Pub Date : 2026-01-08DOI: 10.1016/j.jfranklin.2025.108391
Shouting Hong , Haoyue Yang , Tarek Raïssi , Junfeng Zhang
This paper investigates the dual-synchronization problem of fuzzy positive Markovian jump complex networks with dynamic links based on double observations. A class of fuzzy positive Markovian jump complex networks is established by introducing dynamic links between nodes. Subsequently, a controller and the corresponding link coupling term are designed to achieve the positive and synchronous of the node systems and the link systems. Then, a synchronization strategy is proposed based on the observations of node and link states. The main contributions are as follows: (i) A dual-synchronization framework is constructed for both nodes and links by designing the controller and the coupling term, (ii) A synchronization control strategy is proposed based on double observers of node subsystems and link subsystems, and (iii) A manageable approach is developed for design and analysis by employing linear programming and constructing co-positive Lyapunov functions. Finally, the effectiveness and feasibility of the proposed approaches are illustrated via simulation examples.
{"title":"Double observers-based node and link synchronization of fuzzy Markovian jump positive complex networks","authors":"Shouting Hong , Haoyue Yang , Tarek Raïssi , Junfeng Zhang","doi":"10.1016/j.jfranklin.2025.108391","DOIUrl":"10.1016/j.jfranklin.2025.108391","url":null,"abstract":"<div><div>This paper investigates the dual-synchronization problem of fuzzy positive Markovian jump complex networks with dynamic links based on double observations. A class of fuzzy positive Markovian jump complex networks is established by introducing dynamic links between nodes. Subsequently, a controller and the corresponding link coupling term are designed to achieve the positive and synchronous of the node systems and the link systems. Then, a synchronization strategy is proposed based on the observations of node and link states. The main contributions are as follows: (i) A dual-synchronization framework is constructed for both nodes and links by designing the controller and the coupling term, (ii) A synchronization control strategy is proposed based on double observers of node subsystems and link subsystems, and (iii) A manageable approach is developed for design and analysis by employing linear programming and constructing co-positive Lyapunov functions. Finally, the effectiveness and feasibility of the proposed approaches are illustrated via simulation examples.</div></div>","PeriodicalId":17283,"journal":{"name":"Journal of The Franklin Institute-engineering and Applied Mathematics","volume":"363 3","pages":"Article 108391"},"PeriodicalIF":4.2,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145980926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Model Predictive Control (MPC) has become a powerful framework for optimizing system performance under constraints. However, when applied to nonlinear systems subject to unknown but bounded uncertainties (UBB), conventional MPC approaches face significant challenges related to computational complexity and robustness. This paper proposes a robust tube-based economic model predictive control (REMPC) method using a linear parameter-varying (LPV) approach for nonlinear systems with unknown but bounded uncertainty, based on nominal predictions. Using a nonlinear embedding approach, the nonlinear model is transformed into an LPV model. The optimal states and inputs found from solving the previous optimization problem are used to estimate the scheduling variables along the prediction horizon while executing the receding horizon strategy. This approach converts the nonlinear optimization problem into a quadratic optimization problem, effectively reducing computational time by leveraging the efficiency inherent in the LPV formulation. Recursive feasibility and input-to-state stability are guaranteed. Recursive feasibility is ensured by tighter constraints, which are computed online using a zonotopic approach based on the disturbance reachable sets. A gain-scheduling H∞ controller is employed as the local controller to further tighten these constraints. The stability of the proposed approach is ensured by forcing the terminal state to converge towards the optimal equilibrium or working point of the system. Moreover, the terminal constraint is relaxed by using a constraint set around the terminal state instead of a constraint value and adding a penalty on the terminal state in the cost function. Additionally, strict dissipativity is established as a sufficient condition to prove stability. Finally, the effectiveness of the LPV-based REMPC strategy is demonstrated by controlling an isothermal Continuous Stirred Tank Reactor (CSTR), and an REMPC-LPV-based planning approach for a 1/10 scale autonomous remote-controlled (RC) electric car is also tested through simulations.
