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learning-Based Modeling and Predictive control for Unknown Nonlinear System With Stability Guarantees

ieee TRANSACTIONS ON NEURAL NETWORKS AND learning systems 2025年 PP卷 PP页

作者： Jin, Ao Zhang, Fan Shen, Ganghui Huang, Bingxiao Huang, Panfeng Shaanxi Prov Innovat Team Intelligent Robot Techno Xian 710072 Peoples R China Northwestern Polytech Univ Sch Automat Res Ctr Intelligent Robot Xian 710072 Peoples R China Northwestern Polytech Univ Sch Astronaut Res Ctr Intelligent Robot Xian 710072 Peoples R China

This work focuses on the safety of learning-based control for unknown nonlinear system, considering the stability of learned dynamics and modeling mismatch between the learned dynamics and the true one. A learning-based scheme imposing the stability constraint is proposed in this work for modeling and stable control of unknown nonlinear system. Specifically, a linear representation of unknown nonlinear dynamics is established using the Koopman theory. Then, a deep learning approach is utilized to approximate embedding functions of Koopman operator for unknown system. For the safe manipulation of proposed scheme in the real-world applications, a stable constraint of learned dynamics and Lipschitz constraint of embedding functions are considered for learning a stable model for prediction and control. Moreover, a robust predictive control scheme is adopted to eliminate the effect of modeling mismatch between the learned dynamics and the true one, such that the stabilization of unknown nonlinear system is achieved. Finally, the effectiveness of proposed scheme is demonstrated on the tethered space robot (TSR) with unknown nonlinear dynamics.

关键词： data-driven control learning-based control model predictive control nonlinear system

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Composite learning Fixed-Time control for Nonlinear Servo systems With State Constraints and Unknown Dynamics

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ieee TRANSACTIONS ON systems MAN CYBERNETICS-systems 2025年第3期55卷 2332-2342页

作者： Wang, Shubo Sun, Chuanbin Chen, Qiang He, Haoran Kunming Univ Sci & Technol Fac Mech & Elect Engn Kunming 650550 Peoples R China Kunming Univ Sci & Technol Yunnan Key Lab Intelligent Control & Applicat Kunming 650550 Peoples R China Qingdao Univ Sch Automat Qingdao 266071 Peoples R China Zhejiang Univ Technol Coll Informat Engn Data Driven Intelligent Syst Lab Hangzhou 310023 Peoples R China

Robot systems, due to their unique flexibility and economy, are widely used in modern industry and intelligent manufacturing. The parameters of the system are unknown, and traditional parameter estimation methods are difficult to achieve fixed time convergence, which leads to extremely position tracking control problem. In addition, the transient and steady-state performance of the robot system is difficult to specify in advance. In this article, a novel composite learning fixed-time (FxT) control strategy is proposed for the robotic systems to deal with these issues. The funnel control (FC) is utilized to transform the original error system into a new error dynamics with transient performance constraints. The two-phase nonsingular FxT sliding mode surface is constructed to avoid the singularity problem. Then, the filter operation is introduced to obtain the expression of parameter estimation error and is used to design the composite learning law. To achieve parameter estimation, a FxT composite learning law based on online historical data and regression extension is proposed, where the interval excitation (IE) is considered in the adaptive law. Finally, the designed adaption is incorporated into the nonsingular FxT sliding mode control to achieve tracking control. Moreover, the comparison of three different controllers is made to demonstrate the benefits of the developed control strategy.

关键词： Robots Convergence Parameter estimation Transient analysis Symmetric matrices Servomotors Vectors Electronic mail Adaptive control Trajectory tracking fixed-time (FxT) convergence funnel control (FC) parameter estimation sliding mode control (SMC)

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A supervised contrastive learning method with novel data augmentation for transient stability assessment considering sample imbalance

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2025年 256卷

作者： Huang, Yaodi Song, Yunpeng Cai, Zhongmin Xi An Jiao Tong Univ Sch Automat Sci & Engn MOE KLINNS Lab Xian 710054 Peoples R China

