Pub Date : 2026-01-01DOI: 10.1016/j.isatra.2025.11.013
Xiaotao Zhou , Jieqing Tan , Yangang Yao , Anguo Zhang
This paper addresses the exponential stability problem of stochastic networked control systems (SNCSs) subjected to both state-dependent and state-independent deception attacks. A novel self-triggered impulsive framework is proposed, comprising two strategies: self-triggered impulsive control (STIC) and self-triggered delayed impulsive control (STDIC), tailored for systems without and with impulse delays, respectively. In contrast to existing STIC approaches, the proposed methods impose no restrictions on inter-impulse intervals, eliminate the need for auxiliary comparison systems, and avoid complex implicit formulations. These advantages render the schemes more flexible and amenable to practical implementation. Moreover, unlike event-triggered impulsive control (ETIC), the STIC and STDIC strategies do not require continuous or periodic event detection while effectively excluding Zeno behavior. Sufficient conditions are established to ensure exponential stability under both types of deception attacks, explicitly revealing the interplay among triggering parameters, time delays, attack probabilities, and impulse gain. Finally, simulation results validate the effectiveness of the proposed methods.
{"title":"Stability analysis of stochastic networked control systems under deception attacks: A novel self-triggered impulsive framework","authors":"Xiaotao Zhou , Jieqing Tan , Yangang Yao , Anguo Zhang","doi":"10.1016/j.isatra.2025.11.013","DOIUrl":"10.1016/j.isatra.2025.11.013","url":null,"abstract":"<div><div>This paper addresses the exponential stability problem of stochastic networked control systems (SNCSs) subjected to both state-dependent and state-independent deception attacks. A novel self-triggered impulsive framework is proposed, comprising two strategies: self-triggered impulsive control (STIC) and self-triggered delayed impulsive control (STDIC), tailored for systems without and with impulse delays, respectively. In contrast to existing STIC approaches, the proposed methods impose no restrictions on inter-impulse intervals, eliminate the need for auxiliary comparison systems, and avoid complex implicit formulations. These advantages render the schemes more flexible and amenable to practical implementation. Moreover, unlike event-triggered impulsive control (ETIC), the STIC and STDIC strategies do not require continuous or periodic event detection while effectively excluding Zeno behavior. Sufficient conditions are established to ensure exponential stability under both types of deception attacks, explicitly revealing the interplay among triggering parameters, time delays, attack probabilities, and impulse gain. Finally, simulation results validate the effectiveness of the proposed methods.</div></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":"168 ","pages":"Pages 319-329"},"PeriodicalIF":6.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145535131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01DOI: 10.1016/j.isatra.2025.11.009
Jingliang Sun , Mengmeng Wang , Zihan Wang , Chen Chen
In this paper, a profile-tracking-based gain-varying backstepping guidance law is developed to address the impact angle constrained guidance issue for the maneuvering target with input saturation. A new range-elapsed-based polynomial function is formulated as the reference line-of-sight angle profile to guide the missile to intercept the target with a specified angle. The profile can be analytically determined without tedious numerical optimization. Then, a novel gain-varying finite-time constrained backstepping guidance law is developed for tracking the designed profile. The tracking-error-based varying gain is designed to counteract the disturbance effects of target maneuvers. The stability of the closed-loop system is theoretically guaranteed. Finally, comparative simulations verify the effectiveness and advantages of the proposed method.
