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A LESO Based Backstepping Controller Considering Coal Seam Hardness for Rotary Speed in Coal Mine Tunnel Drilling Process

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Journal of systems Science & Complexity 2024年第5期37卷 1789-1808页

作者： CHEN Luefeng LIU Xiao WU Min LU Chengda PEDRYCZ Witold HIROTA Kaoru School of Automation China University of GeosciencesWuhan 430074China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of EducationWuhan 430074China Department of Electrical and Computer Engineering University of AlbertaEdmontonAB T6R2G7Canada The Tokyo Institute of Technology Yokohama 226-8502Japan

In the process of coal mine drilling,controlling the rotary speed is important as it determines the efficiency and safety of *** this paper,a linear extended state observer(LESO)based backstepping controller for rotary speed is proposed,which can overcome the impact of changes in coal seam hardness on rotary ***,the influence of coal seam hardness on the drilling rig’s rotary system is considered for the first time,which is reflected in the numerical variation of load torque,and a dynamic model for the design of rotary speed controller is *** an LESO is designed to observe the load torque,and feedforward compensation is carried out to overcome the influence of coal seam *** on the model of the compensated system,a backstepping method is used to design a controller to achieve tracking control of the rotary ***,the effectiveness of the controller designed in this paper is demonstrated through simulation and field experiments,the steady-state error of the rotary speed in field is 1 r/min,and the overshoot is reduced to 5.8%.This greatly improves the stability and security,which is exactly what the drilling process requires.

关键词： Backstepping extended state observer hydraulic tunnel drilling machine rotary speed

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Performance Evaluation of Hierarchical Federated Learning on Resource-Constrained IoT systems 100

Performance Evaluation of Hierarchical Federated Learning on...

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Adam, Mumin Baroudi, Uthman Interdisciplinary Center for Intelligent Secure Systems King Fahd University of Petroleum & Minerals Department of Computer Engineering Dhahran Saudi Arabia

ISBN: (纸本)9798331517786

The Internet of Things (IoT) has transformed how smart cities operate, significantly improving their inhabitants' efficiency and overall quality of life. However, the massive volume of sensitive data generated by IoT devices presents challenges, including privacy concerns and communication overheads. Traditional centralized data processing can compromise privacy and require significant communication, leading to scalability problems and power drains. Federated Learning (FL) offers a solution by processing data locally and transmitting only the model parameters. Vanilla FL faces challenges in IoT environments due to latency, bandwidth constraints, and power drain. Hierarchical FL (HFL) effectively addresses these issues by hierarchically leveraging the processing capabilities of both cloud and edge servers to optimize resource utilization and efficiently minimize latency. This paper evaluates HFL using IoT-derived datasets, develops and implements the HFL framework for resource-constrained IoT systems, and conducts the first known HFL tests on relevant datasets to demonstrate its performance. © 2024 IEEE.

关键词： Air quality

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A Survey of Cyber Attacks on Cyber Physical systems:Recent Advances and Challenges

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IEEE/CAA Journal of Automatica Sinica 2022年第5期9卷 784-800页

作者： Wenli Duo MengChu Zhou Abdullah Abusorrah Macao Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China IEEE Department of Electrical and Computer Engineering K.A.CARE Energy Research and Innovation Center Department of Electrical and Computer Engineering K.A.CARE Energy Research and Innovation Center and Center of Research Excellence in Renewable Energy and Power SystemsKing Abdulaziz University

A cyber physical system(CPS)is a complex system that integrates sensing,computation,control and networking into physical processes and objects over *** plays a key role in modern industry since it connects physical and cyber *** order to meet ever-changing industrial requirements,its structures and functions are constantly ***,new security issues have arisen.A ubiquitous problem is the fact that cyber attacks can cause significant damage to industrial systems,and thus has gained increasing attention from researchers and *** paper presents a survey of state-of-the-art results of cyber attacks on cyber physical ***,as typical system models are employed to study these systems,time-driven and event-driven systems are ***,recent advances on three types of attacks,i.e.,those on availability,integrity,and confidentiality are *** particular,the detailed studies on availability and integrity attacks are introduced from the perspective of attackers and ***,both attack and defense strategies are discussed based on different system *** challenges and open issues are indicated to guide future research and inspire the further exploration of this increasingly important area.

