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6th International Conference on Energy, Electrical and Power Engineering, CEEPE 2023

作者： Yao, Yue Wang, Ying Zhang, Jiangfeng Zhang, Kaifeng Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China State Grid Zhejiang Electric Power Research Institute Zhejiang China

ISBN: (纸本)9798350348279

Accurate estimation for state-of-health (SOH) of lithium-ion batteries is critical to ensure the security and durability of battery operation. Accordingly, this paper proposes a SOH estimation method based on Attention-LSTM neural network using the partial charging features. First, features are extracted from partial constant-current charging curves. Second, Pearson coefficient analysis is employed to analysis the mapping relationship between extracted features and SOH. Third, an Attention-LSTM network is utilized to realize the SOH estimation. The attention mechanism can adaptively assign weights to the input features and time steps, thereby improving the estimation accuracy. Validation experiments are conducted on public dataset from NASA Ames Prognostics Center of Excellence. The experimental results highlight that the proposed method has higher accuracy and robustness compared with other approaches. © 2023 IEEE.

关键词： Lithium-ion batteries

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Composite Model Predictive control for PMSM Servo systems Based on Backlash Cancellation and Disturbance Compensation 41

Composite Model Predictive Control for PMSM Servo Systems Ba...

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第41届中国控制会议

作者： Lei Li Zuo Wang Shihua Li School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Shenzhen Research Institute Southeast University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

This paper investigates the speed regulation problem of permanent magnet synchronous motor(PMSM) servo system with backlash based on model predictive control(MPC).It is known that the existing backlash in servo systems causes steady-state errors,limit cycles and rigid *** order to counteract the adverse performance degradations of the backlash nonlinearity,a composite MPC strategy is investigated based on backlash cancellation and disturbance ***,an approximated dead-zone model of backlash is ***,a generalized proportional-integral observer(GPIO) is constructed to estimate the lumped disturbances,including backlash modelling residual error,parameter uncertainties and external *** introducing disturbance estimation and backlash transmission torque into system model,a composite MPC can be obtained based on the prediction-precision enhanced model,which brings significantly improvements to control ***,the efficacy of the presented method is demonstrated by simulation results.

关键词： Model predictive control(MPC) PMSM servo system Backlash cancellation Generalized proportional-integral observer(GPIO)

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Self-supervised Depth Completion with Adaptive Online Adaptation 23

Self-supervised Depth Completion with Adaptive Online Adapta...

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7th International Conference on Machine Learning and Soft Computing, ICMLSC 2023

作者： Chen, Yang Tan, Yang Key Laboratory of Measurement and Control of Complex System of Engineering Ministry of Education School of Automation Southeast University Nanjing210096 China

ISBN: (纸本)9781450398633

Although depth completion has achieved remarkable performance relying on deep learning in recent years, these models tend to suffer a performance degradation when exposed to new environments. Online adaptation, where the model is trained in a self-supervised manner during testing, seems a promising technique to alleviate the drop. However, continuous online adaptation may cause the model to over-adapt and miss the optimal parameters, resulting in oscillation or even degradation of the model performance, in addition to wasting computational resources. Therefore, this paper proposes an adaptive online adaptation framework to make model adaptively trigger online adaptation when encountering novel environments and stop adaptation when model has adapted to the current environment. In detail, we design a trigger to detect the familiarity of model to the current scenario based on image similarity and then launch online adaptation when the scenario is novel. Besides, we elaborate a stopper to monitor the error between prediction and depth input and convert online adaptation to inference when online adaptation does not bring improvement for model. Experimental results demonstrate that our method improves the accuracy of model prediction and increases average running speed of the model on each frame in online adaptation. © 2023 ACM.

