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10th IEEE International Power Electronics and Motion control Conference, IPEMC 2024 ECCE Asia

作者： Su, Bowen Sun, Kai Yang, Min Xiao, Yunhao Zhang, Kai Liu, Bo Tsinghua University State Key Laboratory of Power System Operation and Control Department of Electrical Engineering China

ISBN: (纸本)9798350351330

Magnetic components play an important role in power electronic equipment, but there is still lack of accurate magnetic core loss estimation model. The data-driven methods are well-developed in recent years, and they are also more robust and accurate than conventional model-driven methods like Steinmetz Equation (SE) and improved Generalized Steinmetz Equation (iGSE). However, existing data-driven methods often focus on single excitation waveform like sinusoidal excitation waveform, which is not robust enough when facing complex excitation waveform with many high-frequency harmonic waveform. This paper presents a data-driven model based on attention mechanism which can capture the inner relationship within the input excitation waveform to present a more accurate core loss estimation results. And the comparison results with Multi-layer Perceptron (MLP) and Long Short-Term Memory(LSTM) models indicates that the proposed model has much more accuracy when facing complex excitation waveform. The proposed model can help to design magnetic components. © 2024 IEEE.

关键词： Facings

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Design and application of deployable heat radiator

Design and application of deployable heat radiator

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2024 International Conference on Aerospace and Mechanics, ICAM 2024

作者： Li, Jin Zhao, Qiwei Zheng, Hongyang Zhang, Yang Yu, Xingang National Key Laboratory of Spacecraft Thermal Control Beijing Institute of Spacecraft System Engineering Beijing China

Limited by the envelope size of the launch vehicle, comparing with the body-mounted radiator of the traditional spacecraft, the thermal control scheme based on single-phase fluid loop is proposed in order to improve the system-level heat dissipation. The thermal analysis model of the deployable heat radiator, including the spacecraft, is established. Through the comparative analysis of the fin parameter, infrared radiation effect, and working fluid selection, the heat dissipation efficiency of the radiator is improved, and the first flight application of the deployable heat radiator is realized. The research results show that in view of the GEO spacecraft's bad external heat flow condition, the heat dissipation of a single radiator can reach 250W/m2 above;the method of using multiple deployable heat radiators can effectively solve the problem of insufficient heat dissipation capacity on the spacecraft. © 2024 Institute of Physics Publishing. All rights reserved.

关键词： Radiators

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HRPose: Real-Time High-Resolution 6D Pose Estimation Network Using Knowledge Distillation

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Chinese Journal of Electronics 2023年第1期32卷 189-198页

作者： GUAN Qi SHENG Zihao XUE Shibei Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management

Real-time six degrees-of-freedom(6D)object pose estimation is essential for many real-world applications, such as robotic grasping and augmented reality. To achieve an accurate object pose estimation from RGB images in real-time, we propose an effective and lightweight model, namely high-resolution 6D pose estimation network(HRPose). We adopt the efficient and small HRNetV2-W18 as a feature extractor to reduce computational burdens while generating accurate 6D poses. With only 33% of the model size and lower computational costs, our HRPose achieves comparable performance compared with state-of-the-art models. Moreover, by transferring knowledge from a large model to our proposed HRPose through output and feature-similarity distillations, the performance of our HRPose is improved in effectiveness and efficiency. Numerical experiments on the widely-used benchmark LINEMOD demonstrate the superiority of our proposed HRPose against state-of-the-art methods.

关键词： Knowledge engineering Computational modeling Pose estimation Grasping Benchmark testing Feature extraction Real-time systems

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Parameter-level Mask-based Adapter for 3D Continual Learning with Vision-Language Model

Parameter-level Mask-based Adapter for 3D Continual Learning...

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2024 IEEE International Conference on Robotics and Biomimetics, ROBIO 2024

作者： Qian, Longyue Zhang, Mingxin Zhang, Qian Zhang, Lin Li, Teng Zhang, Wei School of Control Science and Engineering Shandong University Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan250100 China

ISBN: (纸本)9781665481090

As 3D object classification becomes increasingly essential in various applications, the need for models to continuously adapt to new scenarios is more crucial than ever. However, existing continual learning methods primarily concentrate on 2D tasks, making their direct application to 3D tasks challenging due to the inherent domain gap. To address this issue, we propose Parameter-level Mask-based Adapter (PMA) for 3D continual learning with Vision-Language Models. Our approach leverages the power of Contrastive Language-Image Pre-Training (CLIP) to extract rich multimodal features by projecting 3D point clouds into multi-angle depth maps, which are then processed by the 2D encoder aligned with rendered images. The core of our method is a parameter-level adapter with learnable masks that effectively integrates 3D point cloud features with 2D features. This mechanism flexibly selects the most relevant subsets of weights for each task, allowing subsequent tasks to utilize the learned weights without the need for updates, which effectively balances stability and plasticity during continual learning. Experimental results show that our method significantly outperforms existing state-of-the-art techniques. © 2024 IEEE.