关键词： Attack detection attack strategy cyber attack cyber physical system(CPS) secure control

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Collaborative Routing and Charging/Discharging Scheduling of Electric Autonomous Vehicles in Coupled Power-Traffic Networks: A Multiobjective Approach

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IEEE Internet of Things Journal 2025年第11期12卷 17753-17764页

作者： Chu, Kai-Fung Chen, Tianlun Xie, Yue Lam, Albert Y. S. Song, Yue Iida, Fumiya University of Cambridge Department of Engineering CambridgeCB2 1PZ United Kingdom The University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong The University of Hong Kong Department of Electrical and Electronic Engineering Department of Geography Hong Kong Fano Labs Hong Kong Tongji University Department of Control Science and Engineering Shanghai201804 China Tongji University National Key Laboratory of Autonomous Intelligent Unmanned Systems Shanghai201210 China Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Shanghai200120 China

Autonomous vehicles (AVs) are vehicles that traverse on the road without active human intervention. With a coordinator, AVs can be connected to provide high-efficiency transport services, such as AV-based public transport networks. The controller can manage the network by coordinating the transport request assignment, traveling, and charging/discharging schedule. On the other hand, AVs are likely to be electric and benefit the smart grid via vehicle-to-grid technology. A well-designed mobility network connecting electric AVs (EAVs) and smart grid can substantially reduce unnecessary travel and energy costs. In this article, we aim to maximize utilities in the AV-based public transport network and the power distribution network for the vehicle network containing EAVs, charging stations, and distributed power generations. We formulate the assignment and scheduling problem as a multiobjective mixed-integer program (MIP). To solve the optimization problem, we develop a hybrid heuristic approach based on nondominated sorting genetic algorithm II (NSGA-II) and branch-and-bound (BnB) algorithms. Experiments are conducted on a modified 15-bus distribution system and a simulated traffic network. The results show that the proposed strategy effectively minimizes the total travel and energy purchase cost by 21%. This study provides valuable insights on vehicle coordination for multiple tasks, offering visionary guidance for stakeholders engaged in multifaceted transportation endeavors. © 2014 IEEE.

关键词： Vehicle-to-grid

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Control of Linear Servo Carts with Integral-Based Disturbance Rejection

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computers, Materials & Continua 2022年第10期73卷 453-463页

作者： Ibrahim M.Mehedi Abdulah Jeza Aljohani Ubaid M.Al-Saggaf Ahmed I.Iskanderani Thangam Palaniswamy Mohamed Mahmoud Mohammed J.Abdulaal Muhammad Bilal Waleed Alasmary Department of Electrical and Computer Engineering(ECE) King Abdulaziz University5521589Saudi Arabia Center of Excellence in Intelligent Engineering Systems(CEIES) King Abdulaziz University6521589Saudi Arabia Electrical and Computer Engineering TN38505United States Computer and Information Systems Umm Al-Qura UniversityMakkahSaudi Arabia

This paper describes a system designed for linear servo cart systems that employs an integral-based Linear Active Disturbance Rejection Control(ILADRC)scheme to detect and respond to *** upgrade in this control technique provides extensive immunity to uncertainties,attenuation,internal disturbances,and external sources of *** fundamental technology base of LADRC is Extended State Observer(ESO).LADRC,when combined with Integral action,becomes a hybrid control technique,namely *** tracking is based on Bode’s Ideal Transfer Function(BITF)in this proposed ILADRC *** proves to be a very robust and appropriate pole placement *** proposed LSC system has experimented with the hybrid ILADRC technique plotted the *** the results,it is evident that the proposed ILADRC scheme enhances the robustness of the LSC system with remarkable disturbance ***,the results of a linear quadratic regulator(LQR)and ILADRC schemes are comparatively *** analysis deduced the improved performance of ILADRC over the LQR control scheme.

