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arXiv 2022年

作者： Aghaei, Vahid Tavakol Agababaoglu, Arda Bawo, Biram Naseradinmousavi, Peiman Yıldırım, Sinan Yeşilyurt, Serhat Onat, Ahmet Istinye University Faculty of Engineering and Natural Sciences Electrical and Electronics Engineering Istanbul Turkey Sabanci University Orhanli Tuzla Istanbul34956 Turkey Delivers AI ITU Arı Technological Park Istanbul Turkey Sabanci University Faculty of Engineering and Natural Sciences Mechatronics Engineering Istanbul Turkey San Diego State University Dynamic Systems and Control Laboratory San Diego United States Sabanci University Faculty of Engineering and Natural Sciences Industrial Engineering Istanbul Turkey Istanbul Technical University Control and Automation Engineering Istanbul Turkey

This study focuses on the numerical analysis and optimal control of vertical-axis wind turbines (VAWT) using Bayesian reinforcement learning (RL). We specifically address small-scale wind turbines, which are well-suited to local and compact production of electrical energy on a small scale, such as urban and rural infrastructure installations. Existing literature concentrates on large scale wind turbines which run in unobstructed, mostly constant wind profiles. However urban installations generally must cope with rapidly changing wind patterns. To bridge this gap, we formulate and implement an RL strategy using the Markov chain Monte Carlo (MCMC) algorithm to optimize the long-term energy output of a wind turbine. Our MCMC-based RL algorithm is a model-free and gradient-free algorithm, in which the designer does not have to know the precise dynamics of the plant and its uncertainties. Our method addresses the uncertainties by using a multiplicative reward structure, in contrast with additive reward used in conventional RL approaches. We have shown numerically that the method specifically overcomes the shortcomings typically associated with conventional solutions, including, but not limited to, component aging, modeling errors, and inaccuracies in the estimation of wind speed patterns. Our results show that the proposed method is especially successful in capturing power from wind transients;by modulating the generator load and hence the rotor torque load, so that the rotor tip speed quickly reaches the optimum value for the anticipated wind speed. This ratio of rotor tip speed to wind speed is known to be critical in wind power applications. The wind to load energy efficiency of the proposed method was shown to be superior to two other methods;the classical maximum power point tracking method and a generator controlled by deep deterministic policy gradient (DDPG) method. © 2022, CC BY-NC-ND.

关键词： Reinforcement learning

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METHODOLOGY OF MODELING THE DETECTION OF LIMESCALE DEPOSITS IN SANITARY INSTALLATIONS 21

METHODOLOGY OF MODELING THE DETECTION OF LIMESCALE DEPOSITS ...

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21st International Multidisciplinary Scientific Geoconference: Water Resources, Forest, Marine and Ocean Ecosystems, SGEM 202

作者： Czajkowski, Adrian Remiorz, Leszek Pawlak, Sebastian Remiorz, Eryk Szygula, Jakub Miscea.pl Engineering Sp. z o.o. Poland Department of Power Engineering and Turbomachinery Faculty of Energy and Environmental Engineering Silesian University of Technology Poland Doctoral School The Silesian University of Technology Poland Faculty of Mechanical Engineering Scientific and Didactic Laboratory of Nanotechnology and Materials Technologies Poland Department of Mining Mechanization and Robotisation Faculty of Mining Safety Engineering and Industrial Automation Silesian University of Technology Poland Department of Distributed Systems and Informatic Devices Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Poland

ISBN: (纸本)9786197603361

The article presents the issue of scale deposits in sanitary installations. Scale contributes to improper operation of sanitary devices and leads to their degradation. Scale build-up in pipes and measuring elements results in higher water consumption due to inaccurate estimation of this resource consumption. The consequence is that devices based on process automation do not work properly due to disturbances in the actual water flow. The formation of deposits also leads to economic and ecological losses, as sanitary installations elements have to be replaced, which affects the increased use of raw materials and the generation of waste. This is not in line with current trends and global objectives related to circular economy. Scale also results in increased energy consumption and reduced energy efficiency. This is due to the resistance of the flows and the increased work of the pumps. Scale has insulating properties, which results in less efficient heat transfer. In this stady, a mathematical model was developed to detect the formation of scale deposits in sanitary installations. On this basis, it is possible to control the quality status of sanitary installation components. In this way, by taking appropriate action at an early stage of the formation of deposits, it can extend the life of the installation. Having information on scale deposits, it will help avoid wasting water and lead to more accurate measurement of water flows. This is important for the proper operation of devices such as heat pumps, where their operation is automated and requires precise measurements of the energy processes taking place. Scale is a significant problem for households, and also for factories or public institutions that are exposed to hard water environments. © 2021 International Multidisciplinary Scientific Geoconference. All rights reserved.

