Pub Date : 2025-09-30DOI: 10.1109/TSMC.2025.3612585
Yu Gao;Wei Sun;Ning Sun
This study concentrates on adaptive prescribed-time tracking control for n-link flexible-joint (FJ) manipulators with unmeasurable state variables. First of all, auxiliary signals are constructed utilizing measurable variables. Based on auxiliary signals, the observer is directly designed to estimate the system states, which allows the observer dynamics to incorporate unknown terms. Owing to the uniqueness of the designed observer, the observation errors converge to zero. Furthermore, with the aim of enhancing control efficiency, the prescribed-time scale function is introduced into the controllers, and the unknown terms are processed based on the fuzzy logic system (FLS), so that the tracking error of FJ manipulators converges to the specified range within the prescribed time. Meanwhile, with the help of positive integrable time-varying functions, asymptotic tracking is further achieved. In the whole control design, the tuning functions are adopted to avoid overparameterization. In theory, it is ensured that all signals in the closed-loop system are bounded, and the tracking error converges to the small neighborhood of the zero within a specified time and gradually converges to zero. Finally, the simulation example confirms the feasibility of the control design.
{"title":"Observer-Based Adaptive Prescribed-Time Asymptotic Tracking Control for Flexible-Joint Manipulators","authors":"Yu Gao;Wei Sun;Ning Sun","doi":"10.1109/TSMC.2025.3612585","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3612585","url":null,"abstract":"This study concentrates on adaptive prescribed-time tracking control for n-link flexible-joint (FJ) manipulators with unmeasurable state variables. First of all, auxiliary signals are constructed utilizing measurable variables. Based on auxiliary signals, the observer is directly designed to estimate the system states, which allows the observer dynamics to incorporate unknown terms. Owing to the uniqueness of the designed observer, the observation errors converge to zero. Furthermore, with the aim of enhancing control efficiency, the prescribed-time scale function is introduced into the controllers, and the unknown terms are processed based on the fuzzy logic system (FLS), so that the tracking error of FJ manipulators converges to the specified range within the prescribed time. Meanwhile, with the help of positive integrable time-varying functions, asymptotic tracking is further achieved. In the whole control design, the tuning functions are adopted to avoid overparameterization. In theory, it is ensured that all signals in the closed-loop system are bounded, and the tracking error converges to the small neighborhood of the zero within a specified time and gradually converges to zero. Finally, the simulation example confirms the feasibility of the control design.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 12","pages":"9165-9174"},"PeriodicalIF":8.7,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145546961","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2025-09-30DOI: 10.1109/TSMC.2025.3613057
Moatsum Alawida
As an integral application within the aerospace and electronic engineering systems, the aviation system relies heavily on secure communication protocols to safeguard sensitive information exchanged during travel and operations. However, many existing communication systems lack adequate security, leaving them vulnerable to adversarial attacks and data exfiltration. While effective in many security protocols, classical encryption algorithms are susceptible to various attacks, including statistical, differential, and side-channel attacks. To address these vulnerabilities, a new cryptographic encryption algorithm is proposed based on the extended Feistel network structure and binary tree structure. This algorithm enhances security measures by segmenting plaintext into blocks of 1024 bits, further divided into left and right halves across four distinct levels. Encryption begins at Level 64, employing a summation-based algorithm and XOR operations to ensure diffusion and confusion properties. The parallel implementation of encryption enhances processing speed while deriving subkeys from a sensitive secret key enhances security against single-bit changes. Experimental assessments and security analyses demonstrate the robustness of the proposed cipher against various attacks. The proposed cipher offers high-quality encryption capabilities, making it an ideal candidate for inclusion in the secure communication protocols of aviation systems.
