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Dynamic event-triggered data-driven iterative learning bipartite tracking control for nonlinear MASs with prescribed performance

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Science China(Information Sciences) 2025年第1期68卷 292-305页

作者： Tao SHI Wei-Wei CHE School of Mechatronic Engineering and Automation Shanghai University State Key Laboratory of Synthetical Automation for Process Industries Northeastern University College of Information Science and Engineering Northeastern University

This article proposes a distributed dynamic event-triggered data-driven iterative learning control(DET-DDILC)scheme under a predefined performance to tackle the bipartite tracking control problem for multiagent systems(MASs). An improved dynamic linearization technique is utilized to convert the nonlinear MASs into an iterative linear data model. First,a peer-to-peer mapping function is introduced to map the constrained distributed system output homeomorphism to an unconstrained one. In addition, a DET mechanism based on a time-iteration-varying function is devised to conserve network communication resources. Based on the unconstrained transformation and the designed DET mechanism, the DET-DDILC algorithm is devised to ensure that the bipartite tracking performance of MASs can be within the preset range. Finally, the effectiveness and feasibility of the designed control scheme are demonstrated via a simulation case by a comparison.

关键词： bipartite tracking control prescribed performance event-triggered iterative learning control multiagent systems

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Smart fiber-optic masks: real-time respiratory monitoring and health analysis

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Science China(Information Sciences) 2025年第4期68卷 216-227页

作者： Qi YU Ya-Nan ZHANG Jiaqi ZHANG Shuang YANG Lingxiao JIANG Xuegang LI College of Information Science and Engineering Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology Affiliated Hospital of North Sichuan Medical College

In this study, we design a smart mask based on an ultrathin retractable flexible fiber optic sensor with high sensitivity(0.56 V/m-1), ultra-light mass（0.24 g）, and excellent tensile properties. The sensor was integrated with a medical mask to recognize multiple breathing patterns（standard, fast, slow, shallow, deep, breath-holding）, coughing, and speaking,as well as to study gender characteristics and physiological differences in the respiratory system. By comparing the nasal-oral respiratory waveforms and frequencies, guidance for respiratory correction was provided. In addition, affixing flexible fiber optic sensors to the outside of the mask reduces the risk of cross-infection and significantly reduces temperature and humidity disturbances. Studies have shown that nine sensor locations placed on the outside of the mask responded significantly to respiratory waveforms. For personalized health management, the system is also equipped with a communication module,web and mobile apps to support data downloads, real-time monitoring, and exception alerts.

关键词： smart mask respiratory monitoring fiber optic sensor health management anti-interference

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Stealthy Attacks with Historical Data on Distributed State Estimation

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IEEE Transactions on Information Forensics and Security 2025年 20卷 4541-4550页

作者： Xing, Jitao Ye, Dan Li, Pengyu Northeastern University College of Information Science and Engineering Shenyang110819 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China

This paper addresses the problem of designing stealthy attacks on distributed estimation using historical data. The distributed sensors transmit innovations to remote state estimators and neighboring nodes, which attackers can intercept and tamper with. To bypass the configured false data detectors, the attack parameters must satisfy the stealthiness constraints. The determination of the optimal stealthy attack strategy is reformulated as a series of convex optimization problems. Additionally, a lower bound on the compromised estimation error covariance is derived, and analytical solutions for the suboptimal stealthy attack strategy that maximizes the bound are provided. These solutions are proven to be piecewise constant with smaller computational complexity. Finally, numerical simulations validate the theoretical results. © 2005-2012 IEEE.

关键词： Convex optimization

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Optimal Control for Fractional Order Nonlinear Systems Based on Adaptive Dynamic Programming

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3086-3097页

作者： Yan, Yuqing Zhang, Huaguang Sun, Jiayue Yu, Shuhang Northeastern University College of Information Science and Engineering Shenyang110004 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries College of Information Science and Engineering Shenyang110004 China

In this article, an adaptive dynamic programming (ADP)-based optimal control strategy for a series of fractional-order nonlinear systems (FONS) with unknown control directions is investigated. To eliminate the challenges posed by unknown control directions, fractional-order Nussbaum-type functions are introduced for FONS, expanding the range of possible applications. Additionally, since system performance is compromised by disturbances, a fractional-order disturbance observer is designed to counteract the effects of external disturbances and enhance system robustness. Furthermore, differential geometric methods are employed to investigate FONS, constructing appropriate diffeomorphism that provide equivalent systems for decoupled linearization. Then, an optimal control method is studied for a class of strictly feedback FONS, in which Nussbaum-type functions are combined with ADP theory during the backstepping design process. Finally, based on fractional Lyapunov stability theory and backstepping method, it is guaranteed that all signals of the closed-loop FONS are uniformly ultimately bounded (UUB). Numerical simulation and a PMSM model are utilized to verify the effectiveness of the presented method. © 2013 IEEE.

关键词： Dynamic programming

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Performance-Optimize Adaptive Robust Tracking Control for USV-UAV Heterogeneous Systems With Uncertainty

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 7251-7262页

作者： Chen, Guangjun Wang, Wencheng Dong, Jiuxiang Northeastern University College of Information Science and Engineering Liaoning Province Key Laboratory of Safe Operation Technique for Autonomous Unmanned Systems State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Weifang University College of Machinery and Automation Weifang261061 China

This paper investigates the performance-optimized tracking control problem of the heterogeneous system consisting of the unmanned surface vehicle (USV) and the unmanned aerial vehicle (UAV) under uncertain dynamics. Based on the target tracking strategy, a performance optimization control framework for cooperative tracking in sea-air 3D space is established. To avoid the singularity of the UAV in the position loop inverse solution of the desired attitude, the saturation control strategy is designed to solve the UAV guidance problem, and the UAV position loop obtains bounded thrust. For the kinematic layer of the USV-UAV system, based on the prescribed performance control and optimal control methods, a prescribed performance-optimized control strategy is designed to optimize the virtual control input while ensuring the prescribed control performance. In addition, to reduce the impact of uncertainty on the USV-UAV control system, a prescribed performance compensation control scheme is proposed in the kinetic loop, which improves the USV-UAV control system robustness. The established sea-air 3D cooperative target tracking control framework can also be extended to complex formation control. Finally, experimental simulations verify the effectiveness of the designed control scheme. © 1967-2012 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Decentralized Federated Learning Algorithm Under Adversary Eavesdropping

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IEEE/CAA Journal of Automatica Sinica 2025年第2期12卷 448-456页

作者： Lei Xu Danya Xu Xinlei Yi Chao Deng Tianyou Chai Tao Yang the State Key Laboratory of Synthetical Automation for Process Industries Northeastern University the Department of Mechanical Engineering University of Victoria Shanghai Institute of Intelligent Science and Technology Tongji University IEEE the Institute of Advanced Technology Nanjing University of Posts and Telecommunications

In this paper, we study the decentralized federated learning problem, which involves the collaborative training of a global model among multiple devices while ensuring data *** classical federated learning, the communication channel between the devices poses a potential risk of compromising private information. To reduce the risk of adversary eavesdropping in the communication channel, we propose TRADE(transmit difference weight) concept. This concept replaces the decentralized federated learning algorithm's transmitted weight parameters with differential weight parameters, enhancing the privacy data against eavesdropping. Subsequently, by integrating the TRADE concept with the primal-dual stochastic gradient descent(SGD)algorithm, we propose a decentralized TRADE primal-dual SGD algorithm. We demonstrate that our proposed algorithm's convergence properties are the same as those of the primal-dual SGD algorithm while providing enhanced privacy protection. We validate the algorithm's performance on fault diagnosis task using the Case Western Reserve University dataset, and image classification tasks using the CIFAR-10 and CIFAR-100 datasets,revealing model accuracy comparable to centralized federated learning. Additionally, the experiments confirm the algorithm's privacy protection capability.

关键词： Adversary eavesdropping decentralized federated learning privacy protection stochastic gradient descent

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Adaptive Critic-Based Optimal Control of Input-Constrained Stochastic Systems via Generalized Fuzzy Hyperbolic Models

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IEEE Transactions on Fuzzy Systems 2025年第5期33卷 1627-1638页

作者： Zhang, Lulu Zhang, Huaguang Sun, Jiayue Ming, Zhongyang Northeastern University College of Information Science and Engineering Liaoning Shenyang110004 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries School of Information Science and Engineering Liaoning Shenyang110004 China

This article investigates adaptive dynamic programming (ADP)-based optimal control issue of nonlinear stochastic systems with asymmetric input constraints. The solution starts with developing generalized fuzzy hyperbolic model (GFHM) in the stochastic system, which aims to approximate unknown nonlinear terms. By establishing a nonquadratic cost function, the constrained H∞ control problem is converted into zero-sum game and Hamilton–Jacobi–Isaacs equation (HJIE) is derived. To solve the HJIE, the ADP algorithm is developed by constructing a single-network adaptive critic framework. Assisted by GFHM, the updating process obviates the necessity for the dynamics of unknown nonlinear terms. Under the designed controller, the stability of the stochastic system is guaranteed by the Lyapunov method. Two illustrative examples validate the presented method. © 1993-2012 IEEE.