关键词： Contrastive Learning

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P2CBAM: 3D Object Detection for Autonomous Driving

P2CBAM: 3D Object Detection for Autonomous Driving

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2024 IEEE International Conference on Robotics and Biomimetics, ROBIO 2024

作者： Li, Ruitong Zhang, Tao Xu, Xiaoyu Zhao, Yuenan Song, Ran Zhang, Wei School of Control Science and Engineering Shandong University Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan250061 China

ISBN: (纸本)9781665481090

Efficient and accurate object detection from 3D point clouds is crucial for autonomous driving. Due to the sparsity and irregularity of real-world point clouds, as well as the real-time requirements for detectors, existing 3D object detectors face significant challenges in practical applications. PointPillars is widely used in real-world scene due to its fast detection speed, but its accuracy lags behind some existing methods. To enhance the detection accuracy of this model, we propose P2CBAM, which integrates CBAM into the PointPillars baseline model. This integration improves detection accuracy with relatively minimal additional computational resources. Extensive experiments conducted on the KITTI and Waymo Open datasets demonstrate that P2CBAM outperforms the baseline model on both datasets. The code is available at https://***/Bennyliiiiii/P2CBAM. © 2024 IEEE.

关键词： HTTP

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Learning to Navigate Sequential Environments: A Continual Learning Benchmark for Multi-modal Navigation

Learning to Navigate Sequential Environments: A Continual Le...

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2024 IEEE International Conference on Robotics and Biomimetics, ROBIO 2024

作者： Xie, Yinduo Zhao, Yuenan Zhang, Qian Zhang, Lin Li, Teng Zhang, Wei School of Control Science and Engineering Shandong University Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan250061 China

ISBN: (纸本)9781665481090

Existing works for robot navigation typically focus on performance in specific tasks, overlooking the adaptability of navigation strategies to sequential environments. In this paper, we present CLBMN, a continual learning (CL) benchmark for multi-modal robot navigation, which aims to enhance robots' navigation capabilities in handling sequential tasks. The benchmark is composed of a multi-modal dataset, a CL-based algorithm, and three comprehensive evaluation metrics for CL-based multi-modal navigation. Specifically, we construct a multi-modal dataset with 10,000 samples by collecting images, point clouds, and odometry data from five scenarios, including daytime, nighttime, rainy conditions, highway, and field. Then, we propose an end-to-end navigation framework with momentum update to mitigate catastrophic forgetting across various environments, which provides a baseline approach for CL-based robot navigation. Furthermore, we define three evaluation metrics specifically designed to evaluate the robot's continual navigation performance across different scenarios. Experimental results demonstrate that the proposed benchmark is effective for studying multi-modal continual robot navigation in sequential environments. Our project page is available at https://***/clbmn/. © 2024 IEEE.

关键词： Robot learning

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Neural-based prescribed-time consensus control for multiagent systems via dynamic memory event-triggered mechanism

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Science China(Technological Sciences) 2025年第3期68卷 217-227页

作者： Xiaohong ZHENG Hui MA Qi ZHOU Hongyi LI School of Automation Guangdong-Hong Kong Joint Laboratory for Intelligent Decision and Cooperative Controland Guangdong Provincial Key Laboratory for Intelligent Decision and Cooperative ControlGuangdong University of TechnologyGuangzhou510006China School of Mathematics and Statistics Guangdong University of TechnologyGuangzhou510006China College of Electronic and Information Engineering and the Chongqing Key Laboratory of Generic Technology and System of Service Robots Southwest UniversityChongqing400715China

This work investigates the implementation of distributed prescribed-time neural network(NN)control for nonlinear multiagent systems(MASs)using a dynamic memory event-triggered mechanism(DMETM).First,it introduces a composite learning technique in NN *** method leverages the prediction error within the NN update law to enhance the accuracy of the unknown nonlinearity ***,by introducing a time-varying transformation,the study establishes a distributed prescribed-time control *** notable feature of this algorithm is its ability to predetermine the convergence time independently of initial conditions or control ***,the DMETM is established to reduce the actuation frequency of the *** the conventional memoryless dynamic event-triggered mechanism,the DMETM incorporates a memory term to further increase triggering *** a distributed estimator for the leader,the DMETM-based NN prescribed-time controller is designed in a fully distributed manner,which guarantees that all signals in the closed-loop system remain bounded within the prescribed ***,simulation results are presented to validate the effectiveness of the proposed algorithm.