{"title":"Profile-tracking-based gain-varying backstepping guidance law for maneuvering target interception with input saturation","authors":"Jingliang Sun , Mengmeng Wang , Zihan Wang , Chen Chen","doi":"10.1016/j.isatra.2025.11.009","DOIUrl":"10.1016/j.isatra.2025.11.009","url":null,"abstract":"<div><div>In this paper, a profile-tracking-based gain-varying backstepping guidance law is developed to address the impact angle constrained guidance issue for the maneuvering target with input saturation. A new range-elapsed-based polynomial function is formulated as the reference line-of-sight angle profile to guide the missile to intercept the target with a specified angle. The profile can be analytically determined without tedious numerical optimization. Then, a novel gain-varying finite-time constrained backstepping guidance law is developed for tracking the designed profile. The tracking-error-based varying gain is designed to counteract the disturbance effects of target maneuvers. The stability of the closed-loop system is theoretically guaranteed. Finally, comparative simulations verify the effectiveness and advantages of the proposed method.</div></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":"168 ","pages":"Pages 401-412"},"PeriodicalIF":6.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145552508","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01DOI: 10.1016/j.isatra.2025.10.017
Yitong Zhou , Jing Chang , Weisheng Chen
The design of control schemes for heat exchanger systems in large-scale industrial scenarios is challenged by complex dynamics and safety requirements. This paper proposes a human-like model-free control (HLMFC) scheme. The scheme is independent of model information, learning decision-making rules during manual control processes of heat exchangers from experienced operators. Exploiting the thermal equilibrium characteristic of the heat exchangers, a control efficiency factor and control interval iteration mechanism are introduced, which gradually compresses the control input to the desired value. The proposed control strategy eliminates the need for dynamic analysis, suppresses instabilities in the heat transfer process, and allows flexible adjustment of the actuator execution frequency. The bounded stability of the scheme is rigorously proven. Experimental results demonstrate that the proposed strategy achieves control objectives while flexibly allocating actuator frequencies, effectively reducing temperature fluctuations during operation.
{"title":"Human-like model-free control for heat exchangers with a new form of actuator bandwidth limitation","authors":"Yitong Zhou , Jing Chang , Weisheng Chen","doi":"10.1016/j.isatra.2025.10.017","DOIUrl":"10.1016/j.isatra.2025.10.017","url":null,"abstract":"<div><div>The design of control schemes for heat exchanger systems in large-scale industrial scenarios is challenged by complex dynamics and safety requirements. This paper proposes a human-like model-free control (HLMFC) scheme. The scheme is independent of model information, learning decision-making rules during manual control processes of heat exchangers from experienced operators. Exploiting the thermal equilibrium characteristic of the heat exchangers, a control efficiency factor and control interval iteration mechanism are introduced, which gradually compresses the control input to the desired value. The proposed control strategy eliminates the need for dynamic analysis, suppresses instabilities in the heat transfer process, and allows flexible adjustment of the actuator execution frequency. The bounded stability of the scheme is rigorously proven. Experimental results demonstrate that the proposed strategy achieves control objectives while flexibly allocating actuator frequencies, effectively reducing temperature fluctuations during operation.</div></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":"168 ","pages":"Pages 630-640"},"PeriodicalIF":6.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145644078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01DOI: 10.1016/j.isatra.2025.11.005
Roberto Costa Ceccato, José Roberto Castilho Piqueira
This manuscript presents a reduced chattering sliding mode control (SMC) strategy for automatically regulating the depth of hypnosis (DoH) during general anesthesia (GA). The controller uses DoH as the controlled variable and assumes an infusion pump as the actuator. A linear model serves as an approximation in the SMC law, while a sigmoidal function is used instead of the sign function to mitigate chattering. The scheme adopts a tracking-based approach to handle induction and maintenance phases of GA. The method is evaluated through simulations with and without pain stimuli and noise, using real-patient pharmacokinetic and pharmacodynamic parameters and incorporating the dynamic behavior of the DoH monitor. Results showed no high-frequency chattering and demonstrated robust performance, suggesting that the proposed approach is promising for real-world clinical applications.
