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arXiv 2022年

作者： Kobelski, Alexander Hempel, Arne-Jens Padmanabha, Murali Wille, Luiz-Carlos Streif, Stefan Faculty of Electrical Engineering and Information Technology Automatic Control and System Dynamics Technische Universitaet Chemnitz Chemnitz09111 Germany The Department of Digital Engineering Staatliche Studienakademie Glauchau Glauchau08371 Germany

Black soldier fly (BSF) larvae (Hermetia illucens) are a valuable protein source for manufacturing animal feed. To maximize their production, both the quantity and quality of their reproductive cycle, i.e. egg production during oviposition, must be increased. In artificial environments, flies often sit idle in cages without mating, depleting their energy reserves and resulting in lower egg production per female. By controlling environmental conditions such as temperature and light inside breeding cages, the flies may be stimulated in a way that improves egg output. However, this stimulation increases the energy demand of the process and may stress the flies, resulting in reduced egg production. Therefore, control must be applied in a careful way, which requires knowledge of the egg production cycle. In this work, a mathematical model describing the various fly life stages and their transition to the egg production process is developed. Relevant factors are identified and their effect on the fly life and egg production is mathematically described. Parameters are identified using data from literature and goodness of fit is evaluated. Using the model, an optimal control problem is formulated with the goal of minimizing the energy costs and increasing the egg production quantity. In Simulation, our approach showed 13% higher output in shorter time at reduced energy costs compared to a (standard) constant setpoint approach. Optimal control could reach same amount of egg in 60 % of time compared to standard scenario. © 2022, CC BY-NC-ND.

关键词： Optimal control systems

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Active Flow control on Airfoils by Reinforcement Learning

SSRN

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SSRN 2023年

作者： Portal-Porras, Koldo Fernandez-Gamiz, Unai Zulueta, Ekaitz Garcia-Fernandez, Roberto Berrizbeitia, Saioa Etxebarria Nuclear Engineering and Fluid Mechanics Department Faculty of Engineering of Vitoria-Gasteiz University of the Basque Country UPV/EHU Nieves Cano 12 Araba Vitoria-Gasteiz01006 Spain Automatic Control and System Engineering Department Faculty of Engineering of Vitoria-Gasteiz University of the Basque Country UPV/EHU Nieves Cano 12 Araba Vitoria-Gasteiz01006 Spain Sunsundegui S.A. Poligono Ibarrea s/n Navarra Altsasu31800 Spain Mechanical Engineering Department Faculty of Engineering of Vitoria-Gasteiz University of the Basque Country UPV/EHU Nieves Cano 12 Araba Vitoria-Gasteiz01006 Spain

In recent years, wind energy is gaining importance, with the aim of achieving a clean and sustainable energy model. However, an enhancement in wind turbine performance is still required to compete with traditional energy sources. The implementation of flow control devices in airfoils is one of the most common solutions to achieve this improvement. Among flow control devices several alternatives are available, but devices with reactive control are the most effective, due to their capacity to adapt to instantaneous conditions of the flow. This study proposes a control strategy based on a Reinforcement Learning (RL) agent to effectively govern the motion of a rotating flap implemented on a NACA0012 airfoil. With this objective, first different Computational Fluid Dynamics (CFD) simulations are conducted to gather data about the tested case. Then, a numerical model based on Artificial Neural Networks (ANN) is developed to model the discussed case. Finally, the RL agent is trained and tested under different conditions. The results show that the trained RL agent is able to provide a fast and reliable response for every tested condition, setting the adequate position of the flap and obtaining an appropriate aerodynamic performance of the airfoil. © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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A Model Predictive Current control for a Three-Phase Modular Matrix Converter

A Model Predictive Current Control for a Three-Phase Modular...

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IEEE Energy Conversion Congress and Exposition, ECCE

作者： Maryam Sarebanzadeh Mohammad Ali Hosseinzadeh Ralph Kennel Ebrahim Babaei Marco Rivera Patrick Wheeler Chair of High-Power Converters System Technical University of Munich Munich Germany Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran Department of Electrical and Electronic Engineering Faculty of Engineering Chair of Power Electronics Machines and Control (PEMC) Research Group University of Nottingham Nottingham UK Laboratorio de Conversión de Energías y Electrónica de Potencia (LCEEP) Vicerrectoría Academica Universidad de Talca Talca Chile

ISBN: (数字)9798350376067

ISBN: (纸本)9798350376074

The modular matrix converter (MMC) integrates a multi-winding transformer with a three-phase-to-single-phase matrix converter for each phase, making it suitable for high-power, high-voltage applications. Predictive control is an advanced control technique widely applied to power converters due to its numerous advantages, including fast dynamic response, the elimination of the need for proportional gain, and straightforward implementation. This study proposes a model predictive control (MPC) strategy to regulate load currents, reactive power, and common-mode voltage in a three-phase modular matrix converter. The proposed MPC algorithm selects the optimal switching states from all valid options for the modular matrix converter to apply in the next control step. A cost function is then used to minimize the error between the predicted system variables and their reference values, ensuring that the best states are applied to the modular matrix converter. The proposed MPC technique effectively controls the load currents to follow the desired reference signals, maintains sinusoidal source current waveforms by regulating reactive power, and reduces the magnitude of the common-mode voltage. The performance of the proposed approach is demonstrated through simulation results obtained in the MATLAB/Simulink environment.