关键词： Deep learning

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Distributed Optimization and Scaling Design for Solving Sylvester Equations

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Journal of systems Science & complexity 2024年第6期37卷 2487-2510页

作者： CHENG Songsong YU Xin ZENG Xianlin LIANG Shu HONG Yiguang Anhui Engineering Laboratory of Human-Robot Integration System and Intelligent Equipment School of Electrical Engineering and AutomationAnhui UniversityHefei 230601China Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of TechnologyBeijing 100081China Department of Control Science and Engineering Tongji UniversityShanghai 201804China Shanghai Research Institute for Intelligent Autonomous Systems Tongji UniversityShanghai 201210China

This paper develops distributed algorithms for solving Sylvester *** authors transform solving Sylvester equations into a distributed optimization problem,unifying all eight standard distributed matrix *** the authors propose a distributed algorithm to find the least squares solution and achieve an explicit linear convergence *** results are obtained by carefully choosing the step-size of the algorithm,which requires particular information of data and Laplacian *** avoid these centralized quantities,the authors further develop a distributed scaling technique by using local information *** a result,the proposed distributed algorithm along with the distributed scaling design yields a universal method for solving Sylvester equations over a multi-agent network with the constant step-size freely chosen from configurable ***,the authors provide three examples to illustrate the effectiveness of the proposed algorithms.

关键词： Distributed optimization least squares solution linear convergence rate step-size interval Sylvester equation

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Generalized Proportional Integral Observer Based Sliding Mode control for Biaxial Motion control System with Mismatched Disturbances 41

Generalized Proportional Integral Observer Based Sliding Mod...

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第41届中国控制会议

作者： Guancheng Ren Zuo Wang Shihua Li School of Automation Southeast University Key Laboratory of Measurement and Control of Complex systems of Engineering Ministry of Education Shenzhen Research Institute Southeast University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In the biaxial motion system,nonlinearities and mismatched disturbances always have adverse effects on the contouring accuracy,which are often *** of existing advanced control methods are developed only to suppress matched *** this paper,the necessity of taking the contour error as the control target is ***,four generalized proportional integral observers(GPIOs) are developed to estimate both matched and mismatched lumped ***,based on contour error,a sliding mode controller is developed for the biaxial motion *** introducing the disturbance estimations into the modified sliding surface design,a composite controller is *** proposed method effectively improves the contouring accuracy and suppress the mismatched disturbances,while the chattering caused by large switching gains is *** stability analysis of the system is established by means of the Lyapunov ***,to demonstrate the superiority and effectiveness of the controller,simulation results of the biaxial motion control system are presented in various working cases.

关键词： Biaxial motion control system contouring control mismatched disturbances generalized proportional-integral observer(GPIO) sliding mode control chattering reduction

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Attention-Modulated Feature Fusion Neural Network for Inverse Modeling of Microwave Filters

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IEEE Microwave and Wireless Technology Letters 2025年第4期35卷 376-379页

作者： Guo, Linwei Cao, Weihua Hu, Wenkai Lu, Zhengyang Wu, Min China University of Geosciences School of Automation Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China

Inverse modeling is extensively applied in the design and tuning of microwave filters (MFs). Inverse models (IMs) take the features extracted from the high-dimensional electromagnetic parameters as input. How to make full use of features from multiple perspective is a critical issue for improving model accuracy. To solve it, this letter proposes an attention-modulated feature fusion neural network (AMFFNN). AMFFNN achieves multiperspective feature fusion (MPFF) at the input side and independent feature fusion (IFF) at the output side. In addition, feature fusion in AMFFNN is enhanced by attention modules to dynamically identify the importance of each feature. Statistical and comparative results of simulations demonstrate that AMFFNN outperforms existing methods in terms of accuracy, stability, and generalization. © 2023 IEEE.

关键词： Inverse problems

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Optimal Strategy for Aircraft Pursuit-evasion Games via Self-play Iteration

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Machine Intelligence Research 2024年第3期21卷 585-596页

作者： Xin Wang Qing-Lai Wei Tao Li Jie Zhang State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing100049China Institute of Systems Engineering Macao University of Science and TechnologyMacao999078China

In this paper,the pursuit-evasion game with state and control constraints is solved to achieve the Nash equilibrium of both the pursuer and the evader with an iterative self-play *** the condition where the Hamiltonian formed by means of Pontryagin’s maximum principle has the unique solution,it can be proven that the iterative control law converges to the Nash equilibrium ***,the strong nonlinearity of the ordinary differential equations formulated by Pontryagin’s maximum principle makes the control policy difficult to figured *** the system dynamics employed in this manuscript contains a high dimensional state vector with *** practical applications,such as the control of aircraft,the provided overload is ***,in this paper,we consider the optimal strategy of pursuit-evasion games with constant constraint on the control,while some state vectors are restricted by the function of the *** address the challenges,the optimal control problems are transformed into nonlinear programming problems through the direct collocation ***,two numerical cases of the aircraft pursuit-evasion scenario are given to demonstrate the effectiveness of the presented method to obtain the optimal control of both the pursuer and the evader.