{"title":"Reduced chattering target-tracking sliding mode control for intraprocedural propofol control","authors":"Roberto Costa Ceccato, José Roberto Castilho Piqueira","doi":"10.1016/j.isatra.2025.11.005","DOIUrl":"10.1016/j.isatra.2025.11.005","url":null,"abstract":"<div><div>This manuscript presents a reduced chattering sliding mode control (SMC) strategy for automatically regulating the depth of hypnosis (DoH) during general anesthesia (GA). The controller uses DoH as the controlled variable and assumes an infusion pump as the actuator. A linear model serves as an approximation in the SMC law, while a sigmoidal function is used instead of the sign function to mitigate chattering. The scheme adopts a tracking-based approach to handle induction and maintenance phases of GA. The method is evaluated through simulations with and without pain stimuli and noise, using real-patient pharmacokinetic and pharmacodynamic parameters and incorporating the dynamic behavior of the DoH monitor. Results showed no high-frequency chattering and demonstrated robust performance, suggesting that the proposed approach is promising for real-world clinical applications.</div></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":"168 ","pages":"Pages 199-210"},"PeriodicalIF":6.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145530713","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01DOI: 10.1016/j.isatra.2025.10.046
Ding Zhou , Ping Chen , Zhigang Cao , Chuan He , Xiaopeng Han , Yukun Niu
This paper investigates the finite- and fixed-time privacy-preserving formation control problem for multiple quadrotors with input delay and connectivity maintenance. A lightweight confidential interaction protocol based on group key agreement is first proposed to ensure secure communication among quadrotors under limited computational resources. To address the input delay, an extended Artstein’s transformation is introduced to convert the system into a delay-free form, and by integrating potential functions with finite-/fixed-time control techniques, novel formation control algorithms are developed to resolve the delay issue while preserving the initial interaction topology. Leveraging Lyapunov stability theory and bi-limit homogeneous system theory, rigorous theoretical analysis is conducted to derive sufficient conditions for finite- and fixed-time formability of the quadrotor formation system. The proposed framework systematically resolves the coupling challenges among privacy protection, time-delay compensation, and topology preservation. Numerical simulations and flight experiments are carried out to illustrate the effectiveness of the theoretical results.
{"title":"Finite- and fixed-time privacy-preserving formation control for multiple quadrotor systems with input delay and connectivity maintenance","authors":"Ding Zhou , Ping Chen , Zhigang Cao , Chuan He , Xiaopeng Han , Yukun Niu","doi":"10.1016/j.isatra.2025.10.046","DOIUrl":"10.1016/j.isatra.2025.10.046","url":null,"abstract":"<div><div>This paper investigates the finite- and fixed-time privacy-preserving formation control problem for multiple quadrotors with input delay and connectivity maintenance. A lightweight confidential interaction protocol based on group key agreement is first proposed to ensure secure communication among quadrotors under limited computational resources. To address the input delay, an extended Artstein’s transformation is introduced to convert the system into a delay-free form, and by integrating potential functions with finite-/fixed-time control techniques, novel formation control algorithms are developed to resolve the delay issue while preserving the initial interaction topology. Leveraging Lyapunov stability theory and bi-limit homogeneous system theory, rigorous theoretical analysis is conducted to derive sufficient conditions for finite- and fixed-time formability of the quadrotor formation system. The proposed framework systematically resolves the coupling challenges among privacy protection, time-delay compensation, and topology preservation. Numerical simulations and flight experiments are carried out to illustrate the effectiveness of the theoretical results.</div></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":"168 ","pages":"Pages 515-530"},"PeriodicalIF":6.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145530479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01DOI: 10.1016/j.isatra.2025.11.002
M.H. Korayem, Sh. Ameri, N. Yousefi Lademakhi
One of the limitations of applying Nonlinear Model Predictive Control (NMPC) in robotic systems is the high computational burden associated with the continuous solution of the Optimal Control Problem (OCP). In this paper, an intelligent central event-triggered method based on the variation of the gradient between the optimal state error and the actual state is proposed to achieve intermittent solving and reduce the frequency of OCP computations, consequently decreasing the computational time of NMPC. Unlike conventional event-triggered NMPC (ET-NMPC), which can degrade solution accuracy when combined with warm-starting, the proposed approach employs a Multilayer Perceptron Neural Network (MLP-NN) to predict the OCP inputs. This strategy reduces the number of iterations required per solution, enhances convergence, and enables the NMPC to track the trajectory more accurately, mitigating the accuracy loss typically associated with event-triggered methods. Simulation and experimental validation were performed on a wheeled mobile robot (WMR) platform. The results indicate that the proposed intelligent event-triggering mechanism reduces the computational time by 64.7 % compared to traditional NMPC, while improving the event-triggered tracking error by 18 %.
