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ieee Transactions on Industrial Cyber-Physical systems 2025年 3卷 341-350页

作者： Ren, Hongru Long, Yinren Li, Hongyi Huang, Tingwen Guangdong University of Technology School of Automation Guangdong-Hong Kong Joint Laboratory for Intelligent Decision and Cooperative Control Guangdong Provincial Key Laboratory for Intelligent Decision and Cooperative Control Guangzhou510006 China Southwest University College of Electronic and Information Engineering Chongqing400715 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518055 China

This paper investigates the group formation control problem for cyber-physical systems (CPSs) with random communication constraints. The distributed cloud computing system is constructed to divide agents into groups and establish communication between agents. A data-driven predictive control strategy is proposed by combining networked predictive control and model-free adaptive control method. The desired group formation control performance can be achieved and the three-channel random communication constraints of CPSs are actively compensated. Thisstrategy does not require the system model and relies solely on the system's I/O data for adaptive learning. Further analyses concludes the conditions for simultaneous reach stability and group formation of the closed-loop CPSs using the data-driven predictive control strategy. The effectiveness of the proposed strategy is validated by simulation results. © 2023 ieee.

关键词： Distributed cloud

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Encoding-Decoding Mechanism-Based Model-Free Adaptive Iterative learning control with data Quantization and data Dropouts 13

Encoding-Decoding Mechanism-Based Model-Free Adaptive Iterat...

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13th ieee data driven control and learning systems conference, DDCLS 2024

作者： Shao, Zhen Kong, Fanrong Duan, Zhaoxia Liu, Zixu College of Artificial Intelligence and Automation Hohai University Changzhou213200 China College of Energy and Electrical Engineering Hohai University Nanjing211100 China

ISBN: (纸本)9798350361674

This paper focuses on tracking control for a specific class of discrete-time single-input single-output (SISO) nonlinear repetitive systems that encounter data quantization and dropouts during transmission. The output of the system requires quantization before transmission to the controller, and dropouts may occur. To address this issue, we introduce the encoding -decoding model-free adaptive iterative learning control (E-DMFAILC) algorithm. This algorithm leverages an encoding-decoding mechanism and intermittent update principle to manage data quantization and dropouts, reducing the communication load while preserving accurate tracking performance. Our research results show that the proposed algorithm achieves zero convergence of tracking error by using only a small amount of input and output (I/O) data. Finally, we verify the effectiveness of the algorithm through simulation experiments, which further confirm the superiority of E-DMFAILC. © 2024 ieee.

关键词： Encoding (symbols)

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Coprime Factorization-Based Encryption and Attack Detection for Nonlinear Cyber-Physical systems Using Deep learning Approach

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ieee TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 14020-14029页

作者： Wu, Shimeng Luo, Hao Zhang, Jiusi Qiao, Xinyu Tian, Jilun Jiang, Yuchen Harbin Inst Technol Simulat Ctr Dept Control Harbin 150001 Peoples R China Natl Key Lab Complex Syst Control & Intelligent Ag Harbin 150001 Peoples R China Univ Elect Sci & Technol China Sch Automat Engn Chengdu 611731 Peoples R China

This paper presents a data-driven framework for integrating encryption transmission and attack detection in cyber-physical systems (CPS) with nonlinear physical plants. The main focus of this research is to use deep neural networks to realize the coprime factorization (CF) of nonlinear systems. The definition of the CF guides the network training and designing process, and the model's topology is designed in the state-space form, which improves the interpretability of the data-driven CF. Based on the CF-aided neural networks, an encrypted transmission module is designed that projects information related to system dynamics into a perpendicular data space, which complements existing encryption methods from a control theory perspective. Subsequently, an anomaly detector are designed using the same CF pairs. This detector not only provides high-accuracy detection of attacks but also distinguishes between attacks and faults, thereby reducing the false positive rate and enhancing the reliability of the attack detection. The proposed method has been validated in a real CPS using a mecanum-wheeled vehicle as the physical plant, demonstrating its effectiveness and applicability.

关键词： Encryption System dynamics Nonlinear systems Protocols Vectors Training Monitoring Mathematical models Manifolds Linear systems Coprime factorization encrypted transmission attack detection fault detection deep neural networks

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Physics-Informed LSTM Network-Based Nonlinear Model Predictive control 12

Physics-Informed LSTM Network-Based Nonlinear Model Predicti...