Fast and accurate transient stability assessment (TSA) is crucial for power system safety, as it provides the basis for operators to decide on emergency control actions. However, data-driven TSA methods are restricted by sample imbalance in real-world systems, characterized by a predominance of stable over unstable samples. Consequently, data-driven TSA methods may bias assessment rules towards classifying unstable as stable, potentially causing significant economic losses. To address this issue, we propose balanced supervised contrastive learning (BSCL) with a novel data augmentation method. First, the postfault transient response observations are utilized as augmented samples to enable the model to learn postfault features. Subsequently, a specially designed balanced supervised contrastive loss is proposed. In the contrastive learning process, the augmented samples belonging to the same class are pulled together in the embedding space, while simultaneously pushing apart samples from different classes. By doing so, the proposed BSCL method can learn future information and separate different classes. After contrastive learning, a classifier is connected to the trained model and undergoes further training for TSA, enabling the model to predict transient stability. Case studies conducted on an ieee 39-bus system and an actual power system demonstrate that the proposed BSCL method achieves high identification accuracy in imbalanced datasets and possesses strong generalization capabilities and robustness. Furthermore, the results indicate that the proposed data augmentation method outperforms other data augmentation methods utilized in BSCL.

关键词： Power system Sample imbalance Supervised contrastive learning Transient stability assessment

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data-driven Group Formation control of Cyber-Physical systems via Distributed Cloud Computing

IEEE Transactions on Industrial Cyber-Physical Systems

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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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Distributed Meta-Deep Reinforcement learning for Wide Area Frequency control of Interconnected Grid Considering Multi-Time Scale Coordination

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ieee TRANSACTIONS ON CIRCUITS AND systems I-REGULAR PAPERS 2025年

作者： Li, Jiawen Dai, Jichao Shanghai Univ Elect Power Shanghai 201306 Peoples R China Shanghai Jiao Tong Univ Shanghai 200240 Peoples R China

To solve the problems of over-shedding for loads in conventional frequency emergency control and prevent constraint violations in power systems, a data-driven wide-area frequency control strategy (WA-FCS) is proposed. The WA-FCS includes an emergency frequency control (EFC) module and a wide automatic generation control (WAGC) module. In online decisions, the EFC can consider the impact of WAGC on the system frequency and prevent system constraint violations. WAGC helps EFC quickly solve the problem of line power overload. By coordinating EFC with WAGC, the load loss following grid failure can be reduced. In addition, to improve the strategy's robustness, a distributed meta-actor critic (DMAC) algorithm is proposed, which draws on meta-learning and distributed exploration to obtain the optimal WA-FCS strategy in a random environment. The simulation results of this paper verify the effectiveness and feasibility of the proposed strategy for the performance-based frequency regulation market.

关键词： Frequency control Frequency measurement Automatic generation control Accuracy Reliability Regulation Real-time systems Accidents Time-frequency analysis Power grids emergency frequency control performance-based frequency regulation market automatic generation control distributed meta-actor critic

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Novel data-driven Models for Detecting and Mitigating False data Injection Attacks in Automatic Generation control Considering Nonlinearities

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ieee TRANSACTIONS ON INDUSTRY APPLICATIONS 2025年第2期61卷 2731-2745页

作者： Abughali, Ahmed Muhammed, Abdullahi Oboh Al-Sumaiti, Ameena Saad El Moursi, Mohamed Shawky Khalifa Univ Adv Power & Energy Ctr Dept Elect Engn Smart Operat Res OR Lab Abu Dhabi 127788 U Arab Emirates Khalifa Univ Dept Elect Engn Abu Dhabi 127788 U Arab Emirates