{"title":"A central event-triggered nonlinear MPC approach to reduce the computational time of WMR","authors":"M.H. Korayem, Sh. Ameri, N. Yousefi Lademakhi","doi":"10.1016/j.isatra.2025.11.002","DOIUrl":"10.1016/j.isatra.2025.11.002","url":null,"abstract":"<div><div>One of the limitations of applying Nonlinear Model Predictive Control (NMPC) in robotic systems is the high computational burden associated with the continuous solution of the Optimal Control Problem (OCP). In this paper, an intelligent central event-triggered method based on the variation of the gradient between the optimal state error and the actual state is proposed to achieve intermittent solving and reduce the frequency of OCP computations, consequently decreasing the computational time of NMPC. Unlike conventional event-triggered NMPC (ET-NMPC), which can degrade solution accuracy when combined with warm-starting, the proposed approach employs a Multilayer Perceptron Neural Network (MLP-NN) to predict the OCP inputs. This strategy reduces the number of iterations required per solution, enhances convergence, and enables the NMPC to track the trajectory more accurately, mitigating the accuracy loss typically associated with event-triggered methods. Simulation and experimental validation were performed on a wheeled mobile robot (WMR) platform. The results indicate that the proposed intelligent event-triggering mechanism reduces the computational time by 64.7 % compared to traditional NMPC, while improving the event-triggered tracking error by 18 %.</div></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":"168 ","pages":"Pages 598-609"},"PeriodicalIF":6.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145524840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01DOI: 10.1016/j.isatra.2025.11.017
Dazi Li , Jiahui Xu , Xin Xu
Trajectory tracking is a fundamental aspect of robotics research and it is essential for robots to track tasks effectively. However, manipulators are multi-input, multi-output systems characterized by high nonlinearity and strong coupling, often functioning under uncertain conditions, such as external random disturbances, parameter fluctuations, and unmodeled dynamics. Therefore, this paper proposes a learning-based predictive control method with a fuzzy extended state observer (LPC-FESO), integrating nonlinear predictive control with reinforcement learning to address the challenge of slow reinforcement learning (RL) convergence in stochastic environments and achieve desired trajectory tracking. A nonlinear predictive control, utilizing a fuzzy backstepping approach to generate the initial control sequence, serves as the base controller for Deep Deterministic Policy Gradient (DDPG). This design minimizes dependency on precise system modeling, enhances computational efficiency, and constrains joint angles and velocities via the value function. A fuzzy extended state observer (FESO), balancing both position and velocity states, is also designed to improve the system’s disturbance rejection capability, ensuring the required transient and steady-state tracking performance. The theoretical convergence properties of the LPC-FESO framework are provided firstly, considering disturbances and state constraints. The proposed framework targets a class of uncertain multi degree-of-freedom (DOF) manipulators that can be represented by the standard manipulator dynamics with bounded external disturbances and model uncertainties. In this paper, a 2-DOF manipulator is used as an example for demonstration and simulation. Simulation results demonstrate that the proposed approach effectively tracks desired trajectories in terms of both position and velocity, exhibits strong disturbance rejection capabilities, and meets the required performance criteria across various trajectory tracking tasks.
{"title":"Learning predictive control based on extended fuzzy state observation for trajectory tracking of an uncertain manipulator","authors":"Dazi Li , Jiahui Xu , Xin Xu","doi":"10.1016/j.isatra.2025.11.017","DOIUrl":"10.1016/j.isatra.2025.11.017","url":null,"abstract":"<div><div>Trajectory tracking is a fundamental aspect of robotics research and it is essential for robots to track tasks effectively. However, manipulators are multi-input, multi-output systems characterized by high nonlinearity and strong coupling, often functioning under uncertain conditions, such as external random disturbances, parameter fluctuations, and unmodeled dynamics. Therefore, this paper proposes a learning-based predictive control method with a fuzzy extended state observer (LPC-FESO), integrating nonlinear predictive control with reinforcement learning to address the challenge of slow reinforcement learning (RL) convergence in stochastic environments and achieve desired trajectory tracking. A nonlinear predictive control, utilizing a fuzzy backstepping approach to generate the initial control sequence, serves as the base controller for Deep Deterministic Policy Gradient (DDPG). This design minimizes dependency on precise system modeling, enhances computational efficiency, and constrains joint angles and velocities via the value function. A fuzzy extended state observer (FESO), balancing both position and velocity states, is also designed to improve the system’s disturbance rejection capability, ensuring the required transient and steady-state tracking performance. The theoretical convergence properties of the LPC-FESO framework are provided firstly, considering disturbances and state constraints. The proposed framework targets a class of uncertain multi degree-of-freedom (DOF) manipulators that can be represented by the standard manipulator dynamics with bounded external disturbances and model uncertainties. In this paper, a 2-DOF manipulator is used as an example for demonstration and simulation. Simulation results demonstrate that the proposed approach effectively tracks desired trajectories in terms of both position and velocity, exhibits strong disturbance rejection capabilities, and meets the required performance criteria across various trajectory tracking tasks.