关键词： Pole placement ADRC Active disturbance rejection robust control linear servo cart system

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A Novel Thermal Analysis Method for Tubular PM Linear Motors Based on Transfer Learning

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IEEE Transactions on Transportation Electrification 2025年第3期11卷 7379-7388页

作者： Wu, Tao Dai, Peipei Xue, Gang Guo, Youguang Lei, Gang Zhu, Jianguo Wang, Yifei China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China University of Technology Sydney School of Electrical and Data Engineering UltimoNSW2007 Australia The University of Sydney School of Electrical and Computer Engineering SydneyNSW2006 Australia

Thermal modeling and analysis are critical for permanent magnet linear synchronous motors (PMLSMs), particularly in multiphysical analysis and motor design optimization. This article proposes a new method for thermal steady state modeling and analysis based on transfer learning (TL), which combines the thermal mechanism model with the data-driven model to achieve high-precision temperature analysis with small number of finite element analysis (FEA) samples. First, an equivalent thermal circuit (ETC) model is established for PMLSMs based on the structural parameters and heat transfer principles. Second, a hybrid model based on TL is developed: the model is pretrained by a large sample set generated from the ETC in the source field (mechanism analytical model), and then, the trained model is transferred to the target field (FEA data-driven model). The parameters of the neural network in the target field are optimized by a few FEA sample sets with higher precision to improve the prediction accuracy of the target model. Experimental results demonstrate that the proposed method significantly boosts the performance of the deep neural network (DNN) model, particularly when training sets are small. The root mean square error (RMSE) of the DNN model decreases by 0.901 in the 5:5 (training/testing) dataset and 1.726 in the 2:8 dataset;and the corresponding R^{2} and maximal absolute error of motor performances are significantly decreased. The smaller the proportion of the training set, the better performance of the hybrid modeling will achieve. Furthermore, a temperature rise experiment conducted on a prototype motor validates the efficacy and precision of the proposed hybrid modeling methodology. © 2015 IEEE.

关键词： Permanent magnets

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OBSO Based Fractional PID for MPPT-Pitch Control of Wind Turbine systems

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computers, Materials & Continua 2022年第5期71卷 4001-4017页

作者： Ibrahim M.Mehedi Ubaid M.Al-Saggaf Mahendiran T.Vellingiri Ahmad H.Milyani Nordin Bin Saad Nor Zaihar Bin Yahaya Department of Electrical and Computer Engineering(ECE) King Abdulaziz UniversityJeddah21589Saudi Arabia Center of Excellence in Intelligent Engineering Systems(CEIES) King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Electrical&Electronics Engineering Universiti Teknologi PETRONASMalaysia

In recent times,wind energy receives maximum attention and has become a significant green energy source *** wind turbine(WT)entered into several domains such as power electronics that are employed to assist the connection process of a wind energy system and *** turbulent characteristics of wind profile along with uncertainty in the design of WT make it highly challenging for prolific power *** pitch control angle is employed to effectively operate the WT at the above nominal wind ***,the pitch controller needs to be intelligent for the extraction of sustainable secure energy and keep WTs in a safe operating *** achieve this,proportional–integral–derivative(PID)controllers are widely used and the choice of optimal parameters in the PID controllers needs to be properly *** this motivation,this paper designs an oppositional brain storm optimization(OBSO)based fractional order PID(FOPID)design for sustainable and secure energy in WT *** proposed model aims to effectually extract the maximum power point(MPPT)in the low range of weather conditions and save the WT in high wind regions by the use of pitch *** OBSO algorithm is derived from the integration of oppositional based learning(OBL)concept with the traditional BSO algorithm in order to improve the convergence rate,which is then applied to effectively choose the parameters involved in the FOPID *** performance of the presented model is validated on the pitch control of a 5 MW WT and the results are examined under different *** simulation outcomes ensured the promising characteristics of the proposed model over the other methods.

