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Consensus of nonlinear multiagent systems on time scales via event-triggered sliding mode control

JOURNAL OF THE FRANKLIN INSTITUTE 2025年第8期362卷

作者： Yang, Chen Tang, Wei Sheng, Yin Zeng, Zhigang Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Key Lab Image Proc & Intelligent Control Educ Mini Wuhan 430074 Peoples R China

In this paper, the tracking consensus of timescale-type nonlinear multiagent systems is investigated. First, the finite-time reachability of the designed integral sliding manifold is achieved through a discontinuous controller. Second, combining the proposed dynamic event-triggered strategy, sufficient condition for practical tracking consensus is obtained by constructing non-negative functions and applying inequality techniques. In addition, in combination with time scale calculus theory, Zeno behavior is excluded. The framework for sliding mode control (SMC) of timescale-type multiagent systems is constructed to generalize previous continuous-time SMC and discrete-time SMC results. Ultimately, the feasibility of the obtained theoretical results is validated through two numerical simulations.

关键词： Multiagent systems Sliding mode control Event-triggered control Tracking consensus Time scale

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A Novel Optimal Distributed Nonlinear Filter for Simultaneous State and Unknown Input Estimation in Multi-Sensor Networks

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IEEE Signal processing Letters 2025年 32卷 1745-1749页

作者： Ding, Bo Wei, Yuanchu Zhang, Enze Fang, Huajing Yangzhou University College of Information Engineering Yangzhou225127 China Huazhong University of Science and Technology School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control Ministry of Education Wuhan430074 China

This letter investigates the problem of simultaneous state and unknown input estimation in a nonlinear system within multi-sensor networks. To avoid the linearization errors caused by existing methods, such as statistical linear regression techniques and first-order Taylor approximation, a novel optimal distributed nonlinear filter is proposed based on the unscented transformation and unbiased minimum variance criterion. First, the unscented transformation is applied to generate predicted estimates and the covariance matrix. Second, input and state estimation are carried out in an unbiased minimum variance manner using measurements from the nonlinear system. Third, a distributed strategy leveraging average consensus is employed to incorporate local estimates from neighboring sensors. Finally, simulation results confirm that the proposed method achieves substantial improvements in estimation accuracy compared to existing methods. © 1994-2012 IEEE.

关键词： Covariance matrix

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Real-time dynamic medical image encryption based on extended multi-scroll memristive Hopfield neural network

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NONLINEAR DYNAMICS 2025年第12期113卷 15517-15536页

作者： Leng, Xiangxin Wang, Xiaoping Du, Baoxiang Ren, Fangmin Zeng, Zhigang Educ Minist China Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan Peoples R China Huazhong Univ Sci & Technol Hubei Key Lab Brain inspired Intelligent Syst Wuhan 430074 Peoples R China Heilongjiang Univ Elect Engn Coll Harbin 150080 Peoples R China

The memristor's inherent memristive and nonlinear properties make it particularly well-suited for simulating synaptic connections in neural networks, inducing rich dynamical behaviors that are essential for understanding brain mechanisms and brain-like learning. In this paper, a new locally active non-volatile trigonometric memristor is constructed and coupled into the Hopfield neural network. The motion state of the memristive Hopfield neural network (MHNN) is influenced by the coupling strength, allowing it to exhibit periodic initial offset boosting behavior. Furthermore, the MHNN demonstrates unique multi-scroll attractor extension behaviors. The number of attractor scrolls increases continuously with parameters variations at fixed time intervals, although there is an upper limit. However, as simulation time extends, the number of attractor scrolls can grow indefinitely, with newly formed scrolls extending monotonically in both directions under different conditions. The MHNN is implemented using both analog circuits and the DSP platform. Eventually, a real-time image encryption scheme aimed at protecting medical image privacy is designed, supported by practical test. In particular, the scheme can be further applied to remote video medical protection, which can encrypt the treatment content.

关键词： Memristive hopfield neural network Multi-scroll attractor Initial offset boosting Real-time image encryption

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Adaptive Event-Triggered Distributed Observer Under Communication Link Faults

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IEEE TRANSACTIONS ON AUTOMATIC control 2025年第5期70卷 2992-3007页

作者： Su, Housheng Zhu, Haoxuan Zeng, Zhigang Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Educ Minist China Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China

This article addresses the problem of event-based fully distributed state estimation for an unperturbed linear time-invariant system in the presence of communication link faults. In a networked system, each agent is capable of constructing its local observer adaptively and transmitting its estimation to neighbors through discrete communication, thereby achieving accurate estimation of the system state while saving communication resources effectively. Specifically, first, the target system state can be decomposed into detectable and undetectable parts through detectability decomposition. Based on this, an adaptive event-triggered distributed observer is proposed, employing event-based adaptive coupling gains without the necessity for any global information. Additionally, a dynamic event-triggered mechanism with a mixed event-triggered threshold is introduced to generate the event-triggered sequence. Subsequently, the effectiveness of the proposed observer is verified through the analysis of estimation error and the assessment of Zeno behavior. Finally, the theoretical results of this article are validated through some simulation examples.

关键词： Event detection Observers Directed graphs Adaptive systems Network topology Couplings Topology Symmetric matrices Estimation error Eigenvalues and eigenfunctions Adaptive scheme communication link faults distributed state estimation event-triggered strategy

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Adaptive Tracking control of Switched Stochastic Non-Linear Systems With Full State Constraints: A MDADT Switching Method

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR control 2025年第9期35卷 3763-3777页

作者： Kong, Meng-Ru Wang, Yan-Wu Zeng, Ze-Hong Liu, Xiao-Kang Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Wuhan Peoples R China

This article proposes an adaptive tracking control scheme for a class of switched stochastic non-linear systems subject to full state constraints (FSCs). A unified framework is established by integrating the adaptive backstepping technique with barrier Lyapunov functions (BLFs) to address the time-varying full state constraints (TFSCs). Accordingly, utilizing the theorem of stochastic non-linear systems, a new adaptive state feedback control scheme is developed for a class of switching signals with the mode-dependent average dwell time (MDADT) property. The proposed adaptive controller is mode-dependent while the adaptive laws are mode-independent so as to facilitate an easy deployment of the controller. It is rigorously proven that all signals in the closed-loop system are bounded in probability, the tracking error converges to a bounded compact set, and the TFSCs are not violated. Finally, simulation examples are conducted to validate the effectiveness of the controller.

关键词： adaptive tracking control full state constraints (FSCs) mode-dependent average dwell time (MDADT) switched stochastic non-linear systems

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Backstepping-Based Composite Prescribed-Time control for Two-Time-Scale Systems

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR control 2025年第8期35卷 3116-3127页

作者： Zeng, Ze-Hong Wang, Yan-Wu Liu, Xiao-Kang Liu, Zhi-Wei Huazhong Univ Sci & Technol Sch Artif Intelligence & Automat Wuhan Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Wuhan Peoples R China

This paper explores the design of a composite prescribed-time controller for two-time-scale systems (TTSSs). First, due to numerical issues caused by the small perturbation parameter, standard control design techniques for prescribed-time stabilization of regular linear systems are typically unsuitable for TTSSs. To address this, we use time-scale separation to decouple the system into slow and fast subsystems. To avoid strict requirements on the input matrix, we apply a state transformation to convert the state equations of the slow and fast subsystems into block forms. Next, to prevent input saturation and achieve a directly and arbitrarily adjustable upper bound on the convergence time, we use time-varying scaling functions to design prescribed-time controllers for both subsystems. The composite prescribed-time controller ensures the prescribed-time convergence of the TTSS. Finally, two comparative examples and one RC ladder circuit system example are presented to demonstrate the effectiveness and advantages of the proposed approach.

关键词： backstepping control prescribed-time stability time-varying scaling function two-time-scale system

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Dynamic Resilient Consensus of Second-Order Multiagent Networks With a Time-Varying Malicious Agent Set

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IEEE TRANSACTIONS ON AUTOMATIC control 2025年第5期70卷 3308-3315页

作者： Liu, Ze-Qiang Liu, Zhi-Wei He, Ding-Xin Ye, Lintao Chi, Ming Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automation Wuhan 430074 Peoples R China

We address the problem of resilient consensus in second-order multiagent networks (MANs) with a time-varying set of malicious agents. Existing research typically assumes a static set of malicious agents. However, in real-world scenarios, network attacks and recoveries may lead to dynamic changes in the roles of agents. To overcome this challenge, a novel adaptive weighted subsequence reduction (AWSR) is proposed for resilient consensus in MANs with a dynamic malicious agent set. Based on the AWSR approach, a resilient consensus algorithm is designed for second-order MANs. Graph-theoretic conditions in the context of network robustness are derived to ensure resilient consensus in the considered MAN. We also extend the algorithm and analysis to MANs with communication delays. Finally, simulation experiments are conducted to demonstrate the effectiveness of the proposed algorithms.

关键词： Heuristic algorithms Safety Vehicle dynamics Switches Topology Robustness Delays Network topology Convergence Buildings Adaptive weighted subsequence reduction (AWSR) communication delays dynamic malicious agent set multiagent network (MAN) resilient consensus

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Roll Torque Vibration Isolation control on Parallel Active Suspension System via Impedance-Based MPC for Vehicular Terminal

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 13253-13268页

作者： Xue, Junfeng Chen, Zhihua Wang, Shoukun Wang, Junzheng Xu, Yongkang Beijing Inst Technol State Key Lab Intelligent Control & Decis Complex Beijing Peoples R China Nanchang Hangkong Univ Key Lab Jiangxi Prov Image Proc & Pattern Recognit Nanchang 330063 Peoples R China Nanchang Hangkong Univ Key Lab Nondestruct Testing Technol MOE Nanchang 330063 Peoples R China

