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Energy-balancing dual-port grid-forming control for VSC-HVDC systems

IFAC-PapersOnLine 2023年第2期56卷 6447-6452页

作者： Irina Subotić Dominic Groß Automatic Control Laboratory ETH Zürich Switzerland Department of Electrical and Computer Engineering University of Wisconsin-Madison USA

This work examines energy-balancing dual port grid-forming (GFM) control for high-voltage direct current (HVDC) transmission. In contrast to the state-of-the-art, HVDC converters controlled in this way do not require assigning GFM and grid-following roles to different converters. Moreover, this control enables primary frequency control and inertia support through HVDC links. A detailed stability and steady-state analysis results in conditions on the control gains such that i) the overall hybrid dc/ac system is stable, ii) asynchronous ac areas are quasi-synchronous, and iii) circulating power in cyclic topologies is avoided. Finally, a high-fidelity case study is used to illustrate and verify the analytical results.

关键词： Power systems Power systems stability Grid-forming control HVDC transmission

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Enhanced cyber-physical system with semantic technologies and machine learning to support smart farming

Enhanced cyber-physical system with semantic technologies an...

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International Conference on control systems and computer Science (CSCS)

作者： Sorin N. Ciolofan Monica Drăgoicea Daniel-Cătălin Popeangă Department of Computer Science Politehnica University of Bucharest Bucharest Romania Department of Automatic Control and Systems Engineering Politehnica University of Bucharest Bucharest Romania

An important aspect related to the effects of agricultural activities on the environment is represented by the nutrient loss in water and air (specifically nitrogen). The interactions between catchments hydrological processes, management of farm activities, climate changes and nitrogen losses constitute a complex phenomenon yet not well understood, being an important concern from the sustainable agriculture perspective. Nitrogen can be lost with water as leaching or runoff, or as gas as ammonia volatilization. Nitrous oxide (N2O) is particularly problematic because it is also a powerful greenhouse gas. The goal of the current article is to present innovative digital techniques to advance in understanding of this phenomena through an Information System that integrates Artificial Intelligence techniques such as Semantic Technologies and Machine Learning (ML) into Cyber-Physical systems (CPS) to support smart farming and sustainable agriculture.

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Scalable and Low-Cost Remote Lab Platforms: Teaching Industrial Robotics Using Open-Source Tools and Understanding Its Social Implications 16th

Scalable and Low-Cost Remote Lab Platforms: Teaching Indust...

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16th International Conference on Social Robotics, ICSR + AI 2024

作者： Kumar, Amit Jose, Jaison Jain, Archit Kulkarni, Siddharth Arya, Kavi Centre for Systems and Control Indian Institute of Technology Bombay Mumbai India Embedded Real-Time Systems / e-Yantra Lab Indian Institute of Technology Bombay Mumbai India Computer Science and Engineering Department Indian Institute of Technology Bombay Mumbai India

ISBN: (纸本)9789819635214

With recent advancements in industrial robots, educating students in new technologies and preparing them for the future is imperative. However, access to industrial robots for teaching poses challenges, such as the high cost of acquiring these robots, the safety of the operator and the robot, and complicated training material. This paper proposes two low-cost platforms built using open-source tools like Robot Operating System (ROS) and its latest version ROS 2 to help students learn and test algorithms on remotely connected industrial robots. Universal Robotics (UR5) arm and a custom mobile rover were deployed in different life-size testbeds, a greenhouse, and a warehouse to create an Autonomous Agricultural Harvester System (AAHS) and an Autonomous Warehouse Management System (AWMS). These platforms were deployed for a period of 7 months and were tested for their efficacy with 1,433 and 1,312 students, respectively. The hardware used in AAHS and AWMS was controlled remotely for 160 and 355 hours, respectively, by students over a period of 3 months. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Costs

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Differential gene correlation analysis to investigate evolutionary divergence between Cardamine hirsuta and Arabidopsis thaliana

Differential gene correlation analysis to investigate evolut...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： D'Annunzio, Federica Fiscon, Giulia Bertolotti, Gaia Dello Ioio, Raffaele Paci, Paola Sapienza University of Rome Department of Translational and Precision Medicine Rome Italy Sapienza University of Rome Institute for Systems Analysis and Computer Science 'A. Ruberti' National Research Council of Rome Department of Computer Control and Management Engineering 'A. Ruberti' Rome Italy Sapienza University of Rome Department of Biology and Biotechnology 'Charles Darwin' Rome Italy Department of Computer Control and Management Engineering 'A. Ruberti' Sapienza University of Rome Rome Italy Karolinska Institutet Stockholm Sweden

ISBN: (纸本)9798350386226

Dissecting the intricate regulatory dynamics between genes stands as a critical step towards the development of precise predictive models within biological systems. A highly effective strategy in this pursuit involves an examination of variations in correlation among gene pairs across different conditions trough differential gene correlation analysis. We anticipated that the application of this method to phylogenetically close plant species could reveal powerful insights on how genes' relationships have a fundamental role in evolutionary dynamics. Building upon this principle, our investigation focused primarily on conducting a comprehensive differential correlation analysis of the roots' orthologous genes of two closely related plant species: Cardamine hirsuta and Arabidopsis thaliana. By leveraging their shared phylogenetic proximity, we were able to draw comparisons that shed light on how evolutionary pressures might have shaped their gene regulatory networks. By integrating differential expression analysis, differential correlation analysis and functional enrichment analysis, we offer a robust framework for future studies aiming to unravel the complexities of adaptation and evolution across a wide range of biological systems. © 2024 IEEE.

关键词： Plant diseases

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Traffic Sign Detection With Event Cameras and DCNN 25

Traffic Sign Detection With Event Cameras and DCNN

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25th IEEE Signal Processing: Algorithms, Architectures, Arrangements, and Applications, SPA 2022

作者： Wzorek, Piotr Kryjak, Tomasz Embedded Vision Systems Group Agh University of Science and Technology Computer Vision Laboratory Department of Automatic Control and Robotics Kraków Poland Silesian University of Technology Department of Digital Systems Gliwice Poland

ISBN: (数字)9788362065424

ISBN: (纸本)9788362065424

In recent years, event cameras (DVS - Dynamic Vision Sensors) have been used in vision systems as an alternative or supplement to traditional cameras. They are characterised by high dynamic range, high temporal resolution, low latency, and reliable performance in limited lighting conditions - parameters that are particularly important in the context of advanced driver assistance systems (ADAS) and self-driving cars. In this work, we test whether these rather novel sensors can be applied to the popular task of traffic sign detection. To this end, we analyse different representations of the event data: event frame, event frequency, and the exponentially decaying time surface, and apply video frame reconstruction using a deep neural network called FireNet. We use the deep convolutional neural network YOLOv4 as a detector. For particular representations, we obtain a detection accuracy in the range of 86.9-88.9% mAP@0.5. The use of a fusion of the considered representations allows us to obtain a detector with higher accuracy of 89.9% mAP@0.5. In comparison, the detector for the frames reconstructed with FireNet is characterised by an accuracy of 72.67% mAP@0.5. The results obtained illustrate the potential of event cameras in automotive applications, either as standalone sensors or in close cooperation with typical frame-based cameras. © 2022 Division of Signal Processing and Electronic systems, Poznan University of Technology (DSPES PUT).

关键词： Cameras

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Tabular Dyna-Q Algorithm for Online Calculation of LQR Gains: A Simulation Study 9

Tabular Dyna-Q Algorithm for Online Calculation of LQR Gains...

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9th International Conference on engineering and Emerging Technology, ICEET 2023

作者： Farooghi, Kian Samadi, Mahdieh Khaloozadeh, Hamid K. N. Toosi University of Technology Department of Systems and Control Tehran Iran University of Tabriz Faculty of Electrical and Computer Engineering Tabriz Iran

ISBN: (纸本)9798350316926

This paper presents a novel approach for the online calculation of Linear Quadratic Regulator (LQR) gains using the Tabular Dyna-Q algorithm. By leveraging Q-learning, this technique enables the determination of gains for unknown dynamic systems by using input-output data only. The proposed algorithm is particularly advantageous for situations where acquiring input-output data is expensive or time-consuming, as it effectively reduces computation time by reusing previous data. To validate the accuracy of the calculations and control scheme, a simulation example is provided. The results indicate the effectiveness of the Tabular Dyna-Q algorithm in accurately computing LQR gains and its potential application in real-world scenarios. This research contributes to advancing the field of control theory by offering an efficient and reliable method for online gain calculation in dynamic systems. © 2023 IEEE.

关键词： Reinforcement learning

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Adaptive Stochastic Predictive control from Noisy Data: A Sampling-based Approach

Adaptive Stochastic Predictive Control from Noisy Data: A Sa...

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IEEE Conference on Decision and control

作者： Johannes Teutsch Christopher Narr Sebastian Kerz Dirk Wollherr Marion Leibold Department of Computer Engineering Chair of Automatic Control Engineering (LSR) Technical University of Munich Theresienstr. 90 Munich Germany

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

In this work, an adaptive predictive control scheme for linear systems with unknown parameters and bounded additive disturbances is proposed. In contrast to related adaptive control approaches that robustly consider the parametric uncertainty, the proposed method handles all uncertainties stochastically by employing an online adaptive sampling-based approximation of chance constraints. The approach requires initial data in the form of a short input-output trajectory and distributional knowledge of the disturbances. This prior knowledge is used to construct an initial set of dataconsistent system parameters and a distribution that allows for sample generation. As new data stream in online, the set of consistent system parameters is adapted by exploiting set membership identification. Consequently, chance constraints are deterministically approximated using a probabilistic scaling approach by sampling from the set of system parameters. In combination with a robust constraint on the first predicted step, recursive feasibility of the proposed predictive controller and closed-loop constraint satisfaction are guaranteed. A numerical example demonstrates the efficacy of the proposed method.