关键词： Current control Reactive power Total harmonic distortion Simulation Switches Cost function Transformers Matrix converters Voltage control Predictive control

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Two-harmonic Periodic Disturbance Compensation for Dead-Time Processes with Online Identification of Input Delay

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IFAC-PapersOnLine 2022年第36期55卷 73-78页

作者： Adam Peichl Michal Kuchař Can Kutlu Yuksel Silviu-Iulian Niculescu Tomáš Vyhlídal Department of Instrumentation and Control Engineering Faculty of Mechanical Engineering Czech Technical University in Prague Technická 4 Prague 6 16607 Czechia Université Paris-Saclay CNRS CentraleSupélec Inria Laboratoire des Signaux et Systèmes 91192 Gif-sur-Yvette France

As in the case of the Smith predictor, when applied to dynamical systems with delay in the input, the stability of the internal model control (IMC) system can be very sensitive to the mismatch that can occur between the plant delay and the identified delay. In this paper, we propose an online identification technique for the plant with input delay, which is used to adapt an IMC controller designed to reject periodic disturbances consisting of two harmonics. The proposed method is based on wavelet analysis and cross-correlation of the measured output. Moreover, the proposed controller structure introduces a novel analytical approach which makes it suitable for the adaptive application. The practical motivation of the study is to address the delay mismatch that can be caused by approximating time-varying high-order systems by first-order models with input delay. The approach is numerically validated.

关键词： Delay vibration control frequency-domain time-varying delay delay estimation

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Genetic-Algorithm-Based Proportional Integral controller (GAPI) for ROV Steering control

arXiv

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arXiv 2023年

作者： Tanveer, Ahsan Ahmad, Sarvat Mushtaq Faculty of Mechanical Engineering Ghulam Ishaq Khan Institute of Engineering Sciences and Technology Topi23640 Pakistan Department of Mechanical & Aerospace Engineering Institute of Avionics and Aeronautics Air University Islamabad44000 Pakistan Control & Instrumentation Engineering Department King Fahd University of Petroleum and Minerals Dhahran31261 Saudi Arabia Interdisciplinary Research Center for Intelligent Manufacturing and Robotics King Fahd University of Petroleum and Minerals Dhahran31261 Saudi Arabia

This article presents the design and real-time implementation of an optimal controller for precise steering control of a remotely operated underwater vehicle (ROV). A PI controller is investigated to achieve the desired steering performance. The gain parameters of the controller are tuned using the genetic algorithm (GA). The experimental response corresponding to the step waveform for the GA is obtained. A root-locus-tuned PI controller alongside a simulated-annealing-based PI controller (SAPI) is used to benchmark the response characteristics such as overshoot, peak time, and settling time. The experimental findings indicate that GAPI provides considerably better performance than SAPI and the root-locus-tuned controller. © 2023, CC BY.

关键词： Simulated annealing

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Interaction of soil microbial communities and phosphorus fractions under long-term fertilization in paddy soil

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Journal of Integrative Agriculture 2022年第7期21卷 2134-2144页

作者： Muhammad QASWAR Waqas AHMED HUANG Jing LIU Kai-lou ZHANG Lu HAN Tian-fu DU Jiang-xue Sehrish ALI Hafeez UR-RAHIM HUANG Qing-hai ZHANG Hui-min National Engineering Laboratory for Improving Quality of Arable Land/lnstitute of Agricultural Resources and Regional Planning Chinese Academy of Agricultural SciencesBeijing 100081P.R.China Department of Environment Faculty of Bioscience EngineeringGhent UniversityGhent 9000Belgium Guangdong Provincial Key Laboratory for Radionuclides Pollution Control and Resources School of Environmental Science and EngineeringGuangzhou UniversityGuangzhou 510006P.R.China National Observation Station of Qiyang Agri-ecology System/lnstitute of Agricultural Resources and Regional Planning Chinese Academy of Agricultural SciencesQiyang 426182P.R.China National Engineering and Technology Research Center for Red Soil Improvement/Jiangxi Institute of Red Soil Nanchang 331717P.R.China Key Laboratory of Industrial Ecology and Environmental Engineering Ministry of Education/School of Environmental Science and TechnologyDalian University of TechnologyDalian 116024P.R.China