{"title":"A New Encryption Algorithm for Secure Aviation Communications","authors":"Moatsum Alawida","doi":"10.1109/TSMC.2025.3613057","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3613057","url":null,"abstract":"As an integral application within the aerospace and electronic engineering systems, the aviation system relies heavily on secure communication protocols to safeguard sensitive information exchanged during travel and operations. However, many existing communication systems lack adequate security, leaving them vulnerable to adversarial attacks and data exfiltration. While effective in many security protocols, classical encryption algorithms are susceptible to various attacks, including statistical, differential, and side-channel attacks. To address these vulnerabilities, a new cryptographic encryption algorithm is proposed based on the extended Feistel network structure and binary tree structure. This algorithm enhances security measures by segmenting plaintext into blocks of 1024 bits, further divided into left and right halves across four distinct levels. Encryption begins at Level 64, employing a summation-based algorithm and XOR operations to ensure diffusion and confusion properties. The parallel implementation of encryption enhances processing speed while deriving subkeys from a sensitive secret key enhances security against single-bit changes. Experimental assessments and security analyses demonstrate the robustness of the proposed cipher against various attacks. The proposed cipher offers high-quality encryption capabilities, making it an ideal candidate for inclusion in the secure communication protocols of aviation systems.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 12","pages":"9186-9200"},"PeriodicalIF":8.7,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145546996","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2025-09-29DOI: 10.1109/TSMC.2025.3612400
Shiyu Xie;Wei Sun;Yuqiang Wu
This study presents an advanced adaptive fuzzy optimal bridge-hole constraint control method for large-scale interconnected systems under quantized input. To address the conflict in constraint ranges caused by the combined effect of both results in the bridge-hole and performance constraints, a new prescribed time function with parameter requirements is proposed, which bridges the balance between them and keeps the tracking error within a desired zone in a prescribed time. Meanwhile, output constraint is realized by building a new bridge-hole constraint function, which ensures the time interval for the constraint behavior to occur by the flexible setting of the switching time. Unlike traditional optimal control schemes, the designed optimal controller is further quantized by a hysteresis quantizer, which minimizes energy cost and saves bandwidth. Besides, a reinforcement learning (RL) scheme based on an actor–critic-identifier fuzzy logic system (FLS) structure is designed; its overall control idea is to optimize the entire backstepping control system by using all virtual and actual backstepping control as the optimal solution of their respective subsystems. Finally, the effectiveness of the proposed scheme is confirmed by simulation experiments.
{"title":"Learning-Based Prescribed-Time Fuzzy Optimal Quantized Control for Large-Scale Systems With Bridge-Hole Constraint","authors":"Shiyu Xie;Wei Sun;Yuqiang Wu","doi":"10.1109/TSMC.2025.3612400","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3612400","url":null,"abstract":"This study presents an advanced adaptive fuzzy optimal bridge-hole constraint control method for large-scale interconnected systems under quantized input. To address the conflict in constraint ranges caused by the combined effect of both results in the bridge-hole and performance constraints, a new prescribed time function with parameter requirements is proposed, which bridges the balance between them and keeps the tracking error within a desired zone in a prescribed time. Meanwhile, output constraint is realized by building a new bridge-hole constraint function, which ensures the time interval for the constraint behavior to occur by the flexible setting of the switching time. Unlike traditional optimal control schemes, the designed optimal controller is further quantized by a hysteresis quantizer, which minimizes energy cost and saves bandwidth. Besides, a reinforcement learning (RL) scheme based on an actor–critic-identifier fuzzy logic system (FLS) structure is designed; its overall control idea is to optimize the entire backstepping control system by using all virtual and actual backstepping control as the optimal solution of their respective subsystems. Finally, the effectiveness of the proposed scheme is confirmed by simulation experiments.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 12","pages":"9097-9108"},"PeriodicalIF":8.7,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145546958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Accidents involving escalators in mass rapid transit (MRT) systems pose a serious risk to public safety, often resulting from clothing or footwear getting caught, or large items toppling during movement. Despite the availability of passive warnings, such as signage and audio announcements, these methods often go unnoticed by commuters and lack the ability to adapt to real-time risks. Existing computer vision solutions are either too computationally intensive for deployment on edge devices or lack sufficient accuracy for practical use. To address these challenges, this study proposes a real-time, lightweight object detection system using a pruned YOLOv7-Tiny model, optimized for deployment on the NVIDIA Jetson Nano edge computing platform. The system is designed to identify safety-critical items, such as general footwear, high heels, long skirts, suitcases, strollers, and shopping trolleys, in real-time. Upon detection, it issues visual and auditory alerts, and in cases involving large items, sends email notifications to station personnel. Model pruning significantly reduces computational overhead while maintaining high accuracy. Experimental results demonstrate that the system achieves a mean average precision (mAP) of 94.69%, outperforming conventional detection models while maintaining real-time performance. These results highlight the system’s potential for enhancing passenger safety and operational efficiency in resource-constrained public transit environments.
