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

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^driven 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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Physics-Model-Regulated Deep Reinforcement learning Towards Safety & Stability Guarantees 62

Physics-Model-Regulated Deep Reinforcement Learning Towards ...

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62nd ieee conference on Decision and control (CDC)

作者： Cao, Hongpeng Mao, Yanbing Sha, Lui Caccamo, Marco Tech Univ Munich Sch Engn & Design D-85748 Munich Germany Wayne State Univ Engn Technol Div Detroit MI 48201 USA Univ Illinois Dept Comp Sci Urbana IL 61801 USA Tech Univ Munich Inst Robot & Machine Intelligence Munich Germany

ISBN: (纸本)9798350301243

Deep reinforcement learning (DRL) has demonstrated impressive success in solving complex control tasks by synthesizing control policies from data. However, the safety and stability of applying DRL to safety-critical systems remain a primary concern and challenging problem. To address the problem, we propose the Phy-DRL: a novel physics-modelregulated deep reinforcement learning framework. The Phy-DRL is novel in two architectural designs: a physics-model-regulated reward and residual control, which integrates physics-modelbased control and data-driven control. The concurrent designs enable the Phy-DRL the mathematically provable safety and stability guarantees. Finally, the effectiveness of the Phy-DRL is validated by an inverted pendulum system. Additionally, the experimental results demonstrate that the Phy-DRL features remarkably accelerated training and enlarged reward.

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Efficient Real2Sim2Real of Continuum Robots Using Deep Reinforcement learning With Koopman Operator

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ieee TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2025年

作者： Ji, Guanglin Gao, Qian Xiao, Yin Sun, Zhenglong Chinese Univ Hong Kong Sch Sci & Engn Shenzhen 518172 Peoples R China Chinese Univ Hong Kong Shenzhen Inst Artificial Intelligence & Robot Soc Shenzhen 518129 Peoples R China

Accurate control of continuum robots is challenging, especially in the presence of external disturbances. To address this issue, reinforcement learning (RL) has been increasingly investigated in continuum robot control due to its online policy updating capability. However, the gap in Sim2Real transfer caused by inaccurate modeling results in RL implementation a dilemma. In this article, we propose a safety-critical Real2Sim2Real online RL framework, where the Real2Sim transfer is first achieved by the identification of a continuum robot using the Koopman operator. We further improve the training efficiency by introducing an imperfect demonstration into the RL framework. The offline policy is trained in simulations and then tested on a real continuum robot platform. During tests, the tracking performance is influenced by the hysteresis effect that cannot be captured by the Koopman operator. This results in a millimeter-level tracking root mean square error (RMSE). To address this issue, we online update the policy as well as the model, and the RMSE of the online controller outperforms the offline controller by 89.16% in free space and 85.70% under external payload, respectively.

关键词： Robots Continuum robots Training Real-time systems Kinematics Aerospace electronics Accuracy Sun Service robots Manipulators Continuum robot data-driven control learning from demonstration reinforcement learning (RL)

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Design and Implementation of RFID-driven Parking Management systems

Design and Implementation of RFID-Driven Parking Management ...

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2024 ieee International conference on Big data and Machine learning, ICBDML 2024

作者： Himabindu, N. Vishnu, S.S. Vishveswaran, M. Swaroop, R. Bairwa, Bansilal SoEEE REVA University Karnataka Bengaluru India

ISBN: (数字)9798350374100

ISBN: (纸本)9798350374100

The present research study provides a comprehensive analysis of a design study aimed at addressing the obstacles encountered in RFID technology implementation for car park systems. By employing RFID tags as a central component and complemented by dedicated software, the system effectively manages various operational aspects, including control, sales reporting, and overall car park management. The integration of RFID technology facilitates automated, secure, and unattended parking processes, thereby mitigating congestion and reducing air pollution. Particularly in urban areas where parking issues contribute to commercial traffic and public frustration, the system utilizes advanced monitoring techniques to direct vehicles to available parking spaces. Furthermore, it incorporates a reservation-based strategy to optimize traffic flow management. The design approach prioritizes simplicity, cost-effectiveness, and the utilization of RFID technology to develop a pragmatic solution for urban parking challenges. © 2024 ieee.