Understanding the impact of biological activities on the soil phosphorus(P)distribution under long-term fertilizer application can facilitate better soil P fertility ***,the primary objectives of this study were to investigate the effect of long-term(since 1981)fertilizer application on the soil P fractions and microbial community and to evaluate correlations between the microbial community structure and P *** following treatments were implemented in a long-term field trial:no fertilization(CK),inorganic N and K(NK),inorganic P and K(PK),inorganic N,P and K(NPK)and manure+NPK(MNPK)*** study showed that the soil pH,soil organic carbon and total and available N and P concentrations were considerably higher in the MNPK treatment than in the CK *** soil microbial biomass C,N and P concentrations were also significantly higher in the MNPK treatment than in the CK *** fertilization treatments,theβ-1,4-glucosidase,α-1,4-glucosidase,urease,acid phosphatase and phosphodiesterase activities were the highest in the MNPK *** to inorganic fertilization,the MNPK treatment increased the labile soil P fractions and decreased the residual soil P *** fertilization significantly affected the soil microbial *** total phospholipid fatty acid(PLFA)concentrations in the NK,PK,NPK and MNPK treatments were 23.3,43.1,48.7 and 87.7%higher,respectively,than in the CK treatment.A significant correlation was observed between the microbial community and soil P ***,the aggregated boosted tree(ABT)model showed that among the various soil biochemical properties,the total PLFA concentration was the factor that most influenced the active P pool,accounting for 35.4%of the relative influence of all soil biochemical properties *** findings reveal that combined manure and inorganic fertilizer application is a better approach than applying inorganic fertilizer alone fo

关键词： enzyme activities long-term fertilization microbial community microbial biomass phosphorus fraction

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Metaheuristic-Based Load Frequency control for EVs-Integrated Hybrid Power systems with Delays

Metaheuristic-Based Load Frequency Control for EVs-Integrate...

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IEEE SSD International Multi-Conference on systems, Signals and Devices

作者： Ghali Ahmad Nezar M. Al-Yazidi Md Shafiullah Mutaz M. Hamdan Control & Instrumentation Engineering Department King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia IRC for Smart Mobility and Logistics King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia IRC for Sustainable Energy Systems King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia K.A.CARE Energy Research & Innovation Center King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia Electrical Engineering Department Faculty of Engineering Al-Ahliyya Amman University Amman Jordan

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

This paper proposed a load frequency control (LFC) to ensure power quality of a two-area interconnected hybrid power system (PS) involving integrated electric vehicles (EVs) and communication delay. The control framework consists of three optimization-based proportional-integralderivative (PID) controllers named genetic algorithm (GA-PID), particle swarm optimization (PSO-PID), and grey wolf optimization (GWO-PID) controllers, and fractional order proportional-integral-derivative (FOPID) for improving the system's stability and performance. The performance of the proposed controllers for handling the challenges arising from EV integration, communication delay, and renewable energy sources (RESs) intermittency has been assessed. A thorough performance analysis is subsequently carried out according to the simulation results and various error indices, including integral square error (ISE), integral absolute error (IAE), and integral time absolute error (ITAE). The findings show that the proposed GA-PID controller performs better dynamically than the PSO-PID, GWO-PID, and FOPID controllers in terms of robustness and convergence rate. The results suggest the improvement of LFC in power systems through optimization algorithms.

关键词： Simulation Power quality Stability criteria Power system stability Hybrid power systems Delays Particle swarm optimization Optimization Frequency control Genetic algorithms

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Drowsy Driver Detection system For Poor Light Conditions

Drowsy Driver Detection System For Poor Light Conditions

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2023 IEEE International Conference on Mechatronics (ICM)

作者： Ahmed Ibnouf Ayman Fadlallah Muaiz Ali Abdelmalek Zidouri Control & Instrumentation Engineering Department King Fahd University of Petroleum and Minerals (KFUPM) Dhahran Saudi Arabia Information & Computer Science Department King Fahd University of Petroleum and Minerals (KFUPM) Dhahran Saudi Arabia Electrical Engineering Department King Fahd University of Petroleum and Minerals (KFUPM) Dhahran Saudi Arabia Electrical Engineering Department Interdisciplinary Research Center for Intelligent Secure System King Fahd University of Petroleum and Minerals (KFUPM) Dhahran Saudi Arabia