{"title":"A Deep Multiobject Detection Model for Passenger Escalator Safety","authors":"Yo-Ping Huang;Satchidanand Kshetrimayum;Haobijam Basanta;Frode Eika Sandnes","doi":"10.1109/TSMC.2025.3612901","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3612901","url":null,"abstract":"Accidents involving escalators in mass rapid transit (MRT) systems pose a serious risk to public safety, often resulting from clothing or footwear getting caught, or large items toppling during movement. Despite the availability of passive warnings, such as signage and audio announcements, these methods often go unnoticed by commuters and lack the ability to adapt to real-time risks. Existing computer vision solutions are either too computationally intensive for deployment on edge devices or lack sufficient accuracy for practical use. To address these challenges, this study proposes a real-time, lightweight object detection system using a pruned YOLOv7-Tiny model, optimized for deployment on the NVIDIA Jetson Nano edge computing platform. The system is designed to identify safety-critical items, such as general footwear, high heels, long skirts, suitcases, strollers, and shopping trolleys, in real-time. Upon detection, it issues visual and auditory alerts, and in cases involving large items, sends email notifications to station personnel. Model pruning significantly reduces computational overhead while maintaining high accuracy. Experimental results demonstrate that the system achieves a mean average precision (mAP) of 94.69%, outperforming conventional detection models while maintaining real-time performance. These results highlight the system’s potential for enhancing passenger safety and operational efficiency in resource-constrained public transit environments.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 12","pages":"9109-9119"},"PeriodicalIF":8.7,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145546976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The dynamic and unknown human behaviors in human–robot interaction make it challenging for collision-free robot manipulation. Although sampling-based model predictive control (MPC) has achieved real-time control in the above scenarios, it is hard to handle equality hard constraints, such as working along a specified trajectory, due to sampling disturbances. To improve manipulation performance under multiple constraints, this article presents a novel constrained sampling-based MPC (CSMPC) method using path integral. First, hierarchical optimization combining policy sampling projection and the Lagrange multiplier method is used to handle equality hard constraints for high-precision manipulation tasks. Second, collision avoidance and smooth motion are modeled as inequality soft constraints, where collision detection and time series prediction are used to ensure the safety and smoothness of dynamic interaction. Finally, an adaptive noise method is built to improve the stability of physical robot manipulation. The simulation and experiment results demonstrate that the proposed method enables a 7-DOF robot manipulator to achieve precise manipulation while avoiding dynamic obstacles.
{"title":"Constrained Sampling-Based MPC Using Path Integral for Collision-Free Robot Manipulation","authors":"Xingfang Wang;Hui Li;Dong Wang;Xiao Huang;Zhihong Jiang","doi":"10.1109/TSMC.2025.3611922","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3611922","url":null,"abstract":"The dynamic and unknown human behaviors in human–robot interaction make it challenging for collision-free robot manipulation. Although sampling-based model predictive control (MPC) has achieved real-time control in the above scenarios, it is hard to handle equality hard constraints, such as working along a specified trajectory, due to sampling disturbances. To improve manipulation performance under multiple constraints, this article presents a novel constrained sampling-based MPC (CSMPC) method using path integral. First, hierarchical optimization combining policy sampling projection and the Lagrange multiplier method is used to handle equality hard constraints for high-precision manipulation tasks. Second, collision avoidance and smooth motion are modeled as inequality soft constraints, where collision detection and time series prediction are used to ensure the safety and smoothness of dynamic interaction. Finally, an adaptive noise method is built to improve the stability of physical robot manipulation. The simulation and experiment results demonstrate that the proposed method enables a 7-DOF robot manipulator to achieve precise manipulation while avoiding dynamic obstacles.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 11","pages":"8701-8714"},"PeriodicalIF":8.7,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145335306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Human–machine hybrid reconfiguration manufacturing is an emerging paradigm in the field of precision equipment production and can greatly improve the production capability of the workshop. However, numerous complex constraints and a dynamic environment make reasonable scheduling very difficult. To this end, this article studies the dynamic human–machine hybrid reconfiguration manufacturing scheduling problem (DHMRSP) and proposes a novel deep reinforcement learning (DRL) scheduling method. Specifically, a dual-agent Markov decision process (MDP) is established, which can handle seven complex constraints and three disturbance events. Then, a heterogeneous competition graph attention network (HCGAN) is designed, where the meta-path-based subgraph conversion reflects the resource-operation competition, and three modules use node-level attention and semantic-level attention to realize important information embedding. Afterward, a dual proximal policy optimization (PPO) algorithm with HCGAN and mixed action space (HM-DPPO) is proposed, where the allocation agent and reconfiguration agent achieve collaborative learning by taking joint action and sharing graph embeddings and reward. Experimental results prove that the proposed approach outperforms rules, genetic programming (GP), and three DRL methods on different instances and can effectively handle various disturbance events.