{"title":"Robust tube-based economic model predictive control of nonlinear systems using linear parameter varying approach","authors":"Heithem Boufrioua , Boubekeur Boukhezzar , Vicenç Puig","doi":"10.1016/j.jfranklin.2026.108409","DOIUrl":"10.1016/j.jfranklin.2026.108409","url":null,"abstract":"<div><div>Model Predictive Control (MPC) has become a powerful framework for optimizing system performance under constraints. However, when applied to nonlinear systems subject to unknown but bounded uncertainties (UBB), conventional MPC approaches face significant challenges related to computational complexity and robustness. This paper proposes a robust tube-based economic model predictive control (REMPC) method using a linear parameter-varying (LPV) approach for nonlinear systems with unknown but bounded uncertainty, based on nominal predictions. Using a nonlinear embedding approach, the nonlinear model is transformed into an LPV model. The optimal states and inputs found from solving the previous optimization problem are used to estimate the scheduling variables along the prediction horizon while executing the receding horizon strategy. This approach converts the nonlinear optimization problem into a quadratic optimization problem, effectively reducing computational time by leveraging the efficiency inherent in the LPV formulation. Recursive feasibility and input-to-state stability are guaranteed. Recursive feasibility is ensured by tighter constraints, which are computed online using a zonotopic approach based on the disturbance reachable sets. A gain-scheduling <em>H</em><sub>∞</sub> controller is employed as the local controller to further tighten these constraints. The stability of the proposed approach is ensured by forcing the terminal state to converge towards the optimal equilibrium or working point of the system. Moreover, the terminal constraint is relaxed by using a constraint set around the terminal state instead of a constraint value and adding a penalty on the terminal state in the cost function. Additionally, strict dissipativity is established as a sufficient condition to prove stability. Finally, the effectiveness of the LPV-based REMPC strategy is demonstrated by controlling an isothermal Continuous Stirred Tank Reactor (CSTR), and an REMPC-LPV-based planning approach for a 1/10 scale autonomous remote-controlled (RC) electric car is also tested through simulations.</div></div>","PeriodicalId":17283,"journal":{"name":"Journal of The Franklin Institute-engineering and Applied Mathematics","volume":"363 3","pages":"Article 108409"},"PeriodicalIF":4.2,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145980931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2026-01-08DOI: 10.1016/j.jfranklin.2026.108402
Jianfeng Guo , Wei Qian , Wudi Li
This paper addresses the problem of event-triggered containment control for linear multi-agent systems. Initially, a state observer is designed to estimate the unmeasurable states of follower agents. Subsequently, a novel sampled-data-based hybrid error-driven adaptive dynamic event-triggered mechanism is proposed utilizing the periodically sampled observed states. This mechanism integrates state measurement errors, containment control errors, along with containment measurement errors, and employs dynamic auxiliary variables as well as adaptive threshold parameters that dynamically adjust in real time to meet the system’s performance requirements. By leveraging the observed states at triggering instants, a containment control protocol is further developed. Furthermore, a composite closed-loop error system, which includes containment errors, measurement errors, and observation errors, is constructed through model transformation. Consequently, the containment control problem is equivalently reformulated as an asymptotic stability analysis for a time-delayed error system. Sufficient conditions for containment control are derived from a stability analysis of the error system driven by the Lyapunov functional approach. A co-design method for controller gain, observer gain, and event-triggered parameters is then developed to ensure system performance. Finally, simulation results from two illustrative examples validate the effectiveness and superiority of the proposed event-triggered mechanism and containment control protocol.