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ieee 12th data driven control and learning systems conference (DDCLS)

作者： Chen, Yujing Qui, Qilin Zhang, Hong Wang, Yanwei Zheng, Ying Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Belt & Rd Joint Lab Measurement & Control Technol Wuhan 430074 Peoples R China Wuhan Inst Technol Sch Mech Elect Engn 693 Chuxiong Rd Wuhan 430073 Peoples R China

ISBN: (纸本)9798350321050

To address the problems of poor physical interpretability and huge sample size requirement when using neural networks to fit nonlinear control system models for state prediction, this paper proposes a model predictive control algorithm based on a physics-informed long short-term memory(LSTM) network. Firstly, the neural network incorporating physical information is extended to model the ordinary differential equations with variable initial states and external control quantities, which makes the network adaptable to the control task and makes the training model physically interpretable. Secondly, a network structure with a mixture of fully connected layers and LSTM layers is built by using the good learning ability of LSTM for time-series data, and the loss function is designed according to the system characteristics and prediction requirements. The trained neural network model is then used as an internal prediction model to construct a nonlinear model predictive control algorithm. Finally, taking the continuous stirring reactor system as an example, the method is verified to be able to fit the system model highly and reduce the time to reach the steady state with a small number of samples.

关键词： nonlinear model predictive control physics-informed neural networks long short-term memory

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Iterative control Decoupling Tuning by Feedforward Compensation for Precision Motion Stage 12

Iterative Control Decoupling Tuning by Feedforward Compensat...

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ieee 12th data driven control and learning systems conference (DDCLS)

作者： Zhao, Hongyang Li, Li Liu, Yang Harbin Inst Technol Key Lab Ultraprecis Intelligent Instrumentat Minist Ind & Informat Technol Harbin 150001 Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China

ISBN: (纸本)9798350321050

In multi-input multi-output (MIMO) ultra-precision motion control system, high-performance decoupling method is the key to reduce the interactions between different degree of freedoms (DOFs). However, the manufacturing and assembling errors will greatly affect the decoupling performance. Some non-negligible factors, such as the center of gravity (CoG) and actuator positions, cannot be accurately determined, which will deteriorate the control accuracy. To tackle this problem, an iterative control decoupling tuning method by feedforward compensation is proposed in this paper. The proposed strategy using feedback signal to tune the parameters of feedforward compensator iteratively and further compensate for the closed-loop dynamics. The simulation results show that the proposed method is more effective, including the convergence accuracy, convergence speed and the improvement of the tracking error caused by the coupling.

关键词： Multiple-input multiple-output (MIMO) control system crosstalk control decoupling iterative feedforward tuning precision motion stage

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Hexapod Robot Gait Switching Based on DifferentWild Terrains 12

Hexapod Robot Gait Switching Based on DifferentWild Terrains

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ieee 12th data driven control and learning systems conference (DDCLS)

作者： Fei, Shijie Chen, Yiyang Tao, Hongfeng Chen, Hongtian Soochow Univ Sch Mech & Elect Engn Suzhou 215137 Peoples R China Jiangnan Univ Key Lab Adv Proc Control Light Ind Minist Educ Wuxi 214122 Jiangsu Peoples R China Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China

ISBN: (纸本)9798350321050

Hexapod robots have become an indispensable part of legged robots due to their excellent stability and traversability, and have recently become a hot research topic. In complex outdoor environments, the ability of the hexapod robot to switch its motion strategy according to the terrain is crucial for improving its mobility. This paper presents a terrain recognition network that enables our hexapod robot to switch its forward gait based on the different types of outdoor terrain. This method provides better stability for the hexapod robot than traditional single-gait walking through different terrains.

关键词： Hexapod Robot Gait switching Terrain recognition

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Switching System-Based Event-Triggered Sampled-data control for Multi-Area Power systems under DoS Attacks: A data-driven Method 13

Switching System-Based Event-Triggered Sampled-Data Control ...

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13th ieee data driven control and learning systems conference, DDCLS 2024

作者： Hou, Rui Jia, Li Li, Jianfang Zhu, Bo School of Mechatronic Engineering and Automation Shanghai University Shanghai200444 China

ISBN: (纸本)9798350361674

The reliance of modern power systems on open communication networks for load frequency control (LFC) increases vulnerability to cyber-attacks, particularly denial-of-service (DoS) attacks. This study explores a novel switching system-based resilient event-triggered sampled-data control for LFC using a data-driven method. Firstly, a linear data-dependent model for power systems is presented addressing unknown nonlinearities, treating sampling period as a vital design parameter. Further, the LFC system is modeled as multiple switching subsystems under DoS attacks, and then a new adaptive sampled-data controller is proposed to accomplish frequency regulation while withstanding the attacks. Additionally, an event-triggered scheduling mechanism (ETSM) is considered to optimize bandwidth resources for data transmission during sampling. To assess the proposed LFC scheme, a new stability criterion and analysis method, employing the average dwell time (ADT) technique and Lyapunov stability theory, is introduced. Finally, the scheme's effectiveness is demonstrated through evaluation on a three-area power system. © 2024 ieee.

关键词： Stability criteria

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A Systematic Review of data-driven Methods in Emergency control of Power systems 34

A Systematic Review of Data-driven Methods in Emergency Cont...

