Inter-Layer Temperature Stabilization in Laser Powder Bed Fusion

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SSRN

SSRN 2023年

作者： Kavas, Barış Balta, Efe C. Tucker, Michael Rupenyan, Alisa Lygeros, John Bambach, Markus Department of Mechanical and Process Engineering Advanced Manufacturing Laboratory ETH Zürich Zürich8092 Switzerland Department of Information Technology and Electrical Engineering Automatic Control Laboratory ETH Zürich Zürich8092 Switzerland

In laser powder bed fusion (LPBF), part quality and process conditions are highly dependent on the interlayer temperature (ILT) of the exposure surface of printed parts. State-of-the-art closed-loop control applications do not stabilize ILT, which has a decisive influence on the resulting part properties. This paper presents a closed-loop control strategy for LPBF that stabilizes the ILT. The thermal balance is defined with respect to exposure area change in overhanging parts and a control strategy is proposed to maintain the thermal balance by actuating energy input. The proposed control strategy uses an off-axis thermal camera for interlayer temperature acquisition to update the laser power layer to layer. An online optimization-based controller that leverages the repetitive nature of the LPBF process is implemented for the task. The developed controller is validated by experiments with different overhanging geometries. Successful ILT stabilization for various overhanging angles shows the robustness of the controller for an extended number of layers. However, modulation of laser power alone is shown to be insufficient to maintain the ILT within the stable processing window due to excessive heat accumulation in the long term. Nevertheless, the results of this study demonstrate the feasibility of ILT stabilization via of closed-loop control in LPBF and the potential for significant improvements in stabilizing the properties of the printed parts. © 2023, The Authors. All rights reserved.

关键词： Temperature control

Data-Driven Source Term Estimation of Hazardous Gas Leakages Under Variable Meteorological Conditions

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SSRN

SSRN 2024年

作者： Ni, Chuantao Lang, Ziqiang Wang, Bing Li, Ang Cao, Chenxi Du, Wenli Qian, Feng Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology Shanghai200237 China Department of Automatic Control and System Engineering University of Sheffield SheffieldS1 3JD United Kingdom

Source term estimation (STE) of hazardous gas leakages in chemical industrial parks (CIPs) is important for addressing environmental pollution and improving engineering safety and reliability. For this purpose, some least squares-based STE methods have recently been developed, which performs the real-time STE using an off-line determined response matrix that represents the relationship between the sensor measurements and strengths of hazardous gas leakages. However, these methods require the number and locations of potential hazardous gas leakage sources are known a priori, which is difficult in many practical applications. To resolve this issue, in the present study, a novel multi-sensor data-driven STE (MSDD-STE) approach is proposed, which overcomes the difficulties with existing least squares-based STE methods and can, for the first time, address the MSDD-STE problems in complicated scenarios where meteorological conditions such as wind directions change over a considerable range. A detailed analysis is introduced to evaluate the performance of the proposed approach. The effectiveness of the proposed approach is verified by comprehensive numerical simulation studies. © 2024, The Authors. All rights reserved.

关键词： Least squares approximations

PAGE-PG: A Simple and Loopless Variance-Reduced Policy Gradient Method with Probabilistic Gradient Estimation

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arXiv 2022年

作者： Gargiani, Matilde Zanelli, Andrea Martinelli, Andrea Summers, Tyler Lygeros, John Automatic Control Laboratory ETH Zurich Switzerland Department of Mechanical and Process Engineering ETH Zurich Switzerland Department of Mechanical Engineering University of Texas Dallas United States

Despite their success, policy gradient methods suffer from high variance of the gradient estimate, which can result in unsatisfactory sample complexity. Recently, numerous variance-reduced extensions of policy gradient methods with provably better sample complexity and competitive numerical performance have been proposed. After a compact survey on some of the main variance-reduced REINFORCE-type methods, we propose ProbAbilistic Gradient Estimation for Policy Gradient (PAGE-PG), a novel loopless variance-reduced policy gradient method based on a probabilistic switch between two types of updates. Our method is inspired by the PAGE estimator for supervised learning and leverages importance sampling to obtain an unbiased gradient estimator. We show that PAGE-PG enjoys a O(∊−3) average sample complexity to reach an ∊-stationary solution, which matches the sample complexity of its most competitive counterparts under the same setting. A numerical evaluation confirms the competitive performance of our method on classical control tasks. © 2022, CC BY.

关键词： Importance sampling

In-situ controller Autotuning by Bayesian Optimization for Closed-loop Feedback control of Laser Powder Bed Fusion process

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arXiv 2024年

作者： Kavas, Barış Balta, Efe C. Tucker, Michael R. Krishnadas, Raamadaas Rupenyan, Alisa Lygeros, John Bambach, Markus Department of Mechanical and Process Engineering ETH Zürich Zürich8092 Switzerland Control and Automation Group Inspire AG Zürich8005 Switzerland Department of Information Technology and Electrical Engineering Automatic Control Laboratory ETH Zürich Zürich8092 Switzerland ZHAW Centre for Artificial Intelligence Zürich University of Applied Sciences Switzerland

Open-loop control of laser powder bed fusion (LPBF) additive manufacturing (AM) has enabled the industrial production of complex and high-criticality parts for aerospace, power generation, medical, transportation, and other industries. This approach relies on static parameter sets obtained through extensive experimentation and a priori simulation on analog parts, with the hope that they remain stable and defect-free once transferred to the production parts. Closed-loop control of LPBF has the potential to enhance process stability further and reduce defect formation in the face of complex thermal histories, stochastic process noise, hardware drift, and unexpected perturbations. The controllers can be classified based on the spatial and temporal scales in which they operate, designated as layer-to-layer and in-layer controllers. However, the performance and effectiveness of controllers largely depend on the tuning of their parameters. Traditionally, controller tuning has been a manual, expertise-driven process that does not guarantee optimal controller performance and is often constrained by the non-transferability of settings between different systems. This study proposes the use of Bayesian Optimization (BO), a sample-efficient algorithm, to automate the tuning of an in-layer controller by leveraging the layer-to-layer repetitive nature of the LPBF process. Two alternative approaches are introduced: online tuning, which adjusts parameters iteratively during the process, and offline tuning, conducted in a representative setup such as laser exposures on a bare metal plate. The proposed methods are experimentally implemented on an in-layer PI controller and the performance of the resulting tuned controllers is investigated on two different wedge geometries that are prone to overheating. The results demonstrate that BO effectively tunes controllers using either method, where both significantly reduced overheating in controlled wedge specimens compared to those uncontro

关键词： controllers