Journal of The Franklin Institute-engineering and Applied Mathematics最新文献

{"title":"Event-triggered fuzzy intermittent control for nonlinear DDPS with unknown spatiotemporal disturbances","authors":"Yufan Wang ,&nbsp;Huiyan Zhang ,&nbsp;Yongchao Liu ,&nbsp;Ning Zhao","doi":"10.1016/j.jfranklin.2025.108121","DOIUrl":"10.1016/j.jfranklin.2025.108121","url":null,"abstract":"<div><div>This paper focuses on designing an anti-disturbance fuzzy intermittent control scheme for nonlinear delayed distributed parameter system (DDPS) subject to unknown spatiotemporal disturbances. Specifically, the paper models the nonlinear DDPS using the Takagi-Sugeno fuzzy model. Then a disturbance observer is employed to accurately estimate external disturbances. In light of the system’s incomplete observability, a system observer is introduced. To alleviate network communication burdens and conserve resources, distinct event-triggered mechanisms (ETMs) are designed for the outputs of both observers. Furthermore, based on the states at the triggering instants of the two observers, a fuzzy intermittent control strategy is proposed. Moreover, a novel Lyapunov-Krasovskii functional is formulated to analyze the system’s stability, and design conditions for controller and observer gains are provided using linear matrix inequalities. The paper also proves that the designed ETMs avoid Zeno behavior, ensuring practical feasibility. Finally, simulations validate the proposed method’s effectiveness.</div></div>","PeriodicalId":17283,"journal":{"name":"Journal of The Franklin Institute-engineering and Applied Mathematics","volume":"362 17","pages":"Article 108121"},"PeriodicalIF":4.2,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145271387","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}

{"title":"Adaptive fixed time tracking performance control of stochastic nonlinearly parameterized systems with unknown trajectory","authors":"Yanli Liu ,&nbsp;Yihua Sun ,&nbsp;Li-Ying Hao ,&nbsp;Hongjun Ma","doi":"10.1016/j.jfranklin.2025.108122","DOIUrl":"10.1016/j.jfranklin.2025.108122","url":null,"abstract":"<div><div>A fixed-time tracking performance control strategy is devised for stochastic nonlinear systems with tracking error constraints to come true the tracking control under the uncertain desired trajectory. Primarily, bonding the Fourier series and the radial basis function neural network (RBFNN), the unknown ideal tracking target is reconstructed, and the controller is designed on this basis. Subsequently, a modified error transformation technique with the fixed-time performance function (FPF) is formulated to dispose of the tracking error constraint. Then, by introducing an improved first-order filter, the problem of differential explosion is avoided. Moreover, the Lyapunov stability theorem proves that the whole system states remain bounded and the tracking error is kept in the preset limits in fixed time despite of the ideal trajectory is unknown. Definitively, the simulation example is designed to confirm the feasibility.</div></div>","PeriodicalId":17283,"journal":{"name":"Journal of The Franklin Institute-engineering and Applied Mathematics","volume":"362 17","pages":"Article 108122"},"PeriodicalIF":4.2,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145247943","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}

{"title":"Block-column iterative bundle adjustment for large-scale 3D reconstruction","authors":"Shangzuo Xie,&nbsp;Gangrong Qu,&nbsp;Wenli Wang","doi":"10.1016/j.jfranklin.2025.108119","DOIUrl":"10.1016/j.jfranklin.2025.108119","url":null,"abstract":"<div><div>Large-scale Bundle Adjustment (BA) poses a significant computational challenge in 3D reconstruction and Structure from Motion (SfM). Traditional BA solvers, such as the Levenberg-Marquardt (LM) and Dogleg (DL) algorithms, become increasingly time-consuming as the scale of the Reduced Camera System (RCS) grows. In this work, we present a novel algorithm designed to address the complexities of large-scale BA. Our method utilizes block-column iterations to exploit the problem’s sparse structure, improving solver efficiency. Experimental validation on the Bundle Adjustment in the Large (BAL) dataset shows that our approach outperforms conventional iterative methods, significantly accelerating the solution of the normal equations and speeding up large-scale 3D reconstruction. Furthermore, our algorithm can be seamlessly integrated as a sub-problem solver within the Stochastic Bundle Adjustment (STBA) framework, enhancing both the speed and accuracy of distributed optimization.</div></div>","PeriodicalId":17283,"journal":{"name":"Journal of The Franklin Institute-engineering and Applied Mathematics","volume":"362 16","pages":"Article 108119"},"PeriodicalIF":4.2,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145269330","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}