</div></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":"168 ","pages":"Pages 352-368"},"PeriodicalIF":6.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145644046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01DOI: 10.1016/j.isatra.2025.12.003
Yixiang Lu, Yuelong Huang, De Zhu, Dawei Zhao, Dong Sun
In real industrial production, bearings usually operate under variable operating conditions. However, existing deep learning-based bearing fault diagnosis methods overlook the complex structural relationships between fault signal data, leading to limitations in generalization ability. To address the above problems, a novel domain adaptation multi-graph convolutional network (DAM-GCN) bearing fault diagnosis method is proposed in this paper. First, we extract the basic fault signal features with the help of a convolutional neural network (CNN). Subsequently, Top-k graph, k-NN graph and Radius graph are used to generate graph structures, which utilize the local, similarity and density information in the data respectively, enabling the network to deeply capture the fault structure characteristics from multiple perspectives. Second, to ensure that these features can be effectively compared on the same scale, a contrastive learning strategy is employed to minimize the feature similarity within the same feature tensor to improve the distinguishability and expressiveness of the features. Finally, we jointly consider the classification loss and domain alignment loss. By minimizing the distribution and graph structure differences between the target and source domains, the fault diagnosis ability of the model under different working conditions is enhanced. Numerous experimental findings show that the proposed domain-adaptive multi-graph neural network-based approach outperforms existing SOTA methods.
{"title":"Domain adaptive fault diagnosis algorithm based on multi-graph convolution for rotating machinery","authors":"Yixiang Lu, Yuelong Huang, De Zhu, Dawei Zhao, Dong Sun","doi":"10.1016/j.isatra.2025.12.003","DOIUrl":"10.1016/j.isatra.2025.12.003","url":null,"abstract":"<div><div>In real industrial production, bearings usually operate under variable operating conditions. However, existing deep learning-based bearing fault diagnosis methods overlook the complex structural relationships between fault signal data, leading to limitations in generalization ability. To address the above problems, a novel domain adaptation multi-graph convolutional network (DAM-GCN) bearing fault diagnosis method is proposed in this paper. First, we extract the basic fault signal features with the help of a convolutional neural network (CNN). Subsequently, Top-k graph, k-NN graph and Radius graph are used to generate graph structures, which utilize the local, similarity and density information in the data respectively, enabling the network to deeply capture the fault structure characteristics from multiple perspectives. Second, to ensure that these features can be effectively compared on the same scale, a contrastive learning strategy is employed to minimize the feature similarity within the same feature tensor to improve the distinguishability and expressiveness of the features. Finally, we jointly consider the classification loss and domain alignment loss. By minimizing the distribution and graph structure differences between the target and source domains, the fault diagnosis ability of the model under different working conditions is enhanced. Numerous experimental findings show that the proposed domain-adaptive multi-graph neural network-based approach outperforms existing SOTA methods.</div></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":"168 ","pages":"Pages 620-629"},"PeriodicalIF":6.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145784183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01DOI: 10.1016/j.isatra.2025.11.007
Kairui Chen , Chengzhen Yu , Zhi Liu , C.L. Philip Chen , Jianhui Wang
This paper proposes an adaptive predefined-time prescribed performance control strategy for nonlinear multi-agent systems with privacy-preservation. Firstly, a privacy preservation method is designed to protect transmitting data within a user-defined time. By adjusting the mask factors, each node owns a unique private encryption, which enhances the privacy preservation. Meanwhile, a prescribed performance mechanism is designed to constrain the actual tracking error with masked information. Based on a predefined-time filter and a filtering error compensation technique, a kind of predefined-time prescribed performance consensus protocol is proposed for nonlinear multi-agent systems. Finally, several simulations are presented to verify the proposed strategies.
