Results in Control and Optimization最新文献_第3页

Optimal control for resource allocation in a multi-patch epidemic model with gravity model-based human dispersal behavior 基于重力模型的人类扩散行为多斑块流行病模型中资源分配的最优控制

IF 3.2 Q3 Mathematics

Results in Control and Optimization

Pub Date : 2025-12-16 DOI: 10.1016/j.rico.2025.100648

A.S.K. Dinasiri , A.U.S. Adikari , H.C.Y. Jayathunga , I.T.S. Piyatilake

Mathematical models translate real-world problems into a structured framework, which makes it easier to investigate and analyze. Multi-patch compartmental models are used to model real-world scenarios related to epidemiology. Optimal control theory is used in this model to identify cost-effective strategies to minimize the proportion of individuals infected with COVID-19 in Sri Lanka. Since a nine-patch SIR-type model is considered in this research, human dispersal behaviors play a vital role. However, due to the lack of mobility data in Sri Lanka, a gravity model approach with a modified gravity model which models the human dispersal behaviors within and between patches is used to incorporate the human dispersal behaviors into the nine-patch SIR-type model. Then, the country is divided into three clusters using K-means clustering, based on the peak number of infections in each province without any control measures, for better representation. When using the control measure effective reproduction number

(R_{t})

represents the spread of the disease with sensitivity with the current susceptible population. It is observed that, in the absence of controls,

R_{t}

decreases from 1.55 to 1.30 within 400 days, and that it decreases from 1.57 to 0 within 20 days in the presence of controls. Control measures such as health measures and vaccination can control the disease within 40, 30–40, and 20–30 days in high-risk, moderate, and low-risk regions, respectively. Furthermore, results suggest that vaccination is the most efficient control strategy since it minimizes disturbing the lives of the general community rather than public health measures.

数学模型将现实世界的问题转化为一个结构化的框架，这使得它更容易调查和分析。多斑块区隔模型用于模拟与流行病学相关的现实世界情景。在该模型中使用最优控制理论来确定成本效益策略，以最大限度地减少斯里兰卡感染COVID-19的个体比例。由于本研究考虑的是一个9块sir型模型，因此人类的扩散行为起着至关重要的作用。然而，由于斯里兰卡缺乏流动性数据，我们采用了一个修正的重力模型方法来模拟人类在斑块内和斑块间的扩散行为，将人类的扩散行为纳入到9个斑块的sir型模型中。然后，根据没有任何控制措施的每个省的感染高峰数量，使用K-means聚类将该国分为三个聚类，以便更好地代表。当采用控制措施时，有效繁殖数（Rt）代表疾病在当前易感人群中的传播具有敏感性。可以观察到，在没有对照的情况下，Rt在400天内从1.55下降到1.30，在有对照的情况下，它在20天内从1.57下降到0。在高风险、中等和低风险地区，卫生措施和疫苗接种等控制措施可分别在40天、30-40天和20-30天内控制疾病。此外，结果表明，疫苗接种是最有效的控制策略，因为它最大限度地减少了对一般社区生活的干扰，而不是公共卫生措施。

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引用次数: 0

Improving diagnostic accuracy for PCOS: A hybrid machine learning architecture with feature selection, data balancing, and explainable AI techniques 提高PCOS的诊断准确性：一种混合机器学习架构，具有特征选择，数据平衡和可解释的AI技术

IF 3.2 Q3 Mathematics

Results in Control and Optimization

Pub Date : 2025-12-15 DOI: 10.1016/j.rico.2025.100647

Khandaker Mohammad Mohi Uddin , Abir Chowdhury , Md. Mahbubur Rahman Druvo , Mehreen Tabassum Jaima , Md. Tofael Ahmed Bhuiyan , Md. Manowarul Islam

