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arXiv 2024年

作者： Breukelman, Enno Hall, Sophie Belgioioso, Giuseppe Dörfler, Florian The KTH Royal Institute of Technology School of Electrical Engineering and Computer Science Division of Decision and Control Systems Malvinas väg 10 StockholmSE-100 44 Sweden The Automatic Control Laboratory Department of Electrical Engineering and Information Technology ETH Zurich Physikstrasse 3 Zürich8092 Switzerland

Over the past decade, the continuous surge in cloud computing demand has intensified data center workloads, leading to significant carbon emissions and driving the need for improving their efficiency and sustainability. This paper focuses on the optimal allocation problem of batch compute loads with temporal and spatial flexibility across a global network of data centers. We propose a bilevel game-theoretic solution approach that captures the inherent hierarchical relationship between supervisory control objectives, such as carbon reduction and peak shaving, and operational objectives, such as priority-aware scheduling. Numerical simulations with real carbon intensity data demonstrate that the proposed approach successfully reduces carbon emissions while simultaneously ensuring operational reliability and priority-aware scheduling. © 2024, CC BY-NC-ND.

关键词： Carbon

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Fault Diagnosis based on Behavior and State-Awareness Digital Twin of Key Equipment in Power Supply System

Fault Diagnosis based on Behavior and State-Awareness Digita...

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Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS), CAA Symposium on

作者： Haifei Liu Qin Zhao Jie Hao Laifa Tao Chen Lu Institute of Reliability Engineering Beihang University Beijing China Science & Technology on Reliability & Environmental Engineering Laboratory Beijing China School of Reliability and Systems Engineering Beihang University Beijing China AVIC Xi’an Flight Automatic Control Research Institute Xi’an China

With the rapid development of multi-electric and all-electric aircraft, the role of power supply systems in aircraft is becoming increasingly prominent. Traditional fault diagnosis methods have problems such as a single means of sensory modelling and unbalanced fault data. The rapid development of digital twin technology provides an opportunity to overcome these difficulties. However, how to achieve adaptive updating and how to improve the data-and-model-fusion capabilities are also urgent challenges to be solved. To address the lack of existing research, this paper combines time-frequency domain analysis, physical information neural network based on differential-algebraic equation (DAE-PINN) and transient stability analysis of the power supply system based on a digital twin model with dimensions including fault-behavior-twin and (FBT) fault-state-awareness-twin (FSAT). Ultimately, this paper achieves effective digital twin modelling, fault eigenfeature extraction and high accuracy fault diagnosis, reaching fault data balance and complete state characterization and adaptive update of the diagnostic model.

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Analysis of fast prototyping of microcontroller-based ML software for acoustic signal classification

Analysis of fast prototyping of microcontroller-based ML sof...

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Signal Processing Algorithms, Architectures, Arrangements and Applications (SPA)

作者： Filip Marciniak Wojciech Marciniak Tomasz Marciniak Faculty of Computing and Telecommunications Poznan University of Technology Poznań Poland DECYBEL Student Science Club Poznan University of Technology Poznań Poland Faculty of Automatic Control Robotics and Electrical Engineering Institute of Automatic Control and Robotics Division of Electronic Systems and Signal Processing Poznan University of Technology Poznań Poland

The paper analyzes the preparation of software for acoustic signal classification with machine learning techniques for microcontrollers. The design process was tested for three types of devices: Nordic Thingy:53, *** and Arduino Nano 33 BLE Sense Lite. The classifier training process was carried out using the Edge Impulse platform. Experimental studies were carried out for the process of classifying sound signals generated by the vacuum cleaner motor. The results of the training and the model test were presented for different configurations.

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Multi-Condition Fault Diagnosis of Dynamic systems: A Survey, Insights, and Prospects

arXiv

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arXiv 2024年

作者： Han, Pengyu Liu, Zeyi He, Xiao Ding, Steven X. Zhou, Donghua The Department of Automation Tsinghua University Beijing100084 China The Institute for Automatic Control and Complex Systems University of Duisburg-Essen Duisburg47057 Germany The College of Electrical Engineering and Automation Shandong University of Science and Technology Qingdao266000 China

With the increasing complexity of industrial production systems, accurate fault diagnosis is essential to ensure safe and efficient system operation. However, due to changes in production demands, dynamic process adjustments, and complex external environmental disturbances, multiple operating conditions frequently arise during production. The multi-condition characteristics pose significant challenges to traditional fault diagnosis methods. In this context, multi-condition fault diagnosis has gradually become a key area of research, attracting extensive attention from both academia and industry. This paper aims to provide a systematic and comprehensive review of existing research in the field. Firstly, the mathematical definition of the problem is presented, followed by an overview of the current research status. Subsequently, the existing literature is reviewed and categorized from the perspectives of single-model and multi-model approaches. In addition, standard evaluation metrics and typical real-world application scenarios are summarized and analyzed. Finally, the key challenges and prospects in the field are thoroughly discussed. Copyright © 2024, The Authors. All rights reserved.