关键词： Traffic congestion

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Behavior Modeling and Bio-Hybrid systems: Using Reinforcement learning to Enhance Cyborg Cockroach in Bio-inspired Swarm Robotics

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ieee Access 2025年 13卷 100119-100148页

作者： Triet, Le Minh Thinh, Nguyen Truong University of Economics Ho Chi Minh City -UEH Institute of Intelligent and Interactive Technologies Ho Chi Minh City Viet Nam

Bio-inspired swarm robotics is an emerging field at the intersection of biology, robotics, and artificial intelligence, offering novel capabilities by integrating living organisms with robotic systems. This paper presents a groundbreaking approach to behavior modeling and enhancement for biological hybrid cockroach robots using reinforcement learning (RL). The study focuses on developing a hybrid system that combines the natural adaptability of cockroaches with the precision of robotics. The RL method predicts and optimizes cockroach movements to enhance task performance in bio-inspired swarm robotics. The proposed methodology includes modeling cockroach behaviors through data-driven techniques, designing a control framework using RL, and integrating these systems into a swarm robotics architecture. By defining precise state-action spaces and reward functions, the RL model effectively learned to influence cockroach behavior via electrical stimulation of their antennae, guiding them to perform complex navigational tasks. Experimental results demonstrate the efficacy of the system in both simulated and real-world environments. The RL framework achieves high prediction accuracy and control fidelity, significantly enhancing the operational capabilities of the bio-hybrid system. Swarm-level tests reveal the potential of this approach for collaborative tasks, such as obstacle avoidance and collective decision-making. This research contributes to the advancement of bio-hybrid systems by bridging biological adaptability and robotic precision, with implications for search-and-rescue missions and environmental monitoring. We discuss ethical considerations to address the challenges of using live organisms. Future work will explore scaling the swarm system and integrating advanced sensors and AI algorithms. © 2013 ieee.

关键词： Robot learning

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A Personalized Federated Framework for Document-level Biomedical Relation Extraction 6

A Personalized Federated Framework for Document-level Biomed...

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6th International conference on data-driven Optimization of Complex systems (DOCS)

作者： Xiao, Yan Jin, Yaochu Zhang, Haoyu Huo, Xu Liu, Qiqi Zheng, Zeqi Shanghai Maritime Univ Coll Informat Engn Shanghai Peoples R China Westlake Univ Sch Engn Hangzhou Peoples R China Hangzhou Normal Univ Sch Informat Sci & Technol Hangzhou Peoples R China Zhejiang A&F Univ Sch Math & Comp Sci Hangzhou Peoples R China

ISBN: (纸本)9798350377859;9798350377842

Document-level relation extraction (RE), a critical task in natural language processing, involves classifying relationships between entities mentioned in documents. Traditional document-level RE methods often rely on centralized datasets, which necessitates the collection of documents from various sources and their storage on a single machine. However, this brings up issues regarding privacy protection, and also limit the diversity of data. In this paper, we solve the document-level RE problem in federated learning scenario and propose a personalized federated learning framework named PF-BRE, specifically designed for biomedical RE. This framework enables multiple clients to collaboratively train a robust model without sharing the raw data. Additionally, to simulate the varying data characteristics of different clients in real-world application scenarios, we process the existing biomedical RE datasets to create distinct clients for personalized federated training. Experiments show that, compared to centralized training, the PF-BRE framework not only achieves impressive performance but also protects the data privacy of each client.

关键词： Personalized Federated learning Biomedical document-level relation extraction

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DRIVE: data-driven Robot Input Vector Exploration

DRIVE: Data-driven Robot Input Vector Exploration

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ieee International conference on Robotics and Automation (ICRA)

作者： Baril, Dominic Deschenes, Simon-Pierre Coupal, Luc Goffin, Cyril Lepine, Julien Giguere, Philippe Pomerleau, Francois Univ Laval Northern Robot Lab Quebec City PQ Canada

ISBN: (纸本)9798350384581;9798350384574

An accurate motion model is a fundamental component of most autonomous navigation systems. While much work has been done on improving model formulation, no standard protocol exists for gathering empirical data required to train models. In this work, we address this issue by proposing data-driven Robot Input Vector Exploration (DRIVE), a protocol that enables characterizing uncrewed ground vehicles (UGVs) input limits and gathering empirical model training data. We also propose a novel learned slip approach outperforming similar acceleration learning approaches. Our contributions are validated through an extensive experimental evaluation, cumulating over 7 km and 1.8 h of driving data over three distinct UGVs and four terrain types. We show that our protocol offers increased predictive performance over common human-driven data-gathering protocols. Furthermore, our protocol converges with 46 s of training data, almost four times less than the shortest human dataset gathering protocol. We show that the operational limit for our model is reached in extreme slip conditions encountered on surfaced ice. DRIVE is an efficient way of characterizing UGV motion in its operational conditions. Our code and dataset are both available online at this link: https://***/norlab-ulaval/DRIVE.

