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}
Pub Date : 2026-01-01DOI: 10.1016/j.isatra.2025.11.031
Liang Ma, Yifei Peng, Kaixiang Peng
In order to ensure the product quality and safe operation of automation systems, it is very important to perform efficient and accurate root cause diagnosis of faults in industrial processes. However, some traditional methods can only be used to analyze the linear causalities, and assume that the time series meet linear Gaussian assumption and are stationary after faults occur. Due to the fault information may propagated along with the causalities among process variables, the nonstationary and asymmetric characteristics make the time series regression models poorly fit, and the accuracy of causality analysis may be affected. Inspired by the above issues, in this paper, a new spatial-temporal fusion based nonlinear causality analysis framework is proposed for root cause diagnosis of faults in nonstationary industrial processes with asymmetric distribution. In particular, the problem of causality analysis for the coexistence of stationary and nonstationary time series, as well as the coexistence of symmetrical and asymmetrical distribution time series is given more attention. Firstly, a beta distribution based variational autoencoder is constructed to extract the asymmetric features of time series in industrial processes. Subsequently, a spatial-temporal fusion adjacency matrix is introduced by fast dynamic time warping, and the spatial-temporal fusion nonlinear Granger causality analysis is performed for diagnosing the root causes of faults. Finally, two datasets from the hot rolling process are used to verify the effectiveness and performance of the proposed framework.
{"title":"A spatial-temporal fusion based nonlinear causality analysis framework for root cause diagnosis of faults in nonstationary industrial processes with asymmetric distribution","authors":"Liang Ma, Yifei Peng, Kaixiang Peng","doi":"10.1016/j.isatra.2025.11.031","DOIUrl":"10.1016/j.isatra.2025.11.031","url":null,"abstract":"<div><div>In order to ensure the product quality and safe operation of automation systems, it is very important to perform efficient and accurate root cause diagnosis of faults in industrial processes. However, some traditional methods can only be used to analyze the linear causalities, and assume that the time series meet linear Gaussian assumption and are stationary after faults occur. Due to the fault information may propagated along with the causalities among process variables, the nonstationary and asymmetric characteristics make the time series regression models poorly fit, and the accuracy of causality analysis may be affected. Inspired by the above issues, in this paper, a new spatial-temporal fusion based nonlinear causality analysis framework is proposed for root cause diagnosis of faults in nonstationary industrial processes with asymmetric distribution. In particular, the problem of causality analysis for the coexistence of stationary and nonstationary time series, as well as the coexistence of symmetrical and asymmetrical distribution time series is given more attention. Firstly, a beta distribution based variational autoencoder is constructed to extract the asymmetric features of time series in industrial processes. Subsequently, a spatial-temporal fusion adjacency matrix is introduced by fast dynamic time warping, and the spatial-temporal fusion nonlinear Granger causality analysis is performed for diagnosing the root causes of faults. Finally, two datasets from the hot rolling process are used to verify the effectiveness and performance of the proposed framework.</div></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":"168 ","pages":"Pages 305-318"},"PeriodicalIF":6.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145590487","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.2025.11.025
Shuting Wang, Jinsha Li, Junmin Li
This paper investigates the distributed learning consensus control problem for nonstrict pure-feedback multi-agent systems using neural networks and an incremental adaptive mechanism. A unified adaptive learning consensus control framework is first established by integrating backstepping techniques with neural network approximation. To address the algebraic loop problem inherent in conventional approaches, we develop a neural network-based solution that simultaneously simplifies controller architecture. The proposed incremental adaptation strategy enables efficient parameter updating while significantly reducing computational overhead. Notably, the control scheme incorporates robustness analysis during the design phase to effectively resolve the complexity explosion issue. Theoretical analysis demonstrates that the distributed protocol guarantees prescribed tracking performance while ensuring the uniform boundedness of all closed-loop signals. The numerical case studies validate the effectiveness and learning capabilities of the proposed adaptive control algorithm.