Polycystic Ovary Syndrome (PCOS), which affects 5–10 % of women worldwide who are of reproductive age, is often misdiagnosed (∼70 %) despite the rising risks of metabolic disorders and infertility. Current machine learning diagnostics frequently struggle with unbalanced data and are not interpretable. This research improves PCOS diagnosis by introducing a new, interpretable hybrid architecture. We used mutual information and extra trees to improve feature selection and extensive preprocessing, including SMOTE for class imbalance, on a dataset of 541 patient records. A Soft Voting Ensemble that included Multilayer Perceptron (MLP) with CatBoost, optimized using GridSearchCV, and verified with 5-fold cross-validation, outperformed each individual model with previous research with a state-of-the-art accuracy of 96.88 %. Additionally, deep learning models performed well, most notably DANet (94.50 % accuracy). Importantly, SHAP and LIME improved model interpretability, offering clear insights into diagnostic judgments. The architecture was put into practice in an intuitive Flask web application for explainable, real-time forecasts. This study offers a therapeutically applicable method that strikes a balance between interpretability and high accuracy, enabling early PCOS identification and better patient outcomes. Multimodal integration and dataset extension are potential avenues for future study.

多囊卵巢综合征（PCOS）影响全世界5 - 10%的育龄妇女，尽管代谢紊乱和不孕症的风险不断上升，但仍经常被误诊（约70%）。当前的机器学习诊断经常与不平衡的数据作斗争，并且不可解释。本研究通过引入一种新的、可解释的混合架构来改善PCOS的诊断。我们使用互信息和额外的树来改进特征选择和广泛的预处理，包括针对类别不平衡的SMOTE，在541个患者记录的数据集上。包含多层感知器（MLP）和CatBoost的软投票集成，使用GridSearchCV进行优化，并经过5倍交叉验证，以96.88%的最先进精度优于先前研究的每个单独模型。此外，深度学习模型表现良好，最显著的是DANet（准确率为94.50%）。重要的是，SHAP和LIME提高了模型的可解释性，为诊断判断提供了清晰的见解。该架构在一个直观的Flask web应用程序中付诸实践，用于可解释的实时预测。本研究提供了一种治疗上适用的方法，在可解释性和高准确性之间取得平衡，使PCOS的早期识别和更好的患者预后成为可能。多模态集成和数据集扩展是未来研究的潜在途径。

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引用次数: 0

A stochastic differential model of energy-dependent neural stability 能量依赖神经稳定性的随机微分模型

IF 3.2 Q3 Mathematics

Results in Control and Optimization

Pub Date : 2025-12-15 DOI: 10.1016/j.rico.2025.100645

Zhiwei Xu , Qiang Sun , Zhenhui Liu , Qi Yang

We presented and analyzed a stochastic differential equation model of neural population dynamics leveraging synaptic plasticity energy regulation and noise-driven bifurcation. The model integrates the spike-timing-dependent plasticity and energy evolution equations modulated by a control parameter

λ

, which governs the tradeoff between metabolic expenditure and stochastic suppression. We derived the critical energy threshold

E_{crit}

which ensures stability against stochastic perturbations, and we formulated the cost function to determine the optimal

λ_{opt}

minimizing cost of balancing noise suppression and metabolic expenditure. Through numerical simulation on Ring, Erdős–Rényi, and Scale-Free network topologies, we demonstrate that the system consistently operates above

E_{crit}

, maintains stability via the Jacobian eigenvalue spectrum, and undergoes a bifurcation only beyond high noise amplitudes. Additional entropy and Lyapunov analyses reveal that the system supports high-dimensional, non-synchronous activity. Our results provide a quantitative framework for understanding how neural circuits achieve noise-robust computation under energetic constraints.