{"title":"Adaptive RBFNN-based fixed-time event-triggered control of a class of disturbed Euler-Lagrange systems with actuator faults","authors":"Yuhan Hou ,&nbsp;Xiaozheng Jin ,&nbsp;Jiahu Qin","doi":"10.1016/j.jfranklin.2025.108116","DOIUrl":"10.1016/j.jfranklin.2025.108116","url":null,"abstract":"<div><div>This paper studies the radial basis function neural network (RBFNN)-based fixed-time event-triggered tracking control of a class of Euler-Lagrange (EL) systems with external disturbance, model uncertainties, and bias-actuator faults. A uniform robust exact differentiator (URED) is developed to estimate the accurate states of the EL systems within a constrained time. An adaptive RBFNN-based event-triggered control scheme based on the estimation signals are presented to inhibit the effects of the disturbances, uncertainties, and actuator faults. This scheme utilizes the minimum learning parameter (MLP) technique to significantly reduce both the update frequency of RBFNN parameters and the computational load, and also integrates the event-triggered mechanism to minimize controller updates, so that the resource utilization efficiency is enhanced. The stability of the EL error system under time constraints is proved using Lyapunov stability theory. Finally, simulations of a robotic manipulator system are demonstrated to display the effectiveness and superiority of the designed robust adaptive RBFNN-based fixed-time event-triggered fault-tolerant control strategy.</div></div>","PeriodicalId":17283,"journal":{"name":"Journal of The Franklin Institute-engineering and Applied Mathematics","volume":"362 16","pages":"Article 108116"},"PeriodicalIF":4.2,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145269332","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}

{"title":"On the sum and sum-of-ratio of α−μ distributions and application","authors":"Faissal El Bouanani","doi":"10.1016/j.jfranklin.2025.108106","DOIUrl":"10.1016/j.jfranklin.2025.108106","url":null,"abstract":"<div><div>This research presents closed-form statistical functions for three sums and sum-of-ratios of correlated or uncorrelated <span><math><mrow><mi>α</mi><mo>/</mo><mn>2</mn><mo>−</mo><mi>μ</mi></mrow></math></span> RVs. We evaluated the upper incomplete moment-generating function (UIMGF) and moment-generating function (MGF) of two sum-ratio distributions. The study offers an Erratum of [7] by extending the MGF and CDF of the sum <span><math><mrow><mi>α</mi><mo>−</mo><mi>μ</mi></mrow></math></span> RVs to the unanalyzed case <span><math><mrow><mi>α</mi><mo>&gt;</mo><mo>=</mo><mn>2</mn></mrow></math></span>. Using statistical features of signal-to-noise ratio (SNR) and signal-to-interference-to-noise ratio (SINR) at legitimate and wiretapper receiver outputs, we investigated the secrecy outage probability of two RF wireless communication schemes subject to <span><math><mrow><mi>α</mi><mo>−</mo><mi>μ</mi></mrow></math></span> fading and channel state information. Both receivers are equipped with multiple antennas and use the maximal-ratio combining diversity technique. Additionally, artificial noise is supplied from the primary source or a separate jammer transmitter. Using SINR’s UIMGF and MGF, closed-form secrecy outage probability (SOP) and intercept probability (IP) formulas were derived. The monotonicity of all analytical results vs. system settings has been thoroughly investigated. From a security perspective, the first scenario works better with high artificial noise power. Nevertheless, the output SNR of the legitimate end-user combiner decreases dramatically, reducing channel capacity and the average error rate. The framework might be utilized in full-duplex cooperative or multi-user interference systems. Monte Carlo simulation supports all analytical findings in all scenarios.</div></div>","PeriodicalId":17283,"journal":{"name":"Journal of The Franklin Institute-engineering and Applied Mathematics","volume":"362 16","pages":"Article 108106"},"PeriodicalIF":4.2,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145268625","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}