关键词： Robots

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data-driven Distributionally Robust Optimal control with State-Dependent Noise

Data-Driven Distributionally Robust Optimal Control with Sta...

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ieee/RSJ International conference on Intelligent Robots and systems (IROS)

作者： Liu, Rui Shi, Guangyao Tokekar, Pratap Univ Maryland College Pk MD 20742 USA

ISBN: (纸本)9781665491907

Distributionally Robust Optimal control (DROC) is a technique that enables robust control in a stochastic setting when the true distribution is not known. Traditional DROC approaches require given ambiguity sets or a KL divergence bound to represent the distributional uncertainty. These may not be known a priori and may require hand-crafting. In this paper, we lift this assumption by introducing a data-driven technique for estimating the uncertainty and a bound for the KL divergence. We call this technique D3ROC. To evaluate the effectiveness of our approach, we consider a navigation problem for a car-like robot with unknown noise distributions. The results demonstrate that D3ROC provides robust and efficient control policies that outperform the iterative Linear Quadratic Gaussian (iLQG) control. The results also show the effectiveness of our proposed approach in handling different noise distributions.

关键词： Robust control

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Resilient Model Free Adaptive Distributed LFC for Multi-Area Power systems Against Jamming Attacks

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ieee TRANSACTIONS ON NEURAL NETWORKS AND learning systems 2023年第8期34卷 4120-4129页

作者： Qiu, Xiaojie Wang, Yingchun Zhang, Huaguang Xie, Xiangpeng Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110004 Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang 110004 Peoples R China Nanjing Univ Posts & Telecommun Inst Adv Technol Nanjing 210003 Peoples R China

This article is concerned with distributed resilient load frequency control (LFC) for multi-area power interconnection systems against jamming attacks. First, considering uncertainties and high dimension nonlinearity, the model-free adaptive control (MFAC) model is adopted for the power system, in which only input and output (I/O) data are used. Second, jamming attacks are modeled in a stochastic process, and a multistep predictive compensation algorithm is developed to mitigate the impact of jamming attacks. Then, the distributed MFAC protocol with predictive compensation algorithm is designed such that the frequency tracking errors under the predictive compensation algorithm of multi-area power interconnection systems converge consensually into a small neighborhood of origin in the mean square sense. Simulation results show the effectiveness of the approach.

关键词： Power systems Jamming Adaptation models Prediction algorithms Load modeling Frequency control Power system stability data-driven distributed model-free adaptive control (MFAC) jamming attack load frequency control (LFC) predictive compensation algorithm

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Palm Leaves Image Classification Using Deep learning

Palm Leaves Image Classification Using Deep Learning

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ieee International conference on Automatic control and Intelligent systems (I2CACIS)

作者： Hau, Wong Zi Mpuhus, Sikudhan Lucas Badams, Badiu Wahab, Norhaliza Abdul Mirin, Siti Nur Suhaila Shehu, Ibrahim Abdullahi Univ Teknol Malaysia Fac Elect Engn Johor Baharu Johor Malaysia Ahmadu Bello Univ Dept Elect Engn Zaria Nigeria

ISBN: (纸本)9798350372113;9798350372106

Machine vision has extensive applications in agriculture, including developing efficient land management, precise fruit ripeness grading, and plant disease detection. Palm leaves are distinct in their botanical characteristics and have diverse users. However, they are susceptible to diseases, making early detection crucial for maintaining their health and productivity. This study includes preparing balanced data with classes of palm leaf diseases through data augmentation and implementing convolutional neural networks (CNN) in multi-image classification using the processed dataset. Aside from CNN, transfer learning was applied using ResNet152-V2, VGG19, DenseNet201, MobileNet-V2, and InceptionResNetV2 layers to perform image classification. The CNN and ImageNet pre-trained functional layers models require 1492s average execution time and allow the average final model losses to be lower than 0.22, and the average final model accuracies are higher than 95%. The average precision, recall, and F1-score in predicting the brown spots, healthy, and white scale classes are more than 90% for all applied functional layers.

关键词： oil palm leaves disease multiple-image classification data augmentation CNN ResNet152-V2 VGG19 DenseNet201 MobileNet-V2 InceptionResNetV2

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