我们提出并分析了利用突触可塑性、能量调节和噪声驱动分岔的神经种群动态随机微分方程模型。该模型集成了峰值时间依赖的可塑性和能量演化方程，由控制参数λ调节，控制代谢消耗和随机抑制之间的权衡。我们推导出了保证稳定对抗随机扰动的临界能量阈值Ecrit，并制定了成本函数来确定平衡噪声抑制和代谢消耗的最优λopt最小成本。通过在Ring、Erdős-Rényi和Scale-Free网络拓扑上的数值模拟，我们证明了系统在Ecrit之上持续运行，通过雅可比特征值谱保持稳定性，并且只有在高噪声幅值之外才会发生分岔。附加熵和李亚普诺夫分析表明，该系统支持高维、非同步活动。我们的结果为理解神经回路如何在能量约束下实现噪声鲁棒计算提供了一个定量框架。

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引用次数: 0

Enhancing renewable energy utilization through solar, wind, and electric vehicles in the grid using a chaotic-based prairie dog optimization algorithm 利用基于混沌的草原土拨鼠优化算法，通过太阳能、风能和电动汽车在电网中提高可再生能源的利用率

IF 3.2 Q3 Mathematics

Results in Control and Optimization

Pub Date : 2025-12-14 DOI: 10.1016/j.rico.2025.100646

Ayan Das Sarkar , Provas Kumar Roy , Barun Mandal , Ghanshyam G. Tejani , Seyed Jalaleddin Mousavirad

This study aims to determine the most effective operational strategy for economic load dispatch (ELD). The practical ELD problem can be difficult to solve because of its non-smooth cost function and nonlinear constraints; specifically, electric power generation’s nonlinear multi-objective economic emission dispatch (EED) problem with valve point loading is solved using the prairie dog optimization algorithm-based optimization (PDO). The problem considers nonlinear generator aspects such as valve point effect, ramp rate limits, and restricted operating zones. The PDO technique identifies the optimal solution without requiring any prior knowledge of the gradient of the objective function. The application of the PDO algorithm to solve nonlinear ELD problems appears to be an effective and reliable optimization technique. Three test cases are considered. For example, 40 units with losses (thermal units and renewable energy), 140 units (renewable energy), and 160 units (thermal units and renewable energy) are used for executing and evaluating the suggested algorithm. The outcomes demonstrate the suggested algorithm’s potential and efficacy in comparison to other alternative techniques, and the suggested approach outperforms other established algorithms in terms of proficiency and robustness, as demonstrated by numerical data.

本研究旨在确定经济负荷调度（ELD）最有效的运行策略。实际的ELD问题由于其代价函数的非光滑性和约束条件的非线性而难以求解；具体地说，利用基于草原土拨鼠优化算法的优化（PDO），解决了具有阀点负荷的发电非线性多目标经济排放调度问题。该问题考虑了发电机的非线性方面，如阀点效应、斜坡速率限制和限制操作区域。PDO技术在不需要任何目标函数梯度的先验知识的情况下识别出最优解。应用PDO算法求解非线性电场问题是一种有效、可靠的优化方法。考虑三个测试用例。例如，使用40个具有损耗的单位（热单位和可再生能源）、140个单位（可再生能源）和160个单位（热单位和可再生能源）来执行和评估所建议的算法。与其他替代技术相比，结果证明了所建议的算法的潜力和有效性，并且所建议的方法在熟练度和鲁棒性方面优于其他已建立的算法，如数值数据所示。

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引用次数: 0

Controllability of stochastic multi-term fractional-order impulsive systems involving state delay 包含状态延迟的随机多项分数阶脉冲系统的可控性

IF 3.2 Q3 Mathematics

Results in Control and Optimization

Pub Date : 2025-12-13 DOI: 10.1016/j.rico.2025.100644

G. Arthi , M. Vaanmathi , R. Sivasangari , Yong-Ki Ma

This study investigates the damping behavior of impulsive fractional-order stochastic systems with state delays, which are essential for modeling dynamical processes exhibiting both memory and stochastic effects. The system is formulated using Caputo fractional derivatives and Mittag-Leffler functions, allowing for analytical expressions that accurately capture the hereditary properties of fractional dynamics. The main contribution of this work is the establishment of sufficient conditions for the controllability of both linear and nonlinear systems, achieved through a combination of stochastic analysis and fixed-point techniques, explicitly accounting for the influences of delays, damping, and impulses. The proposed methodology extends existing controllability results to a broader class of fractional stochastic systems and provides a systematic approach for analyzing their damping mechanisms. The theoretical findings are illustrated through a numerical example, confirming the accuracy and effectiveness of the developed methodology.