关键词： Differential games pursuit-evasion games nonlinear control optimal control Nash equilibrium solution

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Multi-task Learning Based Single Temporal Remote Sensing Image Change Detection 23

Multi-task Learning Based Single Temporal Remote Sensing Ima...

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Proceedings of the 2023 12th International Conference on Computing and Pattern Recognition

作者： Peixuan Yang Yichao Cao Xiaobo Lu Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University China

ISBN: (纸本)9798400707988

Change Detection is a classic and fundamental task in remote sensing(RS), and has accomplish remarkable achievement on bitemporal image based methods. However, bitemporal images are very expensive and needs professional researchers to align and label the paired images. Furthermore, most bitemporal image based methods are only semantic-aware not instance-aware, which indicates these methods only use paired images’ semantic information. Thus, we propose a method that only needs unpaired RS images with semantic labels. In this paper, we introduce multi-task learning into change detection progress, which comprises four major parts: Boundary-Aware Module, Contextual-Aware Module, Segmentation Module, Change Detection Module. At first, we construct Boundary-Aware Module and Contextual-Aware Module to extract sharp boundary and dense contextual features. Then we feed the feature maps to Change Detection Module and Segmentation Module to accomplish the subtasks. The comprehensive experiments on a single temporal image dataset demonstrate the efficiency of our approach over other bitemporal image based methods, and the experimental results shows our approach is competitive with other methods while all methods was trained on bitemporal image dataset.

关键词： Change Detection Multi-task Learning Single Temporal Image

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ChatGPT Chats on Computational Experiments:From Interactive Intelligence to Imaginative Intelligence for Design of Artificial Societies and Optimization of Foundational Models

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IEEE/CAA Journal of Automatica Sinica 2023年第6期10卷 1357-1360页

作者： Xiao Xue Xiangning Yu Fei-Yue Wang College of Intelligence and Computing Tianjin UniversityTianjin 300350China State Key Laboratory for Management and Control of Complex Systems the Institute of Automdtion of Chinese Academg of SciencesBeijing 100190China

POWERED by the rapid development of Internet,the penetration of the Internet of Things,the emergence of big data,and the rise of social media,more and more complex systems are exhibiting the characteristics of social,physical,and information *** systems are known as cyber-physicalsocial systems(CPSS)[1],[2].These CPSS face unprecedented challenges in design,analysis,management,control and integration due to their involvement with human and social factors[3],[4].To cope with this challenge,there are two main approaches to CPSS research.

关键词： Intelligence Foundation involvement

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The Journey/DAO/TAO of Embodied Intelligence: From Large Models to Foundation Intelligence and Parallel Intelligence

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IEEE/CAA Journal of Automatica Sinica 2024年第6期11卷 1313-1316页

作者： Tianyu Shen Jinlin Sun Shihan Kong Yutong Wang Juanjuan Li Xuan Li Fei-Yue Wang College of Information Science and Technology Beijing University of Chemical TechnologyBeijing 100029China School of Electrical and Information Engineering Jiangsu UniversityZhenjiang 212013China State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and RoboticsCollege of EngineeringPeking UniversityBeijing 100871China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Peng Cheng Laboratory Shenzhen 518000China State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China IEEE

THE tremendous impact of large models represented by ChatGPT[1]-[3]makes it necessary to con-sider the practical applications of such models[4].However,for an artificial intelligence(AI)to truly evolve,it needs to possess a physical“body”to transition from the virtual world to the real world and evolve through interaction with the real *** this context,“embodied intelligence”has sparked a new wave of research and technology,leading AI beyond the digital realm into a new paradigm that can actively act and perceive in a physical environment through tangible entities such as robots and automated devices[5].

关键词： Intelligence DAO TAO

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