关键词： consensus control composite learning control dynamic memory event-triggered control prescribed-time control multiagent systems(MASs)

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Diagnosis of unbalanced bearing fault samples based on cycle generative adversarial networks

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Neural Computing and Applications 2025年 1-21页

作者： Huang, Rihao Ma, Liangyu Hu, Jingchen Duan, Xiaochong Gao, Haitian Ma, Jin School of Control and Computering Engineering North China Electric Power University Baoding071003 China Baoding Key Laboratory of State Detection and Optimization for Integrated Energy System Baoding071003 China

The robust feature extraction capability of deep learning (DL) has rendered it a popular choice for fault diagnosis in various industries. However, the accuracy of fault diagnosis models is often compromised due to an imbalance between normal and faulty data during the production process. To address this issue, this study proposes an unbalanced bearing fault sample diagnostic method based on a cycle generative adversarial network, using vibration fault diagnosis of rotating machinery rolling bearings as an illustrative example for validation purposes. The method utilizes continuous wavelet transform (CWT) to convert the original vibration signal into time–frequency maps, which are then utilized as input for the cycle generative adversarial network. To address issues related to unstable training and timely convergence of the model, spectral normalization and weight decay techniques are introduced, thereby enhancing the performance of the improved cycle generative adversarial network in generating additional fault samples. Finally, the Swin Transformer model is employed for fault diagnosis and compared with other methods such as Random Forest (RF), Sparse Autoencoder (SAE), Support Vector Machine (SVM), and Convolutional Neural Network (CNN). The diagnostic experiment encompasses sample expansion, imbalanced proportion between normal and faulty samples, as well as scenarios involving limited sample sizes. Experimental results demonstrate that the proposed method can generate higher quality synthetic samples when training data is scarce;moreover, utilizing the Swin Transformer for diagnosis yields superior accuracy compared to alternative approaches such as RF, SAE, SVM, or CNN models—thus showcasing significant potential in addressing imbalanced data within fault diagnosis. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Convolutional neural networks

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A Modulation Scheme of CLLC Converters for Bidirectional Power Transfer with Reduced Switching Frequency Range

A Modulation Scheme of CLLC Converters for Bidirectional Pow...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Wang, Leheng Chen, Huan Sun, Kai Tsinghua University State Key Laboratory of Power System Operation and Control Department of Electrical Engineering Beijing China

ISBN: (纸本)9798350376067

CLLC converters are broadly used in battery energy storage systems (BESS), where the capabilities of bidirectional operation and voltage regulation are desired. In the existing bidirectional power transfer scheme, pulse frequency modulation (PFM) is used to realize the reciprocal gain of forward and backward directions. It leads to not only large frequency modulation range but also asymmetric transfer characteristics. In this paper, a novel modulation scheme of CLLC converters for bidirectional power transfer is proposed. In the proposed backward power transfer scheme, the driving signals of primary and secondary-side switches are aligned at the falling-edge, which brings about symmetrical characteristics as forward scheme. This symmetry further ensures the identical switching frequency in both directions, which simplifies parameter design, minimizes switching frequency range, and improves efficiency. A 10-kW prototype is built and the experimental results validate the effectiveness of the proposed scheme, offering potential for cost-effective and efficient applications. © 2024 IEEE.

关键词： Switching frequency

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Improved Deep Reinforcement Learning for Efficient Motion control of Autonomous Vehicle with Domain-Centralized Electronic and Electrical Architecture

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IEEE Internet of Things Journal 2025年第11期12卷 15620-15635页

作者： Du, Guodong Zou, Yuan Zhang, Xudong Fan, Jie ETH Zurich Institute of Dynamic System and Control Zurich8092 Switzerland Beijing Institute of Technology National Engineering Laboratory for Electric Vehicles School of Mechanical Engineering Beijing China Beijing Institute of Technology National Engineering Laboratory for Electric Vehicles School of Mechanical Engineering Collaborative Innovation Center of Electric Vehicles Beijing China

With the rapid development of intelligent connected vehicles, the domain-based electronic and electrical (E/E) architecture is providing the potential upgrade for the autonomous vehicle. As a representative, the domain-centralized E/E architecture can be installed in the autonomous vehicle which performs its powerful software updates, cabling reduction, and functional integration. For the efficient and stable motion control of autonomous vehicles equipped with domain-centralized E/E architecture, this paper proposes an improved deep reinforcement learning framework based on multi-hops loop delay and accelerated gradient optimization. Firstly, the domain-centralized E/E architecture and motion control problem of autonomous vehicle are modeled, respectively. Then, a multi-hops loop delay analysis is carried out for the E/E architecture to estimate the theoretical boundary value of heterogeneous topology loop delay. Subsequently, the deep reinforcement learning algorithm using modified heuristic experience replay is developed for the motion control of autonomous vehicle equipped with domain-centralized E/E architecture. In the implementation of deep reinforcement learning system, the estimated loop delay value is integrated into the motion controller optimization, and the Nesterov accelerated gradient is introduced and combined with the adaptive moment estimation to improve the optimization effect. Finally, the real-world scenarios and virtual driving environment simulation are applied to evaluate the performance of the improved deep reinforcement learning framework. The results show that the proposed motion control framework achieves better performance and guarantees the stability to the loop delay caused by domain-centralized E/E architecture. © 2014 IEEE.

关键词： Deep reinforcement learning

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