{"title":"Graph-Based Dual-Agent Deep Reinforcement Learning for Dynamic Human–Machine Hybrid Reconfiguration Manufacturing Scheduling","authors":"Yuxin Li;Qihao Liu;Chunjiang Zhang;Xinyu Li;Liang Gao","doi":"10.1109/TSMC.2025.3612300","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3612300","url":null,"abstract":"Human–machine hybrid reconfiguration manufacturing is an emerging paradigm in the field of precision equipment production and can greatly improve the production capability of the workshop. However, numerous complex constraints and a dynamic environment make reasonable scheduling very difficult. To this end, this article studies the dynamic human–machine hybrid reconfiguration manufacturing scheduling problem (DHMRSP) and proposes a novel deep reinforcement learning (DRL) scheduling method. Specifically, a dual-agent Markov decision process (MDP) is established, which can handle seven complex constraints and three disturbance events. Then, a heterogeneous competition graph attention network (HCGAN) is designed, where the meta-path-based subgraph conversion reflects the resource-operation competition, and three modules use node-level attention and semantic-level attention to realize important information embedding. Afterward, a dual proximal policy optimization (PPO) algorithm with HCGAN and mixed action space (HM-DPPO) is proposed, where the allocation agent and reconfiguration agent achieve collaborative learning by taking joint action and sharing graph embeddings and reward. Experimental results prove that the proposed approach outperforms rules, genetic programming (GP), and three DRL methods on different instances and can effectively handle various disturbance events.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 11","pages":"8729-8741"},"PeriodicalIF":8.7,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145335326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study focuses on addressing the challenge of global output feedback control problem for a class of time-varying stochastic nonlinear systems subject to multiple uncertainties. The primary challenge concerns how to construct time-varying functions to counteract the effects of unmeasurable error coming from system output as well as the persistently increasing nonlinearities. By employing a full-order state observer and the dual gain approach, we design an output feedback regulator over the entire time domain to guarantee the existence and uniqueness of the closed-loop system’s solution and the almost sure asymptotic convergence of the state. This methodology achieves both the domination of the unknown growth rate and the unified system design, irrespective of sensor sensitivity. Finally, practical and numerical simulation examples demonstrate the feasibility of the presented approach.