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Reinforcement Learning-Based Path Planning Approach for Unmanned Arial Vehicles

Reinforcement Learning-Based Path Planning Approach for Unma...

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Artificial Intelligence & Green Energy (ICAIGE), IEEE International Conference on

作者： Raja Jarray Soufiene Bouallègue Research Laboratory in Automatic Control (LARA) of National Engineering School of Tunis Tunis Tunisia High Institute of Industrial Systems of Gabès (ISSIG) University of Gabès Zrig Tunis Tunisia

Path planning plays an important role in autonomous flight control design for Unmanned Arial Vehicles (UAVs). In this paper, a path planning method based on the concepts of Reinforcement Learning (RL) is proposed. A Q-learning algorithm with a dynamic reward function is proposed and successfully implemented to ensure flyable paths with efficient collision avoidance. The dynamic reward function is introduced allowing the UAV to use the real-time distance between its current position and the destination as instructional data during training. Such an improved training mechanism leads to minimize the path length, reduce the learning cost, avoid the inefficient exploration of the search space, and ameliorate the convergence of the Q-planning algorithm. Many performance indicators are considered to evaluate the proposed Q-learning-based UAV's planner. Demonstrative results are implemented to illustrate the efficiency and practicability of the proposed artificial intelligence-based path planning approach.

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Exploiting symmetries in active set enumeration for constrained linear-quadratic optimal control

arXiv

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arXiv 2023年

作者： Mitze, Ruth Kvasnica, Michal Mönnigmann, Martin Automatic Control and Systems Theory Ruhr-Universität Bochum Bochum Germany Institute of Information Engineering Automation and Mathematics Faculty of Chemical and Food Technology Slovak University of Technology Bratislava Slovakia

This paper studies symmetric constrained linear-quadratic optimal control problems and their parametric solutions. The parametric solution of such a problem is a piecewise-affine feedback law that can be equivalently expressed as a set of active sets. We show symmetries of the optimal control problem entail symmetries of the active sets, which can be used to simplify finding the set of active sets considerably. Specifically, we improve a recently proposed method for the dynamic-programming-based enumeration of all active sets. The achieved reduction of the computational effort is illustrated with an example. © 2023, CC BY-NC-ND.

关键词： Quadratic programming

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Comparison of A* and D* Algorithms for 3D Path Planning of Unmanned Aerial Vehicles

Comparison of A* and D* Algorithms for 3D Path Planning of U...

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Artificial Intelligence & Green Energy (ICAIGE), IEEE International Conference on

作者： Samah Chattaoui Raja Jarray Soufiene Bouallègue Research Laboratory in Automatic Control (LARA) of National Engineering School of Tunis University of Tunis El Manar Tunis Tunisia Research Laboratory in Automatic Control (LARA) of National Engineering School of Tunis Le Belvédère 1002 High Institute of Industrial Systems of Gabè s University of Gabè s Campus Universitaire Tunis Tunisia

Finding an optimal and collision-free path from a source node to a target one in the presence of environmental obstacles is a challenging task, especially in missions involving Unmanned Aerial Vehicles (UAVs). These robots navigate through 3D space, following aerial waypoints to reach their destinations, adding complexity to the path planning problem. In this paper, a comparative study of two of the most popular geometric model search path planning approaches, i.e. A* and D* algorithms, is conducted in various navigation scenarios with progressive complexity and increased numbers of static obstacles. Demonstrative results in terms of path length, computational time and collision avoidance capability are performed over complex scenarios. By comparing the performance metrics of these algorithms, valuable insights can be gained regarding their efficiency and effectiveness in finding optimal paths for UAVs. This research study aims to provide a comprehensive understanding of the strengths and limitations of each algorithm, aiding in informed decision-making for path planning in 3D environments involving UAVs.

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Fuzzy Logic control of an Artificial Neural Network-Based Floating Offshore Wind Turbine Model Integrated with Four Oscillating Water Columns

SSRN

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SSRN 2022年

作者： Ahmad, Irfan M'zoughi, Fares Aboutalebi, Payam Garrido, Izaskun Garrido, Aitor J. Automatic Control Group—ACG Institute of Research and Development of Processes—IIDP Department of Automatic Control and Systems Engineering Faculty of Engineering of Bilbao University of the Basque Country—UPV/EHU Po Rafael Moreno no3 Bilbao48013 Spain