Increasing cyber vulnerabilities pose various concerns regarding the stability and reliability of power systems. Conventionally, Automatic Generation control (AGC) is employed to maintain the frequency of power systems within a predefined range. However, it is susceptible to cyber-attacks due to its reliance on data transmitted through communication links. Consequently, designing robust protection mechanism to detect, locate and mitigate such attacks is crucial. This paper proposes three data-driven architectures to detect, locate and mitigate False data Injection (FDI) and Denial of Service (DoS) attacks against AGC systems. First, the proposed models are trained and evaluated using diverse Pulse and Ramp stealthy attacks scenarios in a two-area AGC system, considering the AGC nonlinearities. The detection model exhibits exemplar capability for detecting and locating individual and particularly, multiple coordinated stealthy cyber-attacks, that can significantly undermine the effectiveness of detection systems, with F-1-score of 93.46% and 96.32% AUC score. The second and third models, attacked class-based mitigation model (ACM) and comprehensive mitigation model (CMM), are employed to accurately recover the corrupted measurements, attaining RMSEs of 0.003463 and 0.003218, respectively. Furthermore, this paper is the first to innovatively examine the impact of PV power system injections on the effectiveness of the proposed detection model, which accurately classified 75,100 out of 75,131 no-attack instances, showcasing its proficiency in distinguishing PV injections from cyber-attacks. Finally, the proposed models are further evaluated using three-area AGC system under mixed FDI and DoS attack scenarios. The obtained results demonstrate their capability to handle larger systems while meeting practical operational requirements.

关键词： Automatic generation control Power systems Cyberattack Prevention and mitigation Power measurement Load modeling Power system stability Real-time systems Deep learning Power system reliability AGC cyberattacks deep learning DoS FDI nonlinearities power systems PV integration

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Voltage control for Distribution Networks Based on Large Language Model-Assisted Deep Reinforcement learning

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ieee ACCESS 2025年 13卷 76072-76084页

作者： Yan, Limei Cheng, Chongyang Northeast Petr Univ Sch Elect Informat Engn Daqing 163318 Peoples R China

With the continuous integration of large-scale distributed energy resources into distribution networks, numerous challenges arise regarding security, stability, and economic performance, particularly voltage violations and increased network losses. Furthermore, existing deep reinforcement learning (DRL) methods often rely on extensive real-world operational data for agent training. Yet, the lack of diversity in the collected data can significantly limit the generalization ability of agents under varying operating conditions. This paper proposes a regional voltage optimization control strategy for distribution networks to address these issues based on DRL supported by large language model (LLM). By integrating LLM technologies with DRL, the approach leverages prompt engineering to guide large-language models in generating customized datasets for DRL agent training, enabling data augmentation. This reduces the dependence on real-world data while improving the generalizability of agents. The proposed control strategy was validated on modified ieee 33-bus and 123-bus distribution systems. The experimental results effectively mitigate voltage violations and reduce network losses while exhibiting strong robustness and generalization under various operating conditions.

关键词： Distribution networks Voltage control Optimization Training Training data Renewable energy sources Prompt engineering data models Biological system modeling Knowledge engineering Deep reinforcement learning large language model voltage control multi-agent data augmentation data-driven

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Scenario-Optimization-Based Velocity Planning of Autonomous Vehicles for Interacting With Pedestrians

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ieee TRANSACTIONS ON INTELLIGENT TRANSPORTATION systems 2025年第4期26卷 5382-5395页

作者： Jekl, Bence Dabcevic, Zvonimir Nemeth, Balazs Skugor, Branimir Gaspar, Peter Hungarian Res Network HUN REN Inst Comp Sci & Control SZTAK H-1111 Budapest Hungary Univ Zagreb Fac Mech Engn & Naval Architecture Zagreb 10000 Croatia HUN REN SZTAKI H-1111 Budapest Hungary Budapest Univ Technol & Econ Fac Transportat Engn & Vehicle Engn Dept Control Transportat & Vehicle Syst H-1111 Budapest Hungary

This paper presents a velocity planning method for autonomous vehicles (AVs) to guarantee safe interactions with pedestrians at unsignalized crosswalks and with surrounding vehicles on the AV's route. The method is structured within a hierarchical framework that includes robust control, a learning-based component, and a supervisory element. The learning-based component is trained using reinforcement learning techniques to reduce traveling time, minimize control interventions, and set the priority ratio between the AV and pedestrians. The supervisory element employs scenario optimization, using statistical data on pedestrian motions to ensure collision avoidance. A complex game-theory-based pedestrian model is formulated and analyzed in order to evaluate the effectiveness of the proposed velocity planning method. Extensive simulations are performed using the high-precision traffic simulator software SUMO. These simulations evaluate various aspects of the velocity planner, including computation time, traveling time, control interventions, and parameter settings. The results demonstrate the method's ability to achieve real-time implementation while maintaining safety and performance objectives.