{"title":"Hierarchical event-triggered consensus control for multi-agent systems under external disturbances: A propagation pre-prevention strategy","authors":"Yunan Qu,&nbsp;Xiaoli Luan,&nbsp;Haiying Wan,&nbsp;Fei Liu","doi":"10.1016/j.jfranklin.2025.108113","DOIUrl":"10.1016/j.jfranklin.2025.108113","url":null,"abstract":"<div><div>This paper proposes a hierarchical event-triggered consensus control method for multi-agent systems (MASs) under a directed topology with external disturbances. The proposed approach adopts a two-layer structure that decouples inter-agent cooperation from individual regulation: the upper layer designs a virtual reference system to enable cooperation, while the lower layer focuses on developing tracking controllers for individual agents. This two-layer structure effectively isolates external disturbances from the communication among agents. Thus, it can “pre-prevent” the effects of external disturbances on the cooperation process compared to the most commonly used distributed control methods for solving disturbances. Moreover, continuous communication between agents can be eliminated by the developed dynamic event-triggered mechanism. It is proved that the consensus error converges to zero asymptotically under a strongly connected topology and a more general topology containing a spanning tree. Finally, the effectiveness of the proposed control method is illustrated by numerical simulations.</div></div>","PeriodicalId":17283,"journal":{"name":"Journal of The Franklin Institute-engineering and Applied Mathematics","volume":"362 16","pages":"Article 108113"},"PeriodicalIF":4.2,"publicationDate":"2025-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145268629","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}

{"title":"Approximation of RCI set contained within a 2-norm ball or an ellipsoid","authors":"Longfei Yang,&nbsp;Hongli Yang","doi":"10.1016/j.jfranklin.2025.108088","DOIUrl":"10.1016/j.jfranklin.2025.108088","url":null,"abstract":"<div><div>In this paper, we establish both a necessary and sufficient condition, as well as a sufficient condition, for non-empty sets to be contained within a 2-norm ball or an origin-centered ellipsoid. When the constraint sets of a discrete-time linear time-invariant system subjected to bounded disturbances are either 2-norm balls or ellipsoids, we formulate a series of linear programming problems using the proposed conditions to compute the robust control invariant (RCI) sets under such constraint sets. Finally, we design an algorithm to compute the minimal-volume outer approximation of the minimal robust control invariant (mRCI) set within a fixed tolerance. Numerical examples validate the effectiveness of the proposed results.</div></div>","PeriodicalId":17283,"journal":{"name":"Journal of The Franklin Institute-engineering and Applied Mathematics","volume":"362 17","pages":"Article 108088"},"PeriodicalIF":4.2,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145271388","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}

{"title":"Physics-shielded deep reinforcement learning for safe energy management of microgrids with battery health consideration","authors":"Jinlong Yang ,&nbsp;Shuwang Du ,&nbsp;Pengcheng Chen ,&nbsp;Shichao Liu ,&nbsp;Bo Chen","doi":"10.1016/j.jfranklin.2025.108112","DOIUrl":"10.1016/j.jfranklin.2025.108112","url":null,"abstract":"<div><div>While deep reinforcement learning (DRL) algorithms have shown promise in solving the energy management problem in the microgrid (MG), the operational safety of electrical components involved in energy management is often ignored. In this work, a safe DRL-based energy management algorithm is proposed to achieve the supply-demand balance under the high penetration level of wind energy, by optimally coordinating adjustable loads, battery energy storage systems (BESS), and interactions with the main grid. Uniquely, an adaptive physics-shielded mechanism is integrated into the twin delayed deep deterministic policy gradient (TD3) algorithm to enhance the safe operation of the BESS and thus extend its life span. In particular, the state of charge (SoC) is sustained at a safe operating range via the proposed physics-shielded mechanism, and the threshold of the safe range is dynamically adjusted in view of the state of health (SoH). The proposed approach considers both calendar aging and cycling aging to enhance the long-term performance of BESS. Comparisons in real-world dataset show the proposed method can dynamically prevent the SoC of batteries from exceeding the thresholds and substantially extend battery lifespan, in the presence of sharp wind power fluctuation.</div></div>","PeriodicalId":17283,"journal":{"name":"Journal of The Franklin Institute-engineering and Applied Mathematics","volume":"362 16","pages":"Article 108112"},"PeriodicalIF":4.2,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145269328","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}