{"title":"Global Regulation of Time-Varying Stochastic Nonlinear Systems via Output Feedback and Its Application in One-Link Manipulator","authors":"Xian-Long Yin;Zong-Yao Sun;Changyun Wen;Chih-Chiang Chen","doi":"10.1109/TSMC.2025.3611915","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3611915","url":null,"abstract":"This study focuses on addressing the challenge of global output feedback control problem for a class of time-varying stochastic nonlinear systems subject to multiple uncertainties. The primary challenge concerns how to construct time-varying functions to counteract the effects of unmeasurable error coming from system output as well as the persistently increasing nonlinearities. By employing a full-order state observer and the dual gain approach, we design an output feedback regulator over the entire time domain to guarantee the existence and uniqueness of the closed-loop system’s solution and the almost sure asymptotic convergence of the state. This methodology achieves both the domination of the unknown growth rate and the unified system design, irrespective of sensor sensitivity. Finally, practical and numerical simulation examples demonstrate the feasibility of the presented approach.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 11","pages":"8755-8766"},"PeriodicalIF":8.7,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145335307","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2025-09-24DOI: 10.1109/TSMC.2025.3611824
Xingchen Shao;Lipo Mo;Xiangpeng Xie
This article investigates the stabilization problem of fuzzy Markov jump systems (F-MJSs) with incomplete transition probability (TP) information. Existing methods for handling partially unknown TPs often introduce excessive conservatism using scaling techniques that may violate the fundamental stochastic constraints. To address this issue, we propose a novel polynomial-based gain-scheduling control framework that integrates a polytopic probability reconstruction strategy. This strategy rigorously preserves the stochastic completeness of TP matrices (TPMs) while reducing conservatism in controller design. By leveraging homogeneous polynomial theory, we further establish a codesign methodology for both polynomial Lyapunov functions and fuzzy controllers, significantly expanding the feasible solution space. Theoretical analysis demonstrates that the proposed method achieves substantially reduced conservatism compared with conventional aggregated approximation approaches. Numerical simulations reveal the improvement compared with classical aggregated treatment approaches. Hardware-in-the-loop (HIL) experiments on active suspension systems validate the effectiveness and robustness of the designed control strategy, especially $gamma _{mathrm { min}}$ achieved a reduction optimization of 87.5%.
{"title":"Polynomial-Based Gain-Scheduling Mechanism of Fuzzy Markov Jump System With Incomplete Transition Probability Information With Experimental Validation","authors":"Xingchen Shao;Lipo Mo;Xiangpeng Xie","doi":"10.1109/TSMC.2025.3611824","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3611824","url":null,"abstract":"This article investigates the stabilization problem of fuzzy Markov jump systems (F-MJSs) with incomplete transition probability (TP) information. Existing methods for handling partially unknown TPs often introduce excessive conservatism using scaling techniques that may violate the fundamental stochastic constraints. To address this issue, we propose a novel polynomial-based gain-scheduling control framework that integrates a polytopic probability reconstruction strategy. This strategy rigorously preserves the stochastic completeness of TP matrices (TPMs) while reducing conservatism in controller design. By leveraging homogeneous polynomial theory, we further establish a codesign methodology for both polynomial Lyapunov functions and fuzzy controllers, significantly expanding the feasible solution space. Theoretical analysis demonstrates that the proposed method achieves substantially reduced conservatism compared with conventional aggregated approximation approaches. Numerical simulations reveal the improvement compared with classical aggregated treatment approaches. Hardware-in-the-loop (HIL) experiments on active suspension systems validate the effectiveness and robustness of the designed control strategy, especially <inline-formula> <tex-math>$gamma _{mathrm { min}}$ </tex-math></inline-formula> achieved a reduction optimization of 87.5%.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 11","pages":"8742-8754"},"PeriodicalIF":8.7,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145335257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2025-09-24DOI: 10.1109/TSMC.2025.3608211
Nguyen Huu Sau;Mai Viet Thuan
This study addresses the stabilization of discrete-time singular systems with delays by employing an event-triggered control (ETC) method. In particular, we propose an innovative triggering mechanism that compares the measurement-error coordinates with the system state coordinates, thereby preserving system positivity even under time delays. A novel algorithm is introduced, which relies on comparing the coordinates of measurement errors and system states to maintain the positivity of the system. This article establishes sufficient conditions to ensure that the closed-loop system remains regular, causal, positive, and exponentially stable, building upon this newly formulated triggering approach and leveraging advanced matrix properties such as nonnegative matrices. To illustrate the efficacy and nontrivial nature of these conditions, we provide an algorithmic diagram and a diverse set of examples, including both simulations and a practical case study. The ETC mechanism, characterized by the sequence of event occurrences, demonstrates substantial nontrivial properties. These conditions are easily verifiable using MATLAB tools. This article also includes a range of examples, featuring both numerical simulations and a practical case study, to validate the effectiveness of the theoretical findings.