关键词： Robust control Linear systems Uncertainty Shape Probabilistic logic Trajectory Noise measurement Synthetic aperture sonar Predictive control Convergence

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Out-of-distribution Object Detection through Bayesian Uncertainty Estimation

arXiv

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arXiv 2023年

作者： Zhang, Tianhao Wang, Shenglin Bouaynaya, Nidhal Calinescu, Radu Mihaylova, Lyudmila Automatic Control and Systems Engineering The University of Sheffield United Kingdom Department of Electrical and Computer Engineering Rowan University United States Department of Computer Science University of York United Kingdom

The superior performance of object detectors is often established under the condition that the test samples are in the same distribution as the training data. However, in many practical applications, out-of-distribution (OOD) instances are inevitable and usually lead to uncertainty in the results. In this paper, we propose a novel, intuitive, and scalable probabilistic object detection method for OOD detection. Unlike other uncertainty-modeling methods that either require huge computational costs to infer the weight distributions or rely on model training through synthetic outlier data, our method is able to distinguish between in-distribution (ID) data and OOD data via weight parameter sampling from proposed Gaussian distributions based on pre-trained networks. We demonstrate that our Bayesian object detector can achieve satisfactory OOD identification performance by reducing the FPR95 score by up to 8.19% and increasing the AUROC score by up to 13.94% when trained on BDD100k and VOC datasets as the ID datasets and evaluated on COCO2017 dataset as the OOD dataset. Copyright © 2023, The Authors. All rights reserved.

关键词： Object detection

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Output Tracking control of a Nonlinear System Based on Takagi-Sugeno Fuzzy Model: Generalized Partial Eigenstructure Assignment Approach

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IEEE Access 2024年 12卷 18520-18535页

作者： Soltanian, Farzad Shasadeghi, Mokhtar Mobayen, Saleh Skruch, Pawel University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2M9 Canada Shiraz University of Technology Electrical Engineering Faculty Shiraz71557-13876 Iran National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan AGH University of Science and Technology Department of Automatic Control and Robotics Kraków30-059 Poland

This paper introduces an innovative optimal control approach to achieve output tracking while incorporating H2-performance specifications in a specific class of nonlinear dynamics modeled by the Takagi-Sugeno fuzzy model (TSFM). The primary innovation lies in extending partial eigenstructure assignment to TSFM-based nonlinear systems within the framework of sliding mode control (SMC). We propose a two-step methodology for designing optimal sliding surface gains. Initially, optimal state feedback gains are computed for each rule consequence containing a linear subsystem, adhering to predetermined eigenvalues and satisfying H2-performance criteria. Subsequently, using a convex combination, the overall state feedback gain is calculated and utilized to design sliding matrix gains. The sliding matrix gains are then determined by strategically combining previously calculated state-feedback gains in a convex optimization problem. We reframe the output tracking strategy as a stabilization problem using a virtual control input and reformulate the optimization task concerning tracking state errors. This process yields state feedback gains, sliding gains, and the formulation of the virtual control input. The effectiveness of our approach is verified through comprehensive simulations, emphasizing its capability in addressing output tracking challenges within nonlinear systems. © 2013 IEEE.

关键词： Transient analysis

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Decentralised Iterative Learning control for High Performance Collaborative Tracking Problem with Output Constraints

Decentralised Iterative Learning Control for High Performanc...

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UKACC International Conference on control (control)

作者： Bin Chen Bing Chu Department of Automatic Control and Systems Engineering University of Sheffield Sheffield U.K. School of Electronics and Computer Science University of Southampton Southampton U.K.

ISBN: (数字)9798350374261

ISBN: (纸本)9798350374278

High performance collaborative tracking problem, requiring a group of independent subsystems to generate a global output that can precisely track the desired reference in a repetitive manner, has found lots of applications in practice. However, for such an important control task, existing iterative learning control (ILC) methods have not considered the constraint on each subsystem's output, which leads to potential risk within the control process. This paper proposes a novel optimisation based ILC method to address the high performance collaborative tracking problem with output constraints. The proposed ILC framework can guarantee not only each subsystem's output constraint is always satisfied during the control process, but also the monotonic convergence of a well-defined performance index to a possibly minimum value. To avoid huge computational complexity for large scale systems, we further apply the idea of the alternative direction method of multipliers (ADMM) to implement the proposed ILC frame-work in a decentralised manner, which allows the resulting decentralised methods to be applied to large scale and changing systems. Moreover, the decentralised ILC method proposed in this paper is suitable for non-minimum phase, heterogeneous and/or homogeneous systems, which is appealing in practice. Convergence properties of the proposed ILC algorithms are analysed rigorously, and numerical examples are given to demonstrate the algorithms' effectiveness.

关键词： Collaboration Process control Performance analysis Large-scale systems Task analysis Computational complexity Optimization

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