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Research Square 2021年

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

The applications of the continuous feedback method to achieve both path following and a formation moving along desired orbits at a finite time is presented. It is assumed that the topology among the virtual leader and the followers is directed. An additional condition of so called barrier function to yield all the agents moving within a limited area is designed. A novel continuous finite‑time path following control law is first designed based on the barrier function and backstepping. Then a novel continuous finite‑time formation algorithm is designed by regarding the path following errors as disturbances. The settling time properties of the resulting system are studied in detail. Simulations are presented to validate the proposed strategies. © 2021, CC BY.

关键词： Multi agent systems

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Model-Based Prediction of Grinding Surface Roughness With Error Correction via a Knowledge-Based Fuzzy Broad Learning System

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IEEE Transactions on Instrumentation and measurement 2025年 74卷

作者： Zewen Hu Hongcai Chen Kanjian Zhang Haikun Wei Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Southeast University Nanjing China Southeast University Shenzhen Research Institute Shenzhen China

Surface roughness is a key indicator of product quality. Traditional measurement methods generally rely on manual trials and inspections, which are time-consuming and costly. Therefore, developing an accurate and reliable prediction model is vital for automatic grinding, as it enables more informed decision-making, reduces waste, and improves efficiency. However, current model- and data-driven methods suffer from either low accuracy or poor interpretability. To address these challenges, this article proposes an enhanced hybrid framework that integrates a mechanistic model-based predictor with a knowledge-based fuzzy broad learning system (KBFBLS) for error correction. The mechanistic model offers a physically interpretable baseline estimate of surface roughness, while KBFBLS enhances prediction accuracy by learning the mapping from process parameters to the residual errors of the mechanistic model. Built upon the fuzzy broad learning system (FBLS)—an emerging neuro-fuzzy model for efficient nonlinear modeling—KBFBLS integrates expert knowledge-guided fuzzy partition and variance-determined Gaussian membership function (MF) widths, two novel strategies that further improve the system’s expressiveness and adaptability, making it a powerful error corrector. Experimental results on real-world robotic disk grinding tasks show that the proposed framework outperforms representative model-driven, data-driven, and hybrid methods. Furthermore, its adaptability to other machining processes is validated using the wheel grinding and milling datasets.

关键词： Surface roughness Rough surfaces Surface treatment Predictive models Adaptation models Learning systems Training Accuracy Milling Error correction

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Modulated-Virtual-Vector-Based Predictive Current control for Dual Three-Phase PMSM With Enhanced Steady-State Performance

Modulated-Virtual-Vector-Based Predictive Current Control fo...

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Annual Conference of Industrial Electronics Society

作者： Ze Li Xingyu Zheng Jinhui Xia Liling Wang Yuanbo Guo Xiaonan Gao Zhiheng Liu Xiaohua Zhang College of Electronic and Information Engineering Hebei University Baoding China School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering (Southeast University) Ministry of Education Southeast University Nanjing China School of Electrical Engineering Dalian University of Technology Dalian China School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden The School of Electrical Engineering Dalian University of Technology Dalian China

Dual three-phase permanent magnet synchronous machine (DTP-PMSM) has attracted great attention due to its high reliability and high-power output capacities. However, the conventional single-voltage-vector-based predictive current control (SV-PCC) for DTP-PMSM presents high torque ripple and current harmonics, and high computational burden. To solve those issues, a modulated-virtual-vector-based PCC (MVV-PCC) for DTP-PMSM is proposed in this paper. Wherein, twenty-four VVs are synthesized by the inherent voltage vectors, and two VVs and one zero voltage vector with optimal duty cycles are determined and applied in each sampling period to improve the steady-state performance. The selection of optimal VVs and the calculation of the optimal duty cycles are simplified by integrating the deadbeat control and modulation scheme. Various comparisons are carried out to validate the effectiveness and superiority of the proposed MVV-PCC strategy.