{"title":"Improved sampled-data-based adaptive dynamic event-triggered containment control of multi-agent systems","authors":"Jianfeng Guo , Wei Qian , Wudi Li","doi":"10.1016/j.jfranklin.2026.108402","DOIUrl":"10.1016/j.jfranklin.2026.108402","url":null,"abstract":"<div><div>This paper addresses the problem of event-triggered containment control for linear multi-agent systems. Initially, a state observer is designed to estimate the unmeasurable states of follower agents. Subsequently, a novel sampled-data-based hybrid error-driven adaptive dynamic event-triggered mechanism is proposed utilizing the periodically sampled observed states. This mechanism integrates state measurement errors, containment control errors, along with containment measurement errors, and employs dynamic auxiliary variables as well as adaptive threshold parameters that dynamically adjust in real time to meet the system’s performance requirements. By leveraging the observed states at triggering instants, a containment control protocol is further developed. Furthermore, a composite closed-loop error system, which includes containment errors, measurement errors, and observation errors, is constructed through model transformation. Consequently, the containment control problem is equivalently reformulated as an asymptotic stability analysis for a time-delayed error system. Sufficient conditions for containment control are derived from a stability analysis of the error system driven by the Lyapunov functional approach. A co-design method for controller gain, observer gain, and event-triggered parameters is then developed to ensure system performance. Finally, simulation results from two illustrative examples validate the effectiveness and superiority of the proposed event-triggered mechanism and containment control protocol.</div></div>","PeriodicalId":17283,"journal":{"name":"Journal of The Franklin Institute-engineering and Applied Mathematics","volume":"363 3","pages":"Article 108402"},"PeriodicalIF":4.2,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145980923","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2026-01-08DOI: 10.1016/j.jfranklin.2026.108410
Wei Zhang , Yu Zhang , Tianhao Su , Yao Li
This paper mainly studies the model predictive control problem with probabilistic bit flips under constrained bitrate limitations. Since the bitrate required for data transmission often exceeds the maximum bitrate that the system can provide, the transmitted data needs to be quantized to meet the bitrate requirements. The quantized data is encoded into binary data streams for long-distance transmission. However, due to external interferences such as channel noise, certain bits in the binary data streams may flip between 0 and 1, causing flip errors that affect subsequent control.Therefore, this paper quantizes data into specific intervals through a uniform quantizer and establishes a mathematical model of probabilistic bit flips by incorporating the description of bit probability flips using Bernoulli distributions. By combining it with polyhedral uncertain systems, the impacts of quantization and bit flips are transformed into system uncertainties, which are then uniformly handled in model predictive control. Considering the difficulty of obtaining system states in practical scenarios, a dynamic output feedback control framework under robust model predictive control is proposed. Singular value decomposition technology is used to address the non-convexity in the system, and solvable auxiliary optimization problems are proposed. Additionally, sufficient criteria for the mean-square stability of system states are provided. Finally, the effectiveness of the proposed method is verified through two simulation cases.
{"title":"Robust model predictive control with probabilistic bit flips under constrained bit rate","authors":"Wei Zhang , Yu Zhang , Tianhao Su , Yao Li","doi":"10.1016/j.jfranklin.2026.108410","DOIUrl":"10.1016/j.jfranklin.2026.108410","url":null,"abstract":"<div><div>This paper mainly studies the model predictive control problem with probabilistic bit flips under constrained bitrate limitations. Since the bitrate required for data transmission often exceeds the maximum bitrate that the system can provide, the transmitted data needs to be quantized to meet the bitrate requirements. The quantized data is encoded into binary data streams for long-distance transmission. However, due to external interferences such as channel noise, certain bits in the binary data streams may flip between 0 and 1, causing flip errors that affect subsequent control.Therefore, this paper quantizes data into specific intervals through a uniform quantizer and establishes a mathematical model of probabilistic bit flips by incorporating the description of bit probability flips using Bernoulli distributions. By combining it with polyhedral uncertain systems, the impacts of quantization and bit flips are transformed into system uncertainties, which are then uniformly handled in model predictive control. Considering the difficulty of obtaining system states in practical scenarios, a dynamic output feedback control framework under robust model predictive control is proposed. Singular value decomposition technology is used to address the non-convexity in the system, and solvable auxiliary optimization problems are proposed. Additionally, sufficient criteria for the mean-square stability of system states are provided. Finally, the effectiveness of the proposed method is verified through two simulation cases.</div></div>","PeriodicalId":17283,"journal":{"name":"Journal of The Franklin Institute-engineering and Applied Mathematics","volume":"363 3","pages":"Article 108410"},"PeriodicalIF":4.2,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145980929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2026-01-08DOI: 10.1016/j.jfranklin.2025.108387
Zhaoxia Duan , Yuyan Fu , Choon Ki Ahn , Zhengrong Xiang
This paper aims to design a fault detection (FD) filter for continuous two-dimensional (2-D) Markov jump positive systems (MJPSs) with constant state delays that ensures the stochastic stability and L1/ performances of the filtering augmented system. The L1-gain and performances of the delayed continuous 2-D MJPSs are investigated, and their exact values can be calculated. Necessary and sufficient conditions for ensuring the L1-gain performance and index of the system are derived. On this basis, sufficient conditions for the existence of the mixed FD filter are achieved and then are solved via an iterative algorithm. Finally, a numerical example validates the preceding theoretical findings.
{"title":"L1/L− fault detection filtering for delayed 2-D continuous positive Markov jump systems","authors":"Zhaoxia Duan , Yuyan Fu , Choon Ki Ahn , Zhengrong Xiang","doi":"10.1016/j.jfranklin.2025.108387","DOIUrl":"10.1016/j.jfranklin.2025.108387","url":null,"abstract":"<div><div>This paper aims to design a fault detection (FD) filter for continuous two-dimensional (2-D) Markov jump positive systems (MJPSs) with constant state delays that ensures the stochastic stability and <em>L</em><sub>1</sub>/<span><math><msub><mi>L</mi><mo>−</mo></msub></math></span> performances of the filtering augmented system. The <em>L</em><sub>1</sub>-gain and <span><math><msub><mi>L</mi><mo>−</mo></msub></math></span> performances of the delayed continuous 2-D MJPSs are investigated, and their exact values can be calculated. Necessary and sufficient conditions for ensuring the <em>L</em><sub>1</sub>-gain performance and <span><math><msub><mi>L</mi><mo>−</mo></msub></math></span> index of the system are derived. On this basis, sufficient conditions for the existence of the mixed <span><math><mrow><msub><mi>L</mi><mn>1</mn></msub><mo>/</mo><msub><mi>L</mi><mo>−</mo></msub></mrow></math></span> FD filter are achieved and then are solved via an iterative algorithm. Finally, a numerical example validates the preceding theoretical findings.</div></div>","PeriodicalId":17283,"journal":{"name":"Journal of The Franklin Institute-engineering and Applied Mathematics","volume":"363 3","pages":"Article 108387"},"PeriodicalIF":4.2,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145980924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2026-01-08DOI: 10.1016/j.jfranklin.2026.108411
Bin Lu , Lili Li , Jinqi Liu , Xiaowei Zhao
Deception attacks in switched systems can manipulate both the switching signal and system state, inducing severe asynchronous switching. While existing research predominantly employs passive mitigation strategies, this paper proposes a proactive secure control framework integrating prediction into attack detection. A model predictive control-based attack detection mechanism, augmented by a mode-state predictor, detects attacks by comparing predicted and received system modes and states. A detection-aware event-triggered mechanism and a mode-based try-once-discard protocol are implemented to reduce asynchrony between the subsystem and the controller. Additionally, an optimal subsystem switching rule, derived from the optimal states, ensures stability and security during switching. Asymptotic stability of the closed-loop system is analytically verified, and its feasibility is validated through an unmanned surface vehicle case study.