{"title":"A practical type-3 fuzzy vibration control for high-speed trains subjected to unknown nonlinear dynamics","authors":"Chunwei Zhang ,&nbsp;Changdong Du ,&nbsp;Ardashir Mohammadzadeh","doi":"10.1016/j.jfranklin.2025.108115","DOIUrl":"10.1016/j.jfranklin.2025.108115","url":null,"abstract":"<div><div>Vibration control of high-speed trains (HSTs) is a challenging problem due to complex dynamics, a high-level uncertain operating environment, and time-varying rotational dynamics. Also, controlling vibrations while maintaining stability at high speeds requires advanced controllers and engineering solutions. However, the most traditional controllers include dampers and linear controllers. A few studies have considered HST’s nonlinear dynamics and stability concerns. This paper introduces a new effective idea based on the Active Rotary Inertia Driver (ARID) system. The controlled ARID system ensures stability by actively manipulating rotational inertia through a mass disk. The designed controller is applied for a case-study plant, and the whole closed-loop dynamics are considered to be unknown. Considering the nonlinear complexity of HST motion, adaptive type-3 fuzzy logic systems (T3-FLSs) are developed for modeling. The proposed controller autonomously identifies closed-loop dynamics, independent of predefined ARID equations and HST’s models. Both control gain and nonlinearities are estimated using adaptive T3-FLSs. The T3-FLSs are updated online using Lyapunov adaptation rules. A compensatory parallel controller is also designed to counteract disturbances and T3-FLS estimation errors. The efficacy of the designed approach is validated through comprehensive experiments and simulations under various operational conditions. The overall performance is analyzed in terms of cost, time, frequency domain metrics, and energy efficiency.</div></div>","PeriodicalId":17283,"journal":{"name":"Journal of The Franklin Institute-engineering and Applied Mathematics","volume":"362 16","pages":"Article 108115"},"PeriodicalIF":4.2,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222534","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}

{"title":"A data-ensemble-based approach for sample-efficient LQ control of linear time-varying systems","authors":"Sahel Vahedi Noori ,&nbsp;Maryam Babazadeh","doi":"10.1016/j.jfranklin.2025.108118","DOIUrl":"10.1016/j.jfranklin.2025.108118","url":null,"abstract":"<div><div>This paper presents a sample-efficient, data-driven control framework for finite-horizon linear quadratic (LQ) control of linear time-varying (LTV) systems. In contrast to the time-invariant case, the time-varying LQ problem involves a differential Riccati equation (DRE) with time-dependent parameters and terminal boundary constraints, complicating data-driven control. Additionally, the time-varying dynamics invalidate the use of the Fundamental Lemma. To overcome these challenges, we formulate the LQ problem as a nonconvex optimization problem and conduct a rigorous analysis of its dual structure. By exploiting the inherent convexity of the dual problem and analyzing the KKT conditions, we derive an explicit relationship between the optimal dual solution and the parameters of the associated Q-function in time-varying case. This theoretical insight supports the development of a novel, sample-efficient, non-iterative semidefinite programming (SDP) algorithm that directly computes the optimal sequence of feedback gains from an ensemble of input-state data sequences without requiring model identification or a stabilizing controller. The resulting convex, data-dependent framework provides global optimality guarantees for completely unknown LTV systems. As a special case, the method also applies to finite-horizon LQ control of linear time-invariant (LTI) systems. In this setting, a single input-state trajectory suffices to identify the optimal LQ feedback policy, improving significantly over existing Q-learning approaches for finite horizon LTI systems that typically require data from multiple episodes. The approach provides a new optimization-based perspective on Q-learning in time-varying settings and contributes to the broader understanding of data-driven control in non-stationary environments. Simulation results show that, compared to recent methods, the proposed approach achieves superior optimality and sample efficiency on LTV systems, and indicates potential for stabilizing and optimal control of nonlinear systems.</div></div>","PeriodicalId":17283,"journal":{"name":"Journal of The Franklin Institute-engineering and Applied Mathematics","volume":"362 16","pages":"Article 108118"},"PeriodicalIF":4.2,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145269334","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}