{"title":"Prescribed-performance consensus control for nonlinear MASs: a privacy preservation strategy","authors":"Kairui Chen , Chengzhen Yu , Zhi Liu , C.L. Philip Chen , Jianhui Wang","doi":"10.1016/j.isatra.2025.11.007","DOIUrl":"10.1016/j.isatra.2025.11.007","url":null,"abstract":"<div><div>This paper proposes an adaptive predefined-time prescribed performance control strategy for nonlinear multi-agent systems with privacy-preservation. Firstly, a privacy preservation method is designed to protect transmitting data within a user-defined time. By adjusting the mask factors, each node owns a unique private encryption, which enhances the privacy preservation. Meanwhile, a prescribed performance mechanism is designed to constrain the actual tracking error with masked information. Based on a predefined-time filter and a filtering error compensation technique, a kind of predefined-time prescribed performance consensus protocol is proposed for nonlinear multi-agent systems. Finally, several simulations are presented to verify the proposed strategies.</div></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":"168 ","pages":"Pages 340-351"},"PeriodicalIF":6.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145524838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01DOI: 10.1016/j.isatra.2025.11.011
Chenhao Wang, Liling Ma, Jiameng Wang, Hao Yu, Shoukun Wang
Research on fault diagnosis methods based on deep transfer learning is of great significance to both measurement science and automation engineering, with an increasing number of studies adopting wavelet-based neural network frameworks in combination with domain adaptation for cross-condition fault diagnosis. However, existing domain adaptation methods generally assume discrete domains, while real working conditions such as speed and load vary continuously, and this mismatch limits the effectiveness of domain adaptation. Meanwhile, for fault feature extraction in wavelet time-frequency diagrams, few studies consider the unique frequency distribution characteristics of different faults to design networks. Therefore, we propose a dual innovation fault diagnosis framework. Firstly, we introduce the Wavelet-Scale-Wise Convolution Network (WSWCN) to explicitly extract frequency-dependent fault features through a scale-wise convolution structure tailored for the directional sensitivity of wavelet time-frequency diagrams. Secondly, we propose a continuously indexed domain adaptation method based on Multi-Kernel Mutual Information Estimation (MKME), which leverages a variational form of mutual information and kernel-based function approximation to enable direct use of continuous working condition information for domain adaptation without adversarial training. To validate our approach, a series of experiments are conducted on gearbox and bearing fault datasets collected under time-varying working conditions to demonstrate the superiority of the proposed WSWCN and MKME.
{"title":"Information-theoretic continuously indexed domain adaptation network with wavelet-scale-wise convolution for fault diagnosis under continuously varying working conditions","authors":"Chenhao Wang, Liling Ma, Jiameng Wang, Hao Yu, Shoukun Wang","doi":"10.1016/j.isatra.2025.11.011","DOIUrl":"10.1016/j.isatra.2025.11.011","url":null,"abstract":"<div><div>Research on fault diagnosis methods based on deep transfer learning is of great significance to both measurement science and automation engineering, with an increasing number of studies adopting wavelet-based neural network frameworks in combination with domain adaptation for cross-condition fault diagnosis. However, existing domain adaptation methods generally assume discrete domains, while real working conditions such as speed and load vary continuously, and this mismatch limits the effectiveness of domain adaptation. Meanwhile, for fault feature extraction in wavelet time-frequency diagrams, few studies consider the unique frequency distribution characteristics of different faults to design networks. Therefore, we propose a dual innovation fault diagnosis framework. Firstly, we introduce the Wavelet-Scale-Wise Convolution Network (WSWCN) to explicitly extract frequency-dependent fault features through a scale-wise convolution structure tailored for the directional sensitivity of wavelet time-frequency diagrams. Secondly, we propose a continuously indexed domain adaptation method based on Multi-Kernel Mutual Information Estimation (MKME), which leverages a variational form of mutual information and kernel-based function approximation to enable direct use of continuous working condition information for domain adaptation without adversarial training. To validate our approach, a series of experiments are conducted on gearbox and bearing fault datasets collected under time-varying working conditions to demonstrate the superiority of the proposed WSWCN and MKME.</div></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":"168 ","pages":"Pages 413-426"},"PeriodicalIF":6.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145535078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号