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An enhanced algorithm for cell-level anomaly segmentation in photovoltaic solar panels using electroluminescence imaging

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Energy 2025年 331卷

作者： Ruiyao Duan Yongjian Wang Xisong Chen Shihua Li School of Automation Southeast University Nanjing 210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing 210096 PR China Institute for Automatic Control and Complex System University of Duisburg–Essen Duisburg 47057 Germany

Electroluminescence imaging is a crucial diagnostic tool for assessing the quality and performance of photovoltaic (PV) modules. However, current research often focuses on defect detection in PV modules, neglecting the need for detailed segmentation at the individual cell level. To address the limitations of existing methods in recognizing complex defects and suppressing background noise, this paper proposes a novel semantic segmentation algorithm (CAAK-Net), capable of identifying anomalies at the cell level of photovoltaic panels. To enhance network performance, CAAK-Net uses K-Net as a baseline and incorporates the Convolutional Block Attention Module (CBAM), Attention Refinement Module (ARM), and Atrous Spatial Pyramid Pooling (ASPP) modules. Experimental results comparing CAAK-Net with mainstream segmentation networks demonstrate its superior segmentation performance, particularly in recognizing defect edges and small-area attached defects. Additionally, we establish a quantitative correlation model between detection errors and PV system efficiency losses, pioneering the translation of pixel-level segmentation accuracy into measurable operational costs, thereby providing an economic assessment framework for industrial PV inspection. Furthermore, the network exhibits a certain degree of robustness in noisy environments, showcasing its segmentation advantages in diverse defect scenarios.

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Output feedback based high-order sliding mode control design of electrohydraulic system using an exact differentiator

Output feedback based high-order sliding mode control design...

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control Technology and Applications (CCTA),

作者： Hao Sun Zongxuan Sun Shihua Li Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Southeast University Nanjing PR China University of Minnesota-Twin Cities Minneapolis Minnesota USA

ISBN: (纸本)9781665436441

Electrohydraulic systems, which have been widely used in a broad range of applications, are a typical nonlinear system with uncertainties. The presence of nonlinearities, parameter variations degrades the performance of linear controllers, such as the PID controller. As one of the most robust control strategies, sliding mode control methods attract the attention of researchers and are applied to electrohydraulic systems. However, for many applications, only the piston position measurement information is available. Owing to the existence of the nonlinear orifice equation, the observer design is a challenging task and satisfactory performance is not guaranteed in the existing literature. Therefore, an output feedback based high-order sliding mode control (HOSMC) design of electrohydraulic systems is proposed in this paper. By using an exact differentiator, the derivatives of the tracking error are obtained and used to construct the HOSMC. Compared to the conventional sliding mode control (SMC), the HOSMC can achieve a finite-time convergence and improved tracking accuracy. To verify the tracking performance, a simulation is performed on a camless engine valve actuation system.

关键词： Robust control Uncertainty Switching frequency Simulation Position measurement Valves Electrohydraulics

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Cooperative control Based on Distributed Attack Identification and Isolation

Cooperative Control Based on Distributed Attack Identificati...

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International Conference on control, Automation, Robotics and Vision (ICARCV)

作者： Hailing Liu Dalin Huang Ya Zhang Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China

ISBN: (数字)9781728177090

ISBN: (纸本)9781728177106

This paper studies the secure consensus control of multi-agent systems where some agents are injected into malicious attacks and compromised. We focus on the distributed detection and identification of attacks on agents and the realization of consensus of agents against attacks. Firstly, we design monitors that can perform precise distributed attack detection. Then, we propose a distributed attack identification algorithm by constructing distributed observers. After that, a cooperative controller is proposed which ensures the normal control of systems by means of isolating the agents which are attacked. Finally, we validate our results through simulation examples.