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34th Australasian Universities Power Engineering conference

作者： Khan, Abdul Basit Zhang, Rui Zhang, Yuchen Dong, Zhao Yang Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW Australia Queensland Univ Technol Sch Elect Engn & Robot Brisbane Qld Australia Nanyang Technol Univ Sch Elect & Elect Engn Jurong West Singapore

ISBN: (纸本)9798350377958;9798350377941

Emergency control in power systems is crucial for maintaining stability and preventing widespread outages. data-driven methods have emerged as vital tools in optimizing response-driven and event-driven emergency controls, particularly in Under-Voltage Load Shedding (UVLS), Under-Frequency Load Shedding (UFLS), and event-driven load shedding (ELS). This systematic review aims to synthesize the current state of research on data driven methods in the emergency control of power systems, focusing on UVLS, UFLS, and ELS. A systematic review was conducted using the PRISMA framework, examining research papers published between 2014 and 2024. databases such as ieee Xplore and ScienceDirect were searched using relevant keywords. Inclusion and exclusion criteria were applied to ensure the relevance and quality of selected studies. The review identified various data-driven techniques, including optimization algorithms, machine learning approaches, probabilistic methods, and real-time data analytics and control, applied in both response driven and event-driven emergency controls. Each cluster is analyzed for its application, advantages, disadvantages, and complexity. This review highlights the advancements in these methodologies and suggests future research directions to enhance power system stability during emergencies.

关键词： Emergency control Event-driven control response-driven control UFLS UVLS ELS Power system stability

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A data-driven Physical Mechanism Modeling Method for the Spin-Exchange Relaxation-Free Comagnetometer 12

A Data-driven Physical Mechanism Modeling Method for the Spi...

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ieee 12th data driven control and learning systems conference (DDCLS)

作者： Li, Feng Wang, Zhuo Zhang, Min Wang, Ruigang Qin, Bodong Chai, Yanchao Beihang Univ Sch Instrumentat Sci & Optoelect Engn Beijing 100191 Peoples R China Beihang Univ Zhejiang Prov Key Lab Ultra Weak Magnet Field Spa Hangzhou Innovat Inst Hangzhou 310051 Peoples R China

ISBN: (纸本)9798350321050

The Spin-Exchange Relaxation-Free Comagnetometer (SERFCM) is a new quantum instrument with ultra-high accuracy. Normally, the atomic ensembles of SERFCM operate in an open-loop state, which is not conducive to long-term highprecision measurements. In order to realize closed-loop control of its atomic polarization state, it is necessary to model and analyze the dynamic characteristics of the SERFCM system. In this paper, a data-driven physical mechanism (DDPM) modeling method is proposed to realize the modeling of the SERFCM, a multi-input multi-output system. First, the state space equations of the SERFCM are established based on the Bloch equation, which are transformed into a discrete transfer function matrix. Then, based on the criterion of least variance in estimation, we realize the modeling of the discrete transfer function matrix using the excitation input data, the measured output data, and the estimated output data. Finally, the simulation results of modeling under different longitudinal magnetic fields confirm the validity of the proposed method. This work enables the online modeling of SERFCM system and facilitates the analysis of the effects of various parameters on the dynamic characteristics.

关键词： Spin-Exchange Relaxation-Free Comagnetometer data-driven Physical Mechanism Modeling

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Discrete-Time Nonlinear System Identification: A Fixed-Time Concurrent learning Approach

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ieee TRANSACTIONS ON systems MAN CYBERNETICS-systems 2025年第3期55卷 1591-1599页

作者： Tatari, Farzaneh Niknejad, Nariman Modares, Hamidreza Drive Syst Design Inc Control Syst Farmington Hills MI 48335 USA Michigan State Univ Dept Mech Engn E Lansing MI 48824 USA

This article develops a fixed-time identifier for modeling unknown discrete-time nonlinear systems without requiring the standard persistence of excitation (PE) condition. A data-driven update law based on a modified gradient descent (GD) update law is presented to learn the system parameters, which relies on the concurrent learning approach under which the recorded past data and the current data are employed concurrently. Fixed-time convergence guarantees are provided for the modified GD update law under the condition that the recorded data fulfills a rank condition, which is less restrictive than the standard PE condition. To guarantee fixed-time convergence, fixed-time Lyapunov analysis is leveraged. Compared to typical GD-based update laws, two main advantages of the presented approach are: 1) the modified GD update law guarantees fixed-time convergence instead of asymptotic convergence and 2) the convergence guarantee is provided under an easy-to-check rank condition rather than the standard PE condition, which is hard or even impossible to check online. Simulation results are provided to verify the obtained results.

关键词： Convergence Discrete-time systems Standards Real-time systems Nonlinear systems Estimation error Vectors Upper bound Uncertainty Parameter estimation Fixed-time identification nonlinear discrete-time systems unknown dynamics

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