关键词： Deposits

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Graph Reinforcement Learning Application to Co-operative Decision-Making in Mixed Autonomy Traffic: Framework, Survey, and Challenges

arXiv

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arXiv 2022年

作者： Liu, Qi Li, Xueyuan Li, Zirui Wu, Jingda Du, Guodong Gao, Xin Yang, Fan Yuan, Shihua School of Mechanical Engineering Beijing Institute of Technology Beijing China Chair of Traffic Process Automation "Friedrich List" Faculty of Transport and Traffic Sciences TU Dresden Germany School of Mechanical and Aerospace Engineering Nanyang Technological University Singapore639798 Singapore Institute of Dynamic System and Control ETH Zurich Zurich Switzerland

Proper functioning of connected and automated vehicles (CAVs) is crucial for the safety and efficiency of future intelligent transport systems. Meanwhile, transitioning to fully autonomous driving requires a long period of mixed autonomy traffic, including both CAVs and human-driven vehicles. Thus, collaboration decision-making for CAVs is essential to generate appropriate driving behaviors to enhance the safety and efficiency of mixed autonomy traffic. In recent years, deep reinforcement learning (DRL) has been widely used in solving decision-making problems. However, the existing DRL-based methods have been mainly focused on solving the decision-making of a single CAV. Using the existing DRL-based methods in mixed autonomy traffic cannot accurately represent the mutual effects of vehicles and model dynamic traffic environments. To address these shortcomings, this article proposes a graph reinforcement learning (GRL) approach for multi-agent decision-making of CAVs in mixed autonomy traffic. First, a generic and modular GRL framework is designed. Then, a systematic review of DRL and GRL methods is presented, focusing on the problems addressed in recent research. Moreover, a comparative study on different GRL methods is further proposed based on the designed framework to verify the effectiveness of GRL methods. Results show that the GRL methods can well optimize the performance of multi-agent decision-making for CAVs in mixed autonomy traffic compared to the DRL methods. Finally, challenges and future research directions are summarized. This study can provide a valuable research reference for solving the multi-agent decision-making problems of CAVs in mixed autonomy traffic and can promote the implementation of GRL-based methods into intelligent transportation systems. The source code of our work can be found at https://***/Jacklinkk/Graph_CAVs. Copyright © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

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Discrete-time Formation control With Attitude Alignment: A Geometric Approach under Ergodic Products

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IEEE Transactions on control of Network Systems 2025年

作者： Li, Zhen Tang, Yang Zhang, Wenbing Fan, Yongqing Huang, Tingwen Xi-an University of Posts and Telecommunications School of Automation Xian710121 China East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process Shanghai200237 China Yangzhou University Department of Mathematics Jiangsu225002 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518055 China

Assuming agents possess sensing capabilities and dynamics relative to their body coordinate frames, this paper addresses a displacement-based strategy for discrete-time formation control through attitude synchronization. Unlike the Eulerian approximation, the first challenge lies in developing discrete-time models that capture both position and attitude motions, thereby complementing existing continuous-time systems with a discretized version. Then, two leader-following control protocols are designed, incorporating a jointly-connected topology to relax the connectivity requirements. The analysis of these systems often relies on the Lyapunov function, the existence of which may be hard to guarantee. Consequently, geometric properties of several polytopes are explored under the dynamic topologies and time-varying parameters. By extending the ergodicity results to leader-following scenarios, these properties are characterized as positively invariant sets. This invariance, which is independent of the existence of the Lyapunov function or the solvability of its associated criteria, provides a beneficial tool for analyzing time-variant systems. Utilizing this invariance and some mathematical techniques, the desired formation shape can be achieved in the cases where the attitudes of the followers synchronize with the leader. Finally, a numerical simulation validates the results. © 2014 IEEE.