In recent years, the number of car accidents has been gradually increasing. A major factor in this increase is the drowsiness of drivers. There are many image recognition research studies on drowsiness detection but tend to neglect the case of low-light conditions. This paper focuses on detecting drowsy drivers in low-light conditions. In this paper, a behavioralbased drowsy driver detection system (DDDS) was proposed. The proposed DDDS applies a preprocessing algorithm that improves illumination and then focuses on detecting the driver’s face and eyes to calculate eye blink duration. A deep neural network model was used for facial recognition, and Haar Cascade classifiers were used for eyes detection. Every single eye was based to a Convolutional Neural Network (CNN) model to predict its state as either ‘‘Open’’ or ‘‘Closed’’. The proposed CNN was developed using a dataset of 1452 samples and gave an accuracy of 97.92% on the testing dataset, 15% of the 1452 samples. Different case scenarios in poor light conditions were implemented to evaluate the proposed DDDS. The system was able to identify the state of the driver even in harsh and severe poor light conditions.

关键词： Deep learning Face recognition Transportation industry Lighting Safety Convolutional neural networks Standards

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Biosignal-Based Attention Monitoring for Evaluating Train Driver Safety-Relevant Tasks

SSRN

引用

SSRN 2024年

作者： Kim, Jung Hwan Kim, Yeongjun Cho, Younggeol Kim, Tae Kyun Jang, Tongil Park, Chanwoo Kang, Seongkeun Advanced Instrumentation & Control Research Section Korea Atomic Energy Research Institute Daejeon Korea Republic of Department of Nuclear & Quantum Engineering Korea Advanced Institute of Science and Technology Daejeon Korea Republic of School of Computing Korea Advanced Institute of Science and Technology Daejeon Korea Republic of Department of Railroad Electrical & Electronic Engineering Korea National University of Transportation Uiwang Korea Republic of System Safety Research Department Korea Railroad Research Institute Uiwang Korea Republic of

This study explores the impact of phone usage on train driver performance, a critical aspect influencing the safety of railroad operations. The persistent problem of phone distractions, which severely impair situational awareness and contribute to accidents, necessitates innovative solutions to enhance operational safety. Leveraging advancements in technology, this study introduces a novel approach combining electroencephalogram (EEG)-based analysis with deep learning algorithms to assess the effects of phone usage on the attention states of train drivers. Utilizing a full-type train simulator to replicate real-life scenarios, this study conducts an experimental design involving scenarios with and without phone engagement, aiming to provide a comprehensive assessment of phone usage on performance in critical situations. The deep learning model employed achieved an impressive classification accuracy of approximately 85.6% in distinguishing attention states among 25 participants, with high precision and recall values indicating its efficacy in detecting phone interaction. Moreover, findings reveal that phone usage leads to a 1.4 times increase in response time to situational awareness, significantly affecting driver performance. These findings highlight the significant impact of phone usage on train driver attention and underscore the potential of EEG-based analysis and deep learning in improving railroad safety measures. This study contributes to the growing body of safety literature by focusing on train drivers, offering fresh insights and practical implications for enhancing the safety and efficiency of railroad operations. © 2024, The Authors. All rights reserved.

关键词： Railroads

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Innovation-triggered Learning with Application to Data-driven Predictive control

arXiv

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arXiv 2024年

作者： Zheng, Kaikai Shi, Dawei Hirche, Sandra Shi, Yang The MIIT Key Laboratory of Servo Motion System Drive and Control School of Automation Beijing Institute of Technology Beijing100081 China Technical University of Munich Barer Strasse 21 Munich80333 Germany The Department of Mechanical Engineering Faculty of Engineering University of Victoria VictoriaBCV8N 3P6 Canada

Data-driven control has attracted lots of attention in recent years, especially for plants that are difficult to model based on first principles. In particular, a key issue in data-driven approaches is how to make efficient use of data as the abundance of data becomes overwhelming. To address this issue, this work proposes an innovation-triggered learning framework and a corresponding data-driven controller design approach with guaranteed stability. Specifically, we consider a linear time-invariant system with unknown dynamics. A set-membership approach is introduced to learn a parametric uncertainty set for the unknown dynamics. Then, a data selection mechanism is proposed by online evaluating the innovation contained in the sampled data, wherein the innovation is quantified by its effect of shrinking the parametric uncertainty set. Next, after introducing a stability criterion using the set-membership estimate of the system dynamics, a robust data-driven predictive controller is designed by minimizing a worst-case cost function. The closed-loop stability of the data-driven predictive controller equipped with the innovation-triggered learning protocol is discussed within a high probability framework. Finally, comparative numerical experiments are performed to verify the validity of the proposed approach, and the characteristics and the design principle of the learning hyper-parameter are also discussed. Copyright © 2024, The Authors. All rights reserved.

关键词： Predictive control systems

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