关键词： Multi-agent systems Monitoring Sensors Sensor systems Security Observers Data models

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MADDPG Algorithm for Coordinated Welding of Multiple Robots

MADDPG Algorithm for Coordinated Welding of Multiple Robots

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作者： Weibin Chen Lei Hua Leixin Xu Benshun Zhang Mengmeng Li Tao Ma Yang-Yang Chen School of Automation Southeast University Robot Department Jiangsu Automation Research Institute School of Automation Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University

This paper deals with the coordinated welding problem of multi-robot systems by applying a deep reinforcement learning algorithm which is called multi-agent deep deterministic policy gradient(MADDPG).It is assumed that the states and actions of robots are continuous and each robot can only get local information of its neighbors.A novel reward composed of trajectory optimization,coordinated welding and collision avoidance is designed,which yield multi-robot systems to arrive the welding targets quickly,achieve the simultaneous welding for a weld line and avoid collision among *** results are provided to demonstrate the effectiveness of the proposed control algorithm.

关键词： Deep multi-agent reinforcement learning MADDPG Coordinated welding

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QMIX Algorithm for Coordinated Welding of Multiple Robots

QMIX Algorithm for Coordinated Welding of Multiple Robots

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作者： Liangchuang Liao Yang-Yang Chen School of Electronic Science & Engineering Southeast University Robot Department Jiangsu Automation Research Institute School of Automation Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University

This paper considers the coordinated welding control based on deep multi-agent reinforcement *** discrete-time states and actions with local observation for the welding robots and the characteristics of the weld lines(e.g.,the starting points of the weld lines and the ending points of the weld lines) are defined,which is suitable to use the monotonic value function factorisation for deep multi-agent reinforcement learning(QMIX) algorithm.A novel reward composed of trajectory optimization,collision avoidance and the task done is designed,which is proved by the simulation in the grid-world environment.

关键词： Deep multi-agent reinforcement learning Welding robots QMIX

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Adaptive Multi-dimensional Taylor Network control for Cement Calciner Outlet Temperature

Adaptive Multi-dimensional Taylor Network Control for Cement...

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IEEE Information Technology, Networking, Electronic and Automation control Conference

作者： Mao-Qi Zhang Hong-Sen Yan Lin-Wei Yu Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China

ISBN: (数字)9781728143903

ISBN: (纸本)9781728143910

This paper focuses on the problem of adaptive multi-dimensional Taylor network control (adaptive MTN) for cement calciner outlet temperature. Firstly, we establish a mathematical model for the cement calciner system, and the model structure is designed as a multi-input single-output data-driven form with time-delay. Secondly, the cross-correlation method and the recursive least squares method are used to identify the model parameters. Thirdly, based on gradient descent method, MTN controller is designed to implement the online update of network weights. Finally, experiment results are given to illustrate the effectiveness of the proposed control strategy.

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Wind Speed Data Repairing Method Based on Bidirectional Prediction

Wind Speed Data Repairing Method Based on Bidirectional Pred...

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Power Electronics, Computer Applications (ICPECA), IEEE International Conference on

作者： Xincheng Shen Yi Qu Shaoxiong Huang Zhi Li Kaifeng Zhang Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing China State Grid Anhui Electric Power Co. Ltd Hefei China

ISBN: (数字)9781728190044

ISBN: (纸本)9781728190051

In order to repair the lost data in distributed wind power system, this paper puts forward a wind speed data repairing model based on a new bidirectional prediction method. This model consists of two one-way prediction models. In each prediction model, the original wind speed data are decomposed into several intrinsic mode functions (IMFs) and a residue signal by ensemble empirical mode decomposition (EEMD) method. Then the Savitzky-Golay (SG) filter is used to reduce noise for high-frequency IMFs. Next the long short-term memory (LSTM) model and autoregressive integrated moving average (ARIMA) model are combined to predict low-frequency IMFs and the noise reduction results respectively. At the end, all those forecast results are added and form a one-way result. By weighted average of two one -way results, the repairing result is calculated. The experimental results from multiple prediction cases show that this method can get more accurate results.

关键词： Wind speed Distributed databases Predictive models Maintenance engineering Wind power generation Data models Wind forecasting

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