关键词： Lyapunov functions

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Quantifying the Impact of Automated Vehicles on Traffic

Quantifying the Impact of Automated Vehicles on Traffic

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Automated Vehicle Validation Conference (IAVVC), IEEE International

作者： Martin Sigl Binnert Prins Christoph Schütz Sebastian Wagner Frederik Schulte Daniel Watzenig BMW AG München Germany Faculty of Mechanical Maritime and Materials Engineering (3mE) Delf CD Institute of Automation and Control at Graz University of Technology Graz Austria Virtual Vehicle Research GmbH Graz Austria

One of the major challenges in the development of Automated Driving is its assessment. It is expected that Automated Vehicles behave differently than human drivers. Therefore, mixed human-robot traffic will yield different and new driving situations as human-only traffic. It is important to know how this mixed traffic will change the composition of traffic situations to be able to quantify the impact Automated Vehicles will have on everyday traffic. This paper presents a methodology on how to find metrics that quantify traffic in order to detect changes in the traffic space that will come with the introduction of Automated Vehicles. Additionally, this methodology provides tools to help with the validation of virtual testing platforms such as simulation.

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AI4S Based on DeSci: Reference Model and Research Issues

AI4S Based on DeSci: Reference Model and Research Issues

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IEEE International Conference on Systems, Man and Cybernetics

作者： Wenwen Ding Juanjuan Li Rui Qin Sangtian Guan Fei-Yue Wang Faculty of Innovation Engineering Macau University of Science and Technology Macau China The State Key Laboratory for Management and Control of Complex Systems The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

The rise of Artificial Intelligence for Science (AI4S) has highlighted the importance and urgency of ensuring open-ness, fairness, impartiality, diversity, and sustainability in scientific systems. Existing scientific systems, referred to as Centralized Science (CeSci), are built on centralized organizational structures and top-down institutional frameworks, which are lagging behind the development and practical requirements of AI4S. To address these limitations, AI4S needs to embrace a new scientific organizational and operational paradigm, namely Decentralized Science (DeSci). It can provide strong support to AI4S via effectively addressing issues such as information silos, biases, unfair distribution, and monopolies and promoting multidisciplinary, interdisciplinary, and trans disciplinary cooperation in science. Based on these considerations, this paper presents the framework of AI4S based on DeSci and explores its potential application scenarios and research issues. The research can provide effective guidance for the development of scientific systems.

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GPU-Accelerated Policy Optimization via Batch Automatic Differentiation of Gaussian Processes for Real-World control

arXiv

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arXiv 2022年

作者： Taheri, Abdolreza Pajarinen, Joni Ghabcheloo, Reza Faculty of Engineering and Natural Sciences Tampere University Finland Department of Electrical Engineering and Automation Aalto University Finland Intelligent Autonomous Systems TU Darmstadt Germany Control Systems R&D HIAB Sweden

The ability of Gaussian processes (GPs) to predict the behavior of dynamical systems as a more sample-efficient alternative to parametric models seems promising for real-world robotics research. However, the computational complexity of GPs has made policy search a highly time and memory consuming process that has not been able to scale to larger problems. In this work, we develop a policy optimization method by leveraging fast predictive sampling methods to process batches of trajectories in every forward pass, and compute gradient updates over policy parameters by automatic differentiation of Monte Carlo evaluations, all on GPU. We demonstrate the effectiveness of our approach in training policies on a set of reference-tracking control experiments with a heavy-duty machine. Benchmark results show a significant speedup over exact methods and showcase the scalability of our method to larger policy networks, longer horizons, and up to thousands of trajectories with a sublinear drop in speed. © 2022, CC BY-NC-ND.

关键词： Gaussian distribution

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EXAMINATION OF WATERPROOF POCKETS AS PROTECTIVE CARRIERS FOR SMART GARMENTS

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Tekstil ve Muhendis 2022年第126期29卷 80-87页

作者： Akkan, Taner Kara, Şükran Ağin, Berk Yeşİlpinar, Sevil Şenol, Yavuz Dokuz Eylul University Izmir Vocational School Department of Mechatronics Izmir Turkey Dokuz Eylul University Faculty of Engineering Department of Textile Eng. Izmir Turkey İstanbul Technical University Control & Automation Engineering Dept. İstanbul Turkey Dokuz Eylul University Faculty of Engineering Electrical and Electronics Eng. Dept. Izmir Turkey