{"title":"Predictive-Deception-Attack-Detection-Based secure control for switched systems","authors":"Bin Lu , Lili Li , Jinqi Liu , Xiaowei Zhao","doi":"10.1016/j.jfranklin.2026.108411","DOIUrl":"10.1016/j.jfranklin.2026.108411","url":null,"abstract":"<div><div>Deception attacks in switched systems can manipulate both the switching signal and system state, inducing severe asynchronous switching. While existing research predominantly employs passive mitigation strategies, this paper proposes a proactive secure control framework integrating prediction into attack detection. A model predictive control-based attack detection mechanism, augmented by a mode-state predictor, detects attacks by comparing predicted and received system modes and states. A detection-aware event-triggered mechanism and a mode-based try-once-discard protocol are implemented to reduce asynchrony between the subsystem and the controller. Additionally, an optimal subsystem switching rule, derived from the optimal states, ensures stability and security during switching. Asymptotic stability of the closed-loop system is analytically verified, and its feasibility is validated through an unmanned surface vehicle case study.</div></div>","PeriodicalId":17283,"journal":{"name":"Journal of The Franklin Institute-engineering and Applied Mathematics","volume":"363 3","pages":"Article 108411"},"PeriodicalIF":4.2,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145980928","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2026-01-06DOI: 10.1016/j.jfranklin.2026.108405
Yanming Liang , Yongfeng Guo , Qingzeng Song
Abnormal directional propagation of neural signals, as observed in epilepsy, cannot be fully captured by traditional single layer neuronal models driven by Gaussian noise. Departing from prior studies that emphasized Gaussian perturbations or single layer topologies, this work investigates how localized non-Gaussian Lévy noise influences cross-layer synchronization and directional information flow in a multilayer neuronal network. We construct a two-layer FitzHugh-Nagumo (FHN) system with non-local coupling in which only the first layer is exposed to Lévy noise, thereby mimicking focal pathological discharges and enabling the study of interlayer transmission through diffusive coupling. Using transfer entropy (TE) as a directional measure of information flow, we systematically analyze how the noise intensity, stability index, and skewness regulate interlayer communication and synchronization dynamics. The results show that Lévy noise not only induces chimera and solitary states but also drives symmetry breaking in interlayer information flow, with the noise driven layer exerting the dominant regulatory influence. The stability index organizes transitions among synchronized, chimera, and desynchronized regimes, whereas skewness modulates the prevailing direction of information transfer. Notably, directional TE remains elevated even under global desynchronization, indicating persistent causal influence in pathological conditions. These findings reveal a noise induced mechanism for asymmetric information transfer and provide a physiologically grounded framework for modeling epileptic brain dynamics.
{"title":"Directional Information Flow and Chimera States in a Multi-layer FitzHugh–Nagumo Neuronal Network Excited by Local Lévy Noise","authors":"Yanming Liang , Yongfeng Guo , Qingzeng Song","doi":"10.1016/j.jfranklin.2026.108405","DOIUrl":"10.1016/j.jfranklin.2026.108405","url":null,"abstract":"<div><div>Abnormal directional propagation of neural signals, as observed in epilepsy, cannot be fully captured by traditional single layer neuronal models driven by Gaussian noise. Departing from prior studies that emphasized Gaussian perturbations or single layer topologies, this work investigates how localized non-Gaussian Lévy noise influences cross-layer synchronization and directional information flow in a multilayer neuronal network. We construct a two-layer FitzHugh-Nagumo (FHN) system with non-local coupling in which only the first layer is exposed to Lévy noise, thereby mimicking focal pathological discharges and enabling the study of interlayer transmission through diffusive coupling. Using transfer entropy (TE) as a directional measure of information flow, we systematically analyze how the noise intensity, stability index, and skewness regulate interlayer communication and synchronization dynamics. The results show that Lévy noise not only induces chimera and solitary states but also drives symmetry breaking in interlayer information flow, with the noise driven layer exerting the dominant regulatory influence. The stability index organizes transitions among synchronized, chimera, and desynchronized regimes, whereas skewness modulates the prevailing direction of information transfer. Notably, directional TE remains elevated even under global desynchronization, indicating persistent causal influence in pathological conditions. These findings reveal a noise induced mechanism for asymmetric information transfer and provide a physiologically grounded framework for modeling epileptic brain dynamics.</div></div>","PeriodicalId":17283,"journal":{"name":"Journal of The Franklin Institute-engineering and Applied Mathematics","volume":"363 4","pages":"Article 108405"},"PeriodicalIF":4.2,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146170836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2026-01-05DOI: 10.1016/j.jfranklin.2025.108400