To improve the stability of vehicle body dynamics on rugged roads, the majority of researchers have studied vertical force control at the terminal of the vehicle utilizing an active suspension system. However, terminal disturbance roll torque can also have a negative impact on the stability of the vehicle's state. In this paper, a terminal roll torque control strategy (TRTCS) is proposed to enhance the stability of body attitude by controlling terminal roll torque in wheeled motion. The strategy includes impedance-based model predictive control (IMPC) and terminal posture return controller (TPRC). Firstly, IMPC analyzes the single-leg dynamic model embedded with torque impedance at the terminal. Then, the controller iterates through quadratic optimization to obtain an optimal impedance target torque that can efficiently mitigate terminal disturbance roll torque while stabilizing the terminal roll velocity, acceleration, and force on each electric cylinder. Then, TPRC generates additional smooth return speed for the terminal roll angle to the initial value through the Bezier curve and event-trigger mechanisms to estimate the terminal contact state to the ground. TPRC aims to compensate for low angular return speed due to insufficient measured torque on the suspending terminal during the downhill process. Finally, through simulation and experiment, TRTCS significantly diminishes the deviation of terminal roll torque and body attitude from target value to 25.2% and 35.8% respectively when a vehicle crosses multi-slopes compared with traditional torque *** to Practitioners-The motivation for this work is that the vibration isolation control on vehicles or wheeled robots often neglects the disturbance of the roll torque on their feet on rough roads. Unfortunately, this can damage the leg actuators of vehicles or robots, and even have a negative impact on the steadiness of the body posture. Therefore, this paper proposes a TRTCS, which controls the terminal rol

关键词： Active suspension system vibration isolation control impedance control impedance control torque control torque control parallel structure parallel structure parallel structure

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Event denoising for dynamic vision sensor using residual graph neural network with density-based spatial clustering☆

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NEUROCOMPUTING 2025年 636卷

作者： Feng, Weibin Wang, Xiaoping Zhan, Xin Huang, Hongzhi Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automation Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Educ Minist China Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Hubei Key Lab Brain inspired Intelligent Syst Wuhan 430074 Peoples R China

The bio-inspired emerging dynamic vision sensor (DVS), characterized by its exceptional high temporal resolution and immediate response, possesses an innate advantage in capturing rapidly changing scenes. Nevertheless, it is also susceptible to severe noise interference, especially in challenging conditions like low illumination and high exposure. Notably, the existing noise processing approaches tend to oversimplify data into 2-dimensional (2D) patterns, disregarding the sparse and irregular crucial event structure information that the DVS intrinsically provides via its asynchronous output. Aiming at these problems, we propose a residual graph neural network (RGNN) framework based on density spatial clustering for event denoising, called DBRGNN. Leveraging the temporal window rule, we extract non-overlapping event segments from the DVS event stream and adopt a density-based spatial clustering algorithm to obtain event groups with spatial correlations. To fully exploit the inherent sparsity and plentiful spatiotemporal information of the raw event stream, we transform each event group as compact graph representations via directed edges and feed them into a graph coding module composed of a series of graph convolutional and pooling layers to learn robust geometric features from event sequences. Importantly, our approach effectively reduces noise levels without compromising the spatial structure and temporal coherence of spike events. Compared with other baseline methods, our DBRGNN achieves competitive performance by quantitative and qualitative evaluations on publicly available datasets under varying lighting conditions and noise ratios.

关键词： DVS Event denoising Temporal window Density-based clustering Graph learning for visual processing

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Zero-Sum Game-Based Distributed Secondary control for DC Microgrids Against Stealthy Attacks

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2025年第2期55卷 864-875页

作者： Cheng, Fabin Lai, Jingang Zeng, Zhigang Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Minist Educ Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China

This article investigates the voltage restoration problem of direct current (DC) microgrids (MGs) under stealth attacks. The fact is that secondary control requires information exchange through sparse communication networks, which makes cybersecurity the key to achieving voltage restoration. Compared with the attack signals launched by nonintelligent attackers, stealthy attack signals are more difficult to capture. Similarly, since MGs actually operate in a closed environment, malicious attackers have limited knowledge of the MG's structural information. Therefore, we reformulate the problem to a two-person zero-sum game between the attacker and defender and then seek the optimal voltage restoration control strategy. In the process of solving the optimal problem, both robustness and convergence of parameter estimation are considered in this article to enhance the feasibility of adaptive dynamic programming schemes. Additionally, while ensuring the voltage restoration performance of DC MGs and considering the constraints of actual network communication, a novel dynamic event-triggered mechanism is designed to reduce information transmission pressure. The effectiveness of the established control scheme is verified through real-time testing equipment built on OPAL-RT and comparison results.

关键词： Voltage control Games Game theory Event detection Robustness Communication networks Resists Resistance Process control Microgrids Distributed secondary control dynamic event-triggered control stealthy attack voltage restoration zero-sum game

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