{"title":"Neuroadaptive consensus learning for multi-agent systems: An incremental approach to nonstrict pure-feedback control","authors":"Shuting Wang, Jinsha Li, Junmin Li","doi":"10.1016/j.isatra.2025.11.025","DOIUrl":"10.1016/j.isatra.2025.11.025","url":null,"abstract":"<div><div>This paper investigates the distributed learning consensus control problem for nonstrict pure-feedback multi-agent systems using neural networks and an incremental adaptive mechanism. A unified adaptive learning consensus control framework is first established by integrating backstepping techniques with neural network approximation. To address the algebraic loop problem inherent in conventional approaches, we develop a neural network-based solution that simultaneously simplifies controller architecture. The proposed incremental adaptation strategy enables efficient parameter updating while significantly reducing computational overhead. Notably, the control scheme incorporates robustness analysis during the design phase to effectively resolve the complexity explosion issue. Theoretical analysis demonstrates that the distributed protocol guarantees prescribed tracking performance while ensuring the uniform boundedness of all closed-loop signals. The numerical case studies validate the effectiveness and learning capabilities of the proposed adaptive control algorithm.</div></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":"168 ","pages":"Pages 107-116"},"PeriodicalIF":6.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145679760","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.2025.11.039
Huiqin Pei, Weisen Liang
The group consensus tracking of multi-agent systems has been extensively applied in the coordination of unmanned system formations. However, existing methods encounter difficulties in group consensus tracking regarding the group division for the observation cooperative correction term, the enhancement of the self-adjustment ability of tracking agents and the further optimization of communication resources. Therefore, the problem of observer-based group consensus tracking of hybrid multi-agent systems under adaptive event-triggered mechanism is studied. An improved observation strategy with global observation and observation cooperative correction term clustering is proposed. By designing adaptive gain and adaptive event-triggered consensus error, an adaptive group consensus tracking control protocol is presented. To optimize the application of communication resources, an adaptive event-triggered mechanism is designed, and the occurrence of Zeno behavior is fundamentally excluded. Lyapunov method and Barbalat’s lemma are used to prove that the system can attain group consensus tracking. Finally, the efficacy of the research results is validated by a simulation example and comparative experiments.
{"title":"Observer-based group consensus tracking of hybrid multi-agent systems under adaptive event-triggered mechanism","authors":"Huiqin Pei, Weisen Liang","doi":"10.1016/j.isatra.2025.11.039","DOIUrl":"10.1016/j.isatra.2025.11.039","url":null,"abstract":"<div><div>The group consensus tracking of multi-agent systems has been extensively applied in the coordination of unmanned system formations. However, existing methods encounter difficulties in group consensus tracking regarding the group division for the observation cooperative correction term, the enhancement of the self-adjustment ability of tracking agents and the further optimization of communication resources. Therefore, the problem of observer-based group consensus tracking of hybrid multi-agent systems under adaptive event-triggered mechanism is studied. An improved observation strategy with global observation and observation cooperative correction term clustering is proposed. By designing adaptive gain and adaptive event-triggered consensus error, an adaptive group consensus tracking control protocol is presented. To optimize the application of communication resources, an adaptive event-triggered mechanism is designed, and the occurrence of Zeno behavior is fundamentally excluded. Lyapunov method and Barbalat’s lemma are used to prove that the system can attain group consensus tracking. Finally, the efficacy of the research results is validated by a simulation example and comparative experiments.</div></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":"168 ","pages":"Pages 81-93"},"PeriodicalIF":6.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145688873","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.2025.11.046
Wenyu Qin , Yueyong Lyu , Pengyu Wang , Guangfu Ma , Zhiyong Sun
This paper develops a communication-efficient event-triggered intermittent control strategy for multi-agent coordination in directed graphs. The proposed approach establishes a condition ensuring the existence of a minimum time interval between consecutive triggered events, meaning that the event will not be triggered immediately even if the triggering condition is satisfied. This mechanism effectively reduces the communication burden and eliminates Zeno behavior. Another key advantage is that the proposed strategy is asynchronous and aperiodic, and does not require any additional constraints on control or rest periods, thus reducing the conservatism of intermittent control strategies. Finally, both numerical simulations and physical experiments are conducted on a multi-UAV coordination platform to validate the effectiveness and practical applicability of the proposed strategy.
{"title":"Event-triggered multi-agent coordination in directed graphs: An intermittent control approach","authors":"Wenyu Qin , Yueyong Lyu , Pengyu Wang , Guangfu Ma , Zhiyong Sun","doi":"10.1016/j.isatra.2025.11.046","DOIUrl":"10.1016/j.isatra.2025.11.046","url":null,"abstract":"<div><div>This paper develops a communication-efficient event-triggered intermittent control strategy for multi-agent coordination in directed graphs. The proposed approach establishes a condition ensuring the existence of a minimum time interval between consecutive triggered events, meaning that the event will not be triggered immediately even if the triggering condition is satisfied. This mechanism effectively reduces the communication burden and eliminates Zeno behavior. Another key advantage is that the proposed strategy is asynchronous and aperiodic, and does not require any additional constraints on control or rest periods, thus reducing the conservatism of intermittent control strategies. Finally, both numerical simulations and physical experiments are conducted on a multi-UAV coordination platform to validate the effectiveness and practical applicability of the proposed strategy.</div></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":"168 ","pages":"Pages 590-597"},"PeriodicalIF":6.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145717029","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.2025.11.043
Yuehang Liu , Xiuyu Zhang , Yue Wang
Hysteresis nonlinearities, time delays and output constraints are widely present in the practical physical systems such as motion platforms driven by smart material actuators, bionic robots and ultra-high precision machining systems, however, these factors will degrade the control performance and may even induce oscillations in the control system. To overcome the aforementioned problems, in this paper, an adaptive neural pseudo-inverse control scheme is proposed for a class of time-delay nonlinear hysteresis systems considering output constraints with the following contributions: 1) a novel butterfly-like Krasnoselskii-Pokrovskii (BKP) hysteresis model with double loops is constructed to describe the double-loop hysteresis by performing the weighted superposition of the new proposed butterfly-like KP kernel; 2) a novel adaptive pseudo-inverse control algorithm is developed to avoid the difficulty of constructing the direct double-loop inverse model; 3) a new motion control platform actuated by the flexible dielectric elastomer actuators is established to verify the effectiveness of the proposed control scheme and demonstrate its feasibility for drive control systems in soft bionic robots.
{"title":"Adaptive neural pseudo-inverse control for time-delay nonlinear hysteretic systems considering output constraint and its application","authors":"Yuehang Liu , Xiuyu Zhang , Yue Wang","doi":"10.1016/j.isatra.2025.11.043","DOIUrl":"10.1016/j.isatra.2025.11.043","url":null,"abstract":"<div><div>Hysteresis nonlinearities, time delays and output constraints are widely present in the practical physical systems such as motion platforms driven by smart material actuators, bionic robots and ultra-high precision machining systems, however, these factors will degrade the control performance and may even induce oscillations in the control system. To overcome the aforementioned problems, in this paper, an adaptive neural pseudo-inverse control scheme is proposed for a class of time-delay nonlinear hysteresis systems considering output constraints with the following contributions: 1) a novel butterfly-like Krasnoselskii-Pokrovskii (BKP) hysteresis model with double loops is constructed to describe the double-loop hysteresis by performing the weighted superposition of the new proposed butterfly-like KP kernel; 2) a novel adaptive pseudo-inverse control algorithm is developed to avoid the difficulty of constructing the direct double-loop inverse model; 3) a new motion control platform actuated by the flexible dielectric elastomer actuators is established to verify the effectiveness of the proposed control scheme and demonstrate its feasibility for drive control systems in soft bionic robots.</div></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":"168 ","pages":"Pages 531-542"},"PeriodicalIF":6.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145759123","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}
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