关键词： Wind turbine wind energy pitch control brain storm optimization PID controller maximum power point

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Thin-film PMUTs:a review of over 40 years of research

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Microsystems & Nanoengineering 2023年第4期9卷 1-17页

作者： Kaustav Roy Joshua En-Yuan Lee Chengkuo Lee Department of Electrical and Computer Engineering National University of SingaporeSingapore 117583Singapore Center for Intelligent Sensor and MEMS(CISM) National University of SingaporeSingapore 117608Singapore Institute of Microelectronics A*STARSingapore 117685Singapore

Thin-film PMUTs have been important research topics among microultrasound experts,and a concise review on their research progress is reported *** rigorous surveying,scrutinization,and perception,it has been determined that the work in this field began nearly 44 years ago with the primitive development of functional piezoelectric thin-film *** date,there are three major companies commercializing thin-film PMUTs on a bulk *** commercialization illustrates the extensive contributions made by more than 70 different centers,research institutes,and agencies across 4 different continents regarding the vast development of these devices’design,manufacturing,and *** review covers these important contributions in a short yet comprehensive manner;in particular,this paper educates readers about the global PMUT outlook,their governing design principles,their manufacturing methods,nonconventional yet useful PMUT designs,and category-wise *** comparison charts of thin-film piezoelectric material used in PMUTs,and their categorically targeted applications are depicted and discussed to enlighten any MEMS designer who plans to work with ***,each relevant section features clear future predictions based on the author’s past knowledge and expertise in this field of research and on the findings of a careful literature *** short,this review is a one-stop time-efficient guide for anyone interested in learning about these small devices.

关键词： film piezoelectric companies

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Using Interim Recommitment to Reduce the Operational-cost Impacts of Wind Uncertainty

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Journal of Modern Power systems and Clean Energy 2022年第4期10卷 839-849页

作者： Mahan A.Mansouri Ramteen Sioshansi Department of Integrated Systems Engineering The Ohio State UniversityColumbusOH 43210USA Department of Integrated Systems Engineering Department of Electrical and Computer Engineeringand Center for Automotive ResearchThe Ohio State UniversityColumbusOH 43210USA

Using wind-availability forecasts in day-ahead unit commitment can require expensive real-time operational *** examine the benefit of conducting interim recommitment between day-ahead unit commitment and real-time *** a simple stylized example and a case study that is based on ISO New England,we compare system-operation costs with and without interim *** find an important tradeoff—later recommitment provides better wind-availability forecasts,but the system has less flexibility due to operating *** the time windows that we examine,hour-20 recommitment provides the greatest operational-cost reduction.

关键词： Power-system operation power-system economics unit commitment economic dispatch wind generation.

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From Simulation to Reality with Random Noise

From Simulation to Reality with Random Noise

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Luo, Steven Lipkis, Rory López-Francos, Ignacio Vlastos, Pavlo Agogino, Adrian Department of Electrical Engineering and Computer Sciences University of California BerkeleyCA94720 United States Intelligent Systems Division NASA Ames Research Center Moffett FieldCA94035 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

The challenging environment of autonomous vehicle (AV) navigation necessitates certain functions be performed by deep neural networks. Optimizing these models involves collecting vast quantities of domain-specific training data and ensuring that the dataset is representative of expected conditions. High-fidelity simulation plays a vital role in making this process feasible, allowing a wide range of scenarios to be explored at relatively low cost. However, learning from simulation introduces subtle biases into models, which can degrade real-world performance in unpredictable ways. This effect can be mitigated with learning schemes specialized to bridge distributional shifts (transfer learning). Given the complex nature of these methods, the underlying models, and their environments, meaningfully evaluating performance is not straightforward. Many unrelated factors can effect an improvement in generalization accuracy, but a full ablation analysis is often difficult to perform. To tease out signal from noise, it is necessary to understand how transfer learning performance is affected by noise itself. The goals of this paper are (i) to establish a domain randomization baseline for a simple classification transfer learning task and (ii) to validate the RRAV testbed as a platform for further research in sim-to-real learning. We generate imagery from a simulation of NASA Ames Research center and train a small convolutional neural network to classify camera position of an ego vehicle relative to a center line. Further models are trained with different types of noise progressively added to the data. In our experiments, we find that naive domain randomization raises sim-to-real accuracy from 64% to 79%, while training directly on real data yields an 89% accuracy ceiling. These results suggest that the isolated mechanism of domain randomization can significantly improve generalization. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： NASA

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