本文研究了具有状态延迟的脉冲分数阶随机系统的阻尼行为，这是建模具有记忆和随机效应的动态过程所必需的。该系统使用卡普托分数阶导数和Mittag-Leffler函数制定，允许准确捕获分数阶动力学遗传特性的解析表达式。这项工作的主要贡献是建立了线性和非线性系统的可控性的充分条件，通过随机分析和不动点技术的结合来实现，明确地考虑了延迟、阻尼和脉冲的影响。提出的方法将现有的可控性结果扩展到更广泛的分数阶随机系统，并为分析其阻尼机制提供了系统的方法。通过一个算例说明了理论结果，验证了所开发方法的准确性和有效性。

引用次数: 0

Midpoint-width lexicographic equilibria: Existence results for interval-valued equilibrium problems 中点宽度字典均衡：区间值均衡问题的存在性结果

IF 3.2 Q3 Mathematics

Results in Control and Optimization

Pub Date : 2025-12-11 DOI: 10.1016/j.rico.2025.100637

Somaye Jafari

This paper presents a new solution concept for interval-valued equilibrium problems using an appropriate interval ordering that considers both the central value and the uncertainty inherent in the data. The aim is to define solutions in a way that represents the imprecision frequently encountered in real-world situations. The proposed solution concept is then explained through a motivating example, demonstrating its advantages in handling interval-valued data. Furthermore, the study shows that the introduced interval-valued equilibrium problem can be reduced to a mixed equilibrium problem, for which existence results are established using a proof technique based on a KKM-type argument. A projection-based algorithm is also presented by adapting classical splitting methods for equilibrium problems to the proposed interval-valued equilibrium model. This work provides a rigorous and verifiable framework for addressing interval-valued equilibrium problems.

本文提出了一种新的区间值平衡问题的求解概念，采用适当的区间排序，同时考虑了中心值和数据中固有的不确定性。其目的是以一种表示在现实世界中经常遇到的不精确的方式来定义解决方案。然后通过一个激励示例解释了所提出的解决方案概念，展示了它在处理区间值数据方面的优势。进一步研究表明，所引入的区间值均衡问题可以化为混合均衡问题，并利用基于kkm型论证的证明技术建立了混合均衡问题的存在性结果。将经典的均衡问题分裂方法应用于所提出的区间值均衡模型，提出了一种基于投影的算法。这项工作为解决区间值均衡问题提供了一个严格和可验证的框架。

引用次数: 0

Dynamic Nash game for linear stochastic control with Markov Jump in Mpox Mpox中马尔可夫跳跃线性随机控制的动态纳什对策

IF 3.2 Q3 Mathematics

Results in Control and Optimization

Pub Date : 2025-12-10 DOI: 10.1016/j.rico.2025.100640

Md. Abdullah Bin Masud , Tanjina Tasnim , Mostak Ahmed , Md. Khalilur Rahman

Mpox, as a re-emerging infectious disease, poses considerable challenges due to uncertain transmission dynamics and sudden outbreak shocks, which cannot be adequately addressed by classical deterministic control models. To overcome these limitations, we develop a dynamic Nash game framework based on linear stochastic control with Markov jump disturbances. The framework integrates a controlled SEIR system in which regional decision-makers adopt strategies involving vaccination, social distancing, and awareness campaigns, interacting both competitively and cooperatively. By applying Pontryagin’s maximum principle, we derive the Hamiltonians and costate equations and obtain explicit formulations for both Nash equilibrium controls and team optimal controls. The stochastic SEIR model with Markov jumps is solved numerically using the Euler–Maruyama method. Simulation results indicate that Nash strategies significantly reduce infection prevalence compared to uