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Multisource Domain Metalearning Network for Battery State-of-Health Estimation under Multitarget Working Conditions

IEEE Transactions on Power Electronics 2025年第7期40卷 9786-9799页

作者： Miao, Mengqi Xiao, Chaoang Yu, Jianbo Tongji University School of Mechanical Engineering Shanghai201804 China Shanghai Key Laboratory of Wearable Robotics and Human-Machine Interaction Shanghai201804 China

State of health (SOH) estimation is important in battery health prognostics. The discrepancy in working conditions gives rise to the phenomenon of domain shift, which presents a significant obstacle to the accurate estimation of battery SOH. Domain adaptation (DA) has been widely used to solve the domain shift problem in battery SOH estimation by minimizing the distribution discrepancy between different domains. However, the multitarget domain shift problem is not be addressed well for existing methods. Most existing DAs only consider a single target domain, which means that the model needs to be retrained when new scenarios emerge, which differ from the original target working condition. In this article, multisource domain metalearning network (MSDMLN) is proposed for battery SOH estimation under multiple target working conditions. A novel metalearning method, multisource domain metalearning (MSDML) strategy is developed for enhancing the generalization of the network by diversifying battery health degradation features based on meta fusion block. Empirical mode decomposition-densely connected recurrent-convolution network is developed to extract global degradation tendency and local fluctuation features of Li-ion batteries. The effectiveness of MSDMLN is verified on the combined Li-ion battery dataset. The results demonstrate that MSDMLN achieved low mean absolute error (i.e., 0.0474) and normalized root mean square error (i.e., 0.2187) for three different target operating conditions, which is better than other state-of-the-art methods. This illustrates the outperformance of MSDMLN in generalizing the estimation of battery SOH under multitarget working conditions, due to the integration of MSDML and the meta fusion block. © 1986-2012 IEEE.

关键词： Electronic health record

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Robust Finite-Time Containment of Networked Heterogeneous Nonlinear Systems with Intermittent Measurement Only

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IEEE Transactions on Network Science and Engineering 2025年

作者： Tian, Biao Zhang, Hao Cui, Peiyu Wang, Zhuping Yan, Huaicheng Shanghai Key Laboratory of Wearable Robotics and Human-Machine Interaction Tongji University College of Electronic and Information Engineering Department of Control Science and Engineering Shanghai201804 China Ministry of Education Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems China East China University of Science and Technology School of Information Science and Engineering Shanghai200237 China

The robust finite-time containment problem of fully heterogeneous multiagent systems with uncertainties is often challenging, especially when only intermittent output measurement is used. To address the issue posed by the nonidentical system dynamics of multiple leaders, which makes the traditional distributed estimator form for the convex hull inapplicable, a distributed finite-time estimator is constructed for each follower to extract the system matrices and states of leaders. Then, in the output-triggering setting, a finite-time extended state observer driven by intermittent output measurement is developed to reconstruct the uncertainties and unmeasurable states of followers. Meanwhile, the non-continuous output measurement will lead to the differentiation of virtual control laws undefined. To solve this issue, a filtering compensation scheme-based finitetime controller via the backstepping technique is developed for nonlinear followers, ensuring practical finite-time stability (PFTS) of the closed-loop system. It is shown that the proposed algorithm steers each follower into the preset convex combination spanned by the positions of multiple leaders. The capability of the exploited control protocol is verified through simulations and experiments. 2013 IEEE.

关键词： Backstepping

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A Nonlinear MPC-Net Optimization Framework for Wheeled humanoid Robots with Whole Body Dynamics

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IEEE Transactions on Automation Science and Engineering 2025年

作者： Li, Guoxin Liu, Xingjian Wu, Xiaoyu Tang, Qirong Zhang, Hao Li, Zhijun Shi, Peng Tongji University School of Mechanical Engineering Translational Research Center Shanghai YangZhi Rehabilitation Hospital Shanghai Sunshine Rehabilitation Center Shanghai200092 China Shanghai Key Laboratory of Wearable Robotics and Human-Machine Interaction Shanghai201804 China State Key Laboratory of High-Performance Precision Manufacturing Dalian116024 China Tongji University School of Mechanical Engineering Shanghai201804 China National University of Singapore Department of Biomedical Engineering 117583 Singapore Tongji University Department of Control Science and Engineering Shanghai201804 China The University of Adelaide School of Electrical and Mechanical Engineering SA5005 Australia Obuda University Budapest1034 Hungary

The task performance of mobile manipulators can be significantly enhanced by whole-body control and optimization in complex scenarios. Due to the nonlinear properties of whole-body dynamics and parameter uncertainty, modeling accurate system dynamics is essential in addition to designing an effective control strategy. However, traditional control methods have high computational costs and fail to deal with the parameter errors caused by model linearization. To address these issues, we propose a model predictive control (MPC)-Net, a learning-based approach that facilitates rapid online optimization by combining deep learning with multiple MPCs. Firstly, we develop a parameter identification algorithm based on a deep neural model to estimate the unknown dynamics parameters. Although the control performance of the MPC approach positively correlated with the prediction horizon, a long horizon would result in additional computational costs. Thus, MPC-Net is constructed by combining multiple sub-MPC issues, and the nonlinear coefficients are obtained by using a deep neural network-based optimization framework. Furthermore, MPC-Net generates the solution by combining the outputs of multiple sub-MPC problems using the nonlinear transformation of learned coefficients. Experiments are conducted on a mobile manipulator, which demonstrates the proposed MPC-Net-based optimization control offers fast efficient computation and low tracking error performance. © 2004-2012 IEEE.

关键词： Predictive control systems

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AnyTSR: Any-Scale Thermal Super-Resolution for UAV

arXiv

引用

arXiv 2025年

作者： Li, Mengyuan Fu, Changhong Lu, Ziyu Zhang, Zijie Zuo, Haobo Yao, Liangliang School of Mechanical Engineering Tongji University Shanghai201804 China Shanghai Key Laboratory of Wearable Robotics and Human-Machine Interaction Tongji University Shanghai201804 China Department of Computer Science University of Hong Kong Hong Kong999077 Hong Kong

Thermal imaging can greatly enhance the application of intelligent unmanned aerial vehicles (UAV) in challenging environments. However, the inherent low resolution of thermal sensors leads to insufficient details and blurred boundaries. Super-resolution (SR) offers a promising solution to address this issue, while most existing SR methods are designed for fixed-scale SR. They are computationally expensive and inflexible in practical applications. To address above issues, this work proposes a novel any-scale thermal SR method (AnyTSR) for UAV within a single model. Specifically, a new image encoder is proposed to explicitly assign specific feature code to enable more accurate and flexible representation. Additionally, by effectively embedding coordinate offset information into the local feature ensemble, an innovative any-scale upsampler is proposed to better understand spatial relationships and reduce artifacts. Moreover, a novel dataset (UAV-TSR), covering both land and water scenes, is constructed for thermal SR tasks. Experimental results demonstrate that the proposed method consistently outperforms state-of-the-art methods across all scaling factors as well as generates more accurate and detailed high-resolution images. The code is located at https:// ***/vision4robotics/AnyTSR. Copyright © 2025, The Authors. All rights reserved.

关键词： Drones

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