The importance of smart garments is increasing day by day for entertainment, sports, medical and military fields. Both hard electronics and textile based soft electronics find application in these areas. In our previous study, we focused on crew loss detection system that was applicable for team sports such as sailing. We designed a two pieces electronic system in which a master system was fixed on the boat and a slave system was implemented in the sailing garments. This study brought a new requirement;the need of a waterproof case that would protect the electronic slave system from sea water in case the sailors fall overboard. As a result of this need, in this study, we have designed and tested waterproof pockets that could serve as waterproof carriers for the slave system of sailing garments. For this purpose, studies were carried out using two different approaches. In the first approach, 15 types of pockets were developed by using three different fabric materials and 5 different seam sealing techniques. These pocket prototypes were tested by exposing them to the water for 12 h. In the second approach, an electronic circuit was developed by using a humidity sensor to test the pocket samples those were immersed to water. According to the results of the first approach, samples containing polyester base fabric, polyurethane and polyester membrane and double-sided sealing tape gave the best results. By converting the best pocket design into a garment sleeve, the humidity sensor containing test apparatus was tested and measurements were taken successfully from the system © 2022. Tekstil ve *** Rights Reserved

关键词： Humidity sensors

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Data-Driven control and Behavior-Based control Applied to a SISO Mobile Robot

Data-Driven Control and Behavior-Based Control Applied to a ...

引用

IEEE Latin American Robotics Symposium, LARS

作者： M. A. Pastrana Karine R. Santos Alceu B. C. de Farias Daniel M. Muñoz Department of Mechanical Engineering Mechatronics Graduate Program Automation and Control Group/GRACO University of Brasilia Brasilia DF Brazil Faculty of Gama Biomedical Engineering Graduate Program University of Brasilia Brasilia DF Brazil Faculty of Gama Electronics Engineering Undergraduate Program University of Brasilia Brasilia DF Brazil

ISBN: (数字)9781665462808

ISBN: (纸本)9781665462815

With the advancement of technology, systems in many different areas of application have become more complex, and obtaining reliable mathematical models is a task that is increasingly becoming more difficult as well. To overcome these issues, new methodologies have emerged, such as the one known as Data-Driven-control (DDC), which uses the input and output data of the system to design a controller, and Behavior-Based-control (BBC), which provides the controller based on the desired behavior of a system. In this work, these two methodologies were applied to obtain the transfer function of a Single-Input Single-Output mobile robotic platform that moves parallel to a wall. A Proportional Integral Derivative controller was used to control the left wheel, while the right wheel was set to a constant speed, allowing the mobile robot to maintain a constant distance from the wall. The controller was tuned utilizing root locus methodology with an overshot of 16.7%, settling time of 6.45 seconds, and a static error of 2.1%. Additionally, disturbances were added to test the robustness of the mobile robot model and the PID controller.

关键词： Analytical models Biological system modeling Wheels Transfer functions Robot sensing systems control systems Mathematical models

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Spirometer and sEMG respiratory patterns for clinical decision support system

Spirometer and sEMG respiratory patterns for clinical decisi...

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IEEE Instrumentation and Measurement Technology Conference

作者： Amit Krishan Kumar Mansour H. Assaf Voicu Z. Groza Emil M. Petriu Faculty of Electrical-Electronic Engineering Duy Tan University Da Nang Vietnam Key Laboratory of Complex System Intelligent Control and Decision School of Automation Beijing Institute of Technology Beijing China School of Information Technology Engineering Mathematics and Physics The University of the South Pacific Suva Republic of Fiji School of Electrical Engineering and Computer Science University of Ottawa Ottawa Canada

To improve support system for subjects with respiratory ailments, this work analyses the respiratory patterns from spirometer and surface electromyography (sEMG). The patterns are integrated with risk and biochemical systems model to optimise the clinical decision support system (CDSS). The work considers terminal events for the spirometer and sEMG. The events and their associated patterns are correlated and projected on a hyperspace prior implementing it in decision support system. The proposed method shows that the different characteristics of breathing indicate some essential hidden information which when used as 4D scattered projection, optimises the decision making in CDSS. The analysis shows the effectiveness of bimodal respiratory patterns for different breathing capacities, which is promising for the modelled conditions.

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