关键词： Pedestrians Planning Computational modeling Safety data models Real-time systems Predictive models Autonomous vehicles Adaptation models Vehicle dynamics pedestrian modeling velocity planning scenario approach data-driven control

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The Input-Mapping-Based Online learning Sliding Mode control Strategy With Low Computational Complexity

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ieee TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 7670-7678页

作者： Yu, Yaru Ma, Aoyun Li, Dewei Xi, Yugeng Gao, Furong Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Key Lab Syst Control & Informat Proc Minist Educ China Shanghai 200240 Peoples R China Hong Kong Univ Sci & Technol Dept Chem & Biol Engn Hong Kong Peoples R China

The data-driven sliding mode control (SMC) method proves to be highly effective in addressing uncertainties and enhancing system performance. In our previous work, we implemented a co-design approach based on an input-mapping data-driven technique, which effectively improves the convergence rate through historical data compensation. However, this approach increases computational complexity in multi-input and multi-output (MIMO) systems due to the dependency of the number of online optimization variables on system dimensions. To improve applicability, this paper introduces a novel input-mapping-based online learning SMC strategy with low computational complexity. First, a new sliding mode surface is established through online convex combination of pre-designed offline surfaces. Then, an input-mapping-based online learning sliding mode control (IML-SMC) strategy is designed, utilizing a reaching law with adaptively adjusted convergence and switching coefficients to minimize chattering. The input-mapping technique employs the mapping relationship between historical input and output data for predicting future system dynamics. Accordingly, an optimization problem is formulated to learn from the past dynamics of the uncertain system online, thereby enhancing system performance. The optimization problem in this paper features fewer variables and is independent of system dimension. Additionally, the stability of the proposed method is theoretically validated, and the advantages are demonstrated through a MIMO system. Note to Practitioners-The design of control strategies that reduce the impact of mismatches between practical systems and models on system performance, while also ensuring applicability, is crucial. To address this issue, this paper proposes a low-complexity IML-SMC strategy. This strategy uses historical real input-output information and the mapping relationship with future dynamics to compensate for the impact of unknown dynamics and improve the system's conv

关键词： Computational complexity Optimization Uncertainty Convergence Sliding mode control System performance Robustness MIMO communication Computational modeling Switches Sliding mode control (SMC) input-mapping technique convex combined sliding mode surface low computational complexity

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Development and Application of Coverage control Algorithms: A Concise Review

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ieee Transactions on Automation Science and Engineering 2025年 22卷 14906-14927页

作者： Cheng, Bin He, Mingyuan Zhu, Zhongpan He, Bin Chen, Jie Tongji University Department of Control Science and Engineering Shanghai201804 China National Key Laboratory of Autonomous Intelligent Unmanned Systems Shanghai201203 China

Coverage control is a foundational domain within multi-agent systems, which has recently undergone substantial advancements. Model-based and optimization-based control methods have achieved new breakthroughs and applications, while data-driven and learning-based algorithms in coverage control have also yielded significant results. This review examines the evolution and application of coverage control algorithms in multi-agent systems. Moreover, this review then focuses on how contemporary algorithms build on traditional control strategies and integrate cutting-edge data-driven and adaptive learning techniques. Finally, it explores potential future directions, emphasizing the importance of interdisciplinary approaches in overcoming existing challenges and seizing new opportunities in coverage control deployment. This review aims to be a valuable resource for researchers and practitioners, guiding continued exploration in this field. Note to Practitioners - This work was motivated by the need to provide an overview of the development and research of existing multi-agent coverage control algorithms. Different application scenarios and constraints require different methods of multi-agent coverage control, each with varying efficiencies. For researchers and practitioners, deciding which coverage control method to use in specific scenarios, while considering practical constraints such as energy limitations, can be challenging. Therefore, this work offers an overview of existing multi-agent coverage control algorithms and suggests potential application scenarios. This review serves as a comprehensive reference for researchers and practitioners in the field, helping them identify suitable solutions to ensure efficient coverage. © 2004-2012 ieee.

关键词： learning algorithms

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