{"title":"Event-Triggered Control for Linear Positive Discrete-Time Singular Systems With Time Delay","authors":"Nguyen Huu Sau;Mai Viet Thuan","doi":"10.1109/TSMC.2025.3608211","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3608211","url":null,"abstract":"This study addresses the stabilization of discrete-time singular systems with delays by employing an event-triggered control (ETC) method. In particular, we propose an innovative triggering mechanism that compares the measurement-error coordinates with the system state coordinates, thereby preserving system positivity even under time delays. A novel algorithm is introduced, which relies on comparing the coordinates of measurement errors and system states to maintain the positivity of the system. This article establishes sufficient conditions to ensure that the closed-loop system remains regular, causal, positive, and exponentially stable, building upon this newly formulated triggering approach and leveraging advanced matrix properties such as nonnegative matrices. To illustrate the efficacy and nontrivial nature of these conditions, we provide an algorithmic diagram and a diverse set of examples, including both simulations and a practical case study. The ETC mechanism, characterized by the sequence of event occurrences, demonstrates substantial nontrivial properties. These conditions are easily verifiable using MATLAB tools. This article also includes a range of examples, featuring both numerical simulations and a practical case study, to validate the effectiveness of the theoretical findings.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 11","pages":"8625-8637"},"PeriodicalIF":8.7,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145335280","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In dynamic time-linkage optimization, current decisions influence the future state of environments. To make good decisions that have a positive impact on future states, existing methods usually build a model to predict the future rewards of solutions for decision making. However, these prediction models present low accuracy since decision data are not enough to train such a complex model. To address this issue, this article proposes a contrastive-learning-based decision making (CLDM) method, which builds a contrastive model to learn the relationship between solutions but not absolute rewards and adopts a quick decision strategy to select solutions. In CLDM, a clustering-based time-linkage detection (CD) strategy is developed to measure the intensity of the time linkage, which determines whether to make decisions based on future rewards. To represent the relative relationship between solutions, a large number of contrastive samples are constructed using the limited historical decisions. A contrastive model is trained for solution comparison in terms of the combination of current fitness and future rewards. Candidate solutions are clustered into multiple groups to filter poor ones, and a few solutions are preserved to rank using the contrastive model. The winner is taken as the decision solution. Integrating CLDM into particle swarm optimization (PSO), a new algorithm named contrastive-learning-based PSO (CL-PSO) is put forward. Experimental results on multiple dynamic time-linkage optimization instances demonstrate that CL-PSO outperforms state-of-the-art algorithms in terms of solution quality. CL-PSO can also well solve the mobile robot path planning problem.
{"title":"Contrastive-Learning-Based Decision Making for Dynamic Time-Linkage Optimization","authors":"Xiao-Fang Liu;Meng Gao;Yongchun Fang;Zhi-Hui Zhan;Jun Zhang","doi":"10.1109/TSMC.2025.3611797","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3611797","url":null,"abstract":"In dynamic time-linkage optimization, current decisions influence the future state of environments. To make good decisions that have a positive impact on future states, existing methods usually build a model to predict the future rewards of solutions for decision making. However, these prediction models present low accuracy since decision data are not enough to train such a complex model. To address this issue, this article proposes a contrastive-learning-based decision making (CLDM) method, which builds a contrastive model to learn the relationship between solutions but not absolute rewards and adopts a quick decision strategy to select solutions. In CLDM, a clustering-based time-linkage detection (CD) strategy is developed to measure the intensity of the time linkage, which determines whether to make decisions based on future rewards. To represent the relative relationship between solutions, a large number of contrastive samples are constructed using the limited historical decisions. A contrastive model is trained for solution comparison in terms of the combination of current fitness and future rewards. Candidate solutions are clustered into multiple groups to filter poor ones, and a few solutions are preserved to rank using the contrastive model. The winner is taken as the decision solution. Integrating CLDM into particle swarm optimization (PSO), a new algorithm named contrastive-learning-based PSO (CL-PSO) is put forward. Experimental results on multiple dynamic time-linkage optimization instances demonstrate that CL-PSO outperforms state-of-the-art algorithms in terms of solution quality. CL-PSO can also well solve the mobile robot path planning problem.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 11","pages":"8661-8674"},"PeriodicalIF":8.7,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145335292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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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