The innovative hybrid offshore platform concept, which combines Floating Offshore Wind Turbines (FOWT) with Oscillating Water Columns (OWCs), has proven to be a promising solution to harvest clean energy. However, the dynamics of the tower and barge platform are extremely sensitive to wind speed and wave elevation, which causes substantial destabilization in extreme conditions, particularly to the tower-top-displacement and platform pitch angle. Therefore, the aim of this article is to focus on two main novel targets: (i) regressive modeling of the hybrid aero-hydro-elasto-servo-mooring coupled numerical system and (ii) an ad-hoc fuzzy-based control implementation for the stabilization of the platform. To analyze the performance of the hybrid FOWT, this article first employs computational Machine Learning (ML) techniques, i.e., Artificial Neural Networks (ANNs), to match the behavior of the detailed FOWT-OWCs numerical model. Then, FLC is developed and applied to establish a structural controller mitigating the undesired vibrations. Both modeling and control schemes are successfully implemented showing a superior performance when compared to the standard barge-based FOWT system. Experimental results demonstrate that the proposed ANN-based modeling is a promising alternative to other intricate non-linear NREL 5MW FOWT dynamical models. Meanwhile, the proposed FLC improves the platform's dynamic behavior with increasing its stability. © 2022, The Authors. All rights reserved.

关键词： Wind

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Cell-free biosynthesis meets dynamic optimization and control: a fed-batch framework

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IFAC-PapersOnLine 2022年第23期55卷 92-97页

作者： Sebastián Espinel-Ríos Nicolas Huber Edgar Alberto Alcalá-Orozco Bruno Morabito Thomas F.T. Rexer Udo Reichl Steffen Klamt Rolf Findeisen Laboratory for System Theory and Automatic Control Otto von Guericke University Magdeburg Germany Analysis and Redesign of Biological Networks Max Planck Institute for Dynamics of Complex Technical Systems Germany Bioprocess Engineering Max Planck Institute for Dynamics of Complex Technical Systems Germany Control and Cyber-Physical Systems Laboratory TU Darmstadt Germany

Cell-free biosynthesis uses the machinery of cells, such as the metabolic reactions, to carry out conversion processes in vitro. This can be more beneficial than in vivo approaches like fermentations. Some advantages of these synthetic biology processes include higher product yields, rates and titers, and more flexibility in pathway design. Cell-free biosynthesis is still in early stages and, unlike in vivo production, there are very few examples of model-based optimization. Moreover, we encounter static optimizations in most cases, neglecting the dynamic nature of the processes. We present an optimal control framework to maximize the efficiency of cell-free biosynthesis. We focus on fed-batch setups as they allow enhancing the reaction rates via the feeding, extending production processes for longer times, and minimizing the potential negative effects of enzyme kinetics with substrate inhibition. Our framework can in principle handle several cost functions and exploit both static and dynamic degrees of freedom. An aspect that can hinder model-based optimization is model uncertainty, which can arise due to uncertain parameters, oversimplified model assumptions or unknown reaction mechanisms. To counteract this, we propose the use of model predictive control during the process operation. In addition, we outline the use of moving horizon estimation as an observer in the case of unmeasured states. We consider the de novo cell-free synthesis of uridine diphosphate-N-acetylglucosamine as a biomedical relevant case study, where we were able to maximize the volumetric productivity in simulations, and indirectly also the titer and enzyme efficiency use.

关键词： Cell-free fed-batch optimization control state estimation UDP-GlcNAc

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Soft sensor for monitoring dynamic changes in cell composition

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IFAC-PapersOnLine 2022年第23期55卷 98-103页

作者： Sebastián Espinel-Ríos Bruno Morabito Katja Bettenbrock Steffen Klamt Rolf Findeisen Laboratory for System Theory and Automatic Control Otto von Guericke University Magdeburg Germany Analysis and Redesign of Biological Networks Max Planck Institute for Dynamics of Complex Technical Systems Germany Bioprocess Engineering Max Planck Institute for Dynamics of Complex Technical Systems Germany Control and Cyber-Physical Systems Laboratory TU Darmstadt Germany

Precise and accurate online monitoring methods are needed to enable smart biomanufacturing and automation. Most of the sensors available focus on process parameters such as metabolite and dissolved gas concentrations, cell density or viability, among other variables like pH, temperature, etc. In this work, we develop a soft sensor algorithm to estimate the cell composition online, a very important aspect often overlooked in the bioprocess monitoring literature. Our strategy is based on full information estimation, an optimization-based estimator that takes into account the dynamics of the cell metabolism and considers all the available measurements from the beginning of the process, thus it has a memory effect. Being able to track dynamic changes in cell composition can open the door to promising applications, e.g., predictive control and automation of biosystems. As a case study, we consider the Escherichia co li’ s metabolism growing on glycerol under different levels of oxygen supply. We compare the performance of our soft sensor method against resource balance analysis, a previously proposed estimator based on steady-state assumptions. Overall, the presented full information estimator was able to track the dynamic changes in cell composition significantly more accurately. We also discuss how our estimation strategy can be transformed into a moving horizon estimation, where only the available measurements in a fixed and moving window are considered, thereby reducing possible computational burdens.

关键词： Cell composition soft sensor full information estimation resource balance analysis dynamic enzyme-cost flux balance analysis

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