Vipin Kumar, Roberto Guglielmi
This paper explores the concept of exponential synchronization in neutral-type neural networks with mixed delays over arbitrary time domains. We employ a state feedback controller and formulate the problem using the time scales approach, allowing us to address hybrid time domains that include both continuous and discrete-time domains as a special case. Our approach relies on a combination of time scale calculus and the Banach fixed-point theorem, and leads to less restrictive assumptions compared to other techniques. Importantly, the synchronization criterion derived through this approach reduces to a simple, easy-to-verify linear scalar inequality. Furthermore, we present various special cases of the system under consideration and engage in a comprehensive discussion to highlight the advantages of our findings compared to existing results. We validate the effectiveness of our results through simulated numerical examples over different time domains, including an application to secure communication.
{"title":"Exponential synchronization of neutral-type neural networks with leakage and mixed delays on time scales","authors":"Vipin Kumar, Roberto Guglielmi","doi":"10.1016/j.jfranklin.2025.108400","DOIUrl":"10.1016/j.jfranklin.2025.108400","url":null,"abstract":"<div><div>This paper explores the concept of exponential synchronization in neutral-type neural networks with mixed delays over arbitrary time domains. We employ a state feedback controller and formulate the problem using the time scales approach, allowing us to address hybrid time domains that include both continuous and discrete-time domains as a special case. Our approach relies on a combination of time scale calculus and the Banach fixed-point theorem, and leads to less restrictive assumptions compared to other techniques. Importantly, the synchronization criterion derived through this approach reduces to a simple, easy-to-verify linear scalar inequality. Furthermore, we present various special cases of the system under consideration and engage in a comprehensive discussion to highlight the advantages of our findings compared to existing results. We validate the effectiveness of our results through simulated numerical examples over different time domains, including an application to secure communication.</div></div>","PeriodicalId":17283,"journal":{"name":"Journal of The Franklin Institute-engineering and Applied Mathematics","volume":"363 3","pages":"Article 108400"},"PeriodicalIF":4.2,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145981011","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2026-01-05DOI: 10.1016/j.jfranklin.2025.108389
Fang Han , Xiaosheng Zhou , Ming Chi
This study investigates the impact of various networking elements and queue management systems on the performance of Network Control Systems (NCSs) within the framework of nonlinear dynamics and complex systems. By focusing on the performance limitations imposed by White Gaussian Noise (WGN) in both forward and feedback pathways, as well as the influence of codec and queuing systems in feedback channels, we explore the intricate interplay between network-induced constraints and the nonlinear dynamics of NCSs. Utilizing queuing models, we analyze how queuing architectures affect the efficiency of NCSs under limited data availability, emphasizing the emergent behaviors characteristic of complex systems. Through internal and external factorization, along with selective disintegration methods and spectral partitioning techniques, we derive the performance expression of NCSs. Our findings reveal that the system’s effectiveness is governed by its intrinsic nonlinear dynamics, such as the presence of unstable points, Non-Minimum Phase (NMP) points, and their spatial orientation, as well as by network-specific factors, including codec types and WGN. These results underscore the complex, interdependent nature of NCSs as nonlinear dynamical systems operating within networked environments. Finally, the theoretical insights are validated through three distinct simulation experiments, demonstrating the robustness and applicability of our approach in real-world scenarios.