关键词： Cost functions

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Consensus Control Strategy for the Treatment of Tumour With Neuroadaptive Cellular Immunotherapy

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 575-584页

作者： Jiayue Sun Dongni Li Huaguang Zhang Lu Liu Wenyue Zhao IEEE the State Key Laboratory of Synthetical Automation for Process Industries the College of Information Science and Engineering Northeastern University the Department of Breast Surgery The First Hospital of China Medical University the Department of Thoracic Surgery The First Hospital of China Medical University

This paper presents a novel neuro-adaptive cellular immunotherapy control strategy that leverages the high efficiency and applicability of chimeric antigen receptor-engineered T(CAR-T) cells in treating cancer. The proposed real-time control strategy aims to maximize tumor regression while ensuring the safety of the treatment. A dynamic growth model of cancer cells under the influence of cellular immunotherapy is established for the first time, which aligns with clinical experimental *** the backstepping method, a novel consensus reference model is designed to consider the characteristics of cancer cell changes during the treatment process and conform to clinical rules. The model is segmented and continuous, with cancer cells expected to decrease in a step-like manner. Furthermore, a prescribed performance mechanism is constructed to maintain the therapeutic effect of the proposed scheme while ensuring the transient performance of the system. Through the analysis of Lyapunov stability, all signals within the closed-loop system are proven to be semiglobally uniformly ultimately bounded(SGUUB). Simulation results demonstrate the effectiveness of the proposed control strategy, highlighting its potential for clinical application in cancer treatment.

关键词： Cellular immunotherapy therapy chimeric antigen receptor-engineered T (CAR-T) cells neuro-adaptive control novel consensus reference model prescribed performance mechanism

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Robust Predictive Bipartite Bumpless Control for Markov Jump MASs Under Switching Topology: Application to Rotary-Wing Air Vehicles

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 7020-7032页

作者： Wang, Jian Dong, Jiuxiang The Liaoning Province Key Laboratory of Safe Operation Technique for Autonomous Unmanned Systems College of Information Science and Engineering Shenyang110819 China Northeastern University State Key Laboratory of Synthetical Automation of Process Industries Shenyang110819 China

This article addresses the issue of bipartite consensus for Markov jump multi-agent systems subjected to bounded disturbance and input constraints under mode-dependent switching topology. To surmount the challenge of handling disturbance, hard constraints and bump suppression, which are not considered and difficult to deal with in existing literature on solutions to algebraic Riccati equation, a novel bipartite robust predictive bumpless control algorithm is proposed with integrating H∞ performance. To be specific, the offline designing process of bipartite controller parameters is firstly performed by investigating terminal constraint set and robust invariant set. In this sense, H∞ type cost function and consensus error state bump cost are constructed for achieving disturbance attenuation and suppressing bump in error state, respectively. This further avoids possible counterproductive results caused by setting constraints on bump with a small gain. Next, based on the optimization prediction dynamics, perturbation state of controller is optimized online for each agent, which efficiently reduces the computational burden compared with conventional online and efficient model predictive control. Furthermore, the recursive feasibility of the proposed method and stochastic stability of error dynamics are theoretically guaranteed. Finally, an application to rotary-wing air vehicles is utilized to verify the potential of theoretic results. © 1967-2012 IEEE.

关键词： Cost functions

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Dual Perspective Secure Analysis for Local Estimate-Based FDI Attacks in Networked Systems

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3312-3325页

作者： Qu, Fuyi Liu, Hao Tan, Cheng Li, Yuzhe Shenyang Ligong University School of Automation and Electrical Engineering Shenyang110159 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Wuhan Institute of Technology School of Electrical and Information Engineering Wuhan430205 China Qufu Normal University School of Engineering Rizhao276800 China

This article discusses the security concerns related to networked systems, where the sensor sends the local estimate to the remote estimator, which may be attacked. Traditionally, in the remote state estimation with the innovation or raw measurement case, denial of service (DoS) and false data injection (FDI) attacks are investigated thoroughly. Notably, for remote state estimation with local estimate cases considered in this article, most existing works consider DoS attacks but not FDI attacks, negatively affecting remote state estimation performance. Furthermore, current detection mechanisms encounter challenges when identifying such attacks due to the unavailable innovation or raw measurement. As such, we study FDI attacks under this framework and provide the corresponding secure analysis using a dual-perspective approach. Specifically, we propose a detector to detect such attacks using the prior information extracted from the remote estimator. Then, we analyze the existence of stealthy attacks and characterize the corresponding performance evaluation for the remote estimation under such attacks. Following this, we construct the optimal attack scheme, maximizing the expected average and terminal estimation error covariances, respectively. To reduce the above vulnerability, we develop a co-design transmission strategy and offer an analytical detection performance evaluation under different attack scenarios. Finally, simulations are provided to illustrate the proposed results. © 2025 IEEE.

关键词： Detectors State estimation Technological innovation Security Current measurement Communication system security Wireless networks Kalman filters Hands Estimation error

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