{"title":"Best performance study of nonlinear dynamics systems with network analysis over multiple communication constraints","authors":"Fang Han , Xiaosheng Zhou , Ming Chi","doi":"10.1016/j.jfranklin.2025.108389","DOIUrl":"10.1016/j.jfranklin.2025.108389","url":null,"abstract":"<div><div>This study investigates the impact of various networking elements and queue management systems on the performance of <em>Network Control Systems</em> (NCSs) within the framework of nonlinear dynamics and complex systems. By focusing on the performance limitations imposed by <em>White Gaussian Noise</em> (WGN) in both forward and feedback pathways, as well as the influence of codec and queuing systems in feedback channels, we explore the intricate interplay between network-induced constraints and the nonlinear dynamics of NCSs. Utilizing queuing models, we analyze how queuing architectures affect the efficiency of NCSs under limited data availability, emphasizing the emergent behaviors characteristic of complex systems. Through internal and external factorization, along with selective disintegration methods and spectral partitioning techniques, we derive the performance expression of NCSs. Our findings reveal that the system’s effectiveness is governed by its intrinsic nonlinear dynamics, such as the presence of unstable points, <em>Non-Minimum Phase</em> (NMP) points, and their spatial orientation, as well as by network-specific factors, including codec types and WGN. These results underscore the complex, interdependent nature of NCSs as nonlinear dynamical systems operating within networked environments. Finally, the theoretical insights are validated through three distinct simulation experiments, demonstrating the robustness and applicability of our approach in real-world scenarios.</div></div>","PeriodicalId":17283,"journal":{"name":"Journal of The Franklin Institute-engineering and Applied Mathematics","volume":"363 4","pages":"Article 108389"},"PeriodicalIF":4.2,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146025302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2026-01-05DOI: 10.1016/j.jfranklin.2025.108392
Shuo Wang, Shuaiming Yan, Lei Shi, Panpan Zhu
Flocking aims to induce collective aggregation through complex interactions among interconnected agents. Given the pervasive and intricate co-opetitive dynamics in multi-agent systems, realizing flocking behavior in such networks presents both practical relevance and significant challenges. This paper investigates the flocking dynamic behavior of multi-agent systems with cooperative and competitive relationships under asynchronous communication. Building on the classical Cucker-Smale (C-S) model, a distributed control protocol with asynchronous communication is designed. In this protocol, the timing of communication is determined by each agent individually, rather than being updated synchronously by a unified clock. The protocol quantifies the intensity of cooperation and competition by introducing bio-inspired nonlinear positive/negative weight functions related to interaction distances. The convergence of the dynamic model is rigorously verified through mathematical analysis using products of super-stochastic matrices, establishing an algebraic relationship between the degrees of cooperation and competition that ensures emergent flocking behavior. Finally, numerical simulations validate the effectiveness of the proposed algebraic conditions in achieving flocking behavior.
群集的目的是通过相互连接的主体之间复杂的相互作用诱导集体聚集。考虑到多智能体系统中普遍而复杂的合作竞争动态，在这种网络中实现群集行为既有现实意义，也有重大挑战。研究了异步通信条件下具有合作和竞争关系的多智能体系统的群集动态行为。在经典cucker - small （C-S）模型的基础上，设计了一种异步通信的分布式控制协议。在该协议中，通信的时间由每个代理单独决定，而不是由统一的时钟同步更新。该方案通过引入与相互作用距离相关的生物启发的非线性正/负权重函数来量化合作和竞争的强度。利用超随机矩阵的乘积，通过数学分析严格验证了动态模型的收敛性，建立了保证紧急群集行为的合作度与竞争度之间的代数关系。最后，数值模拟验证了所提出的代数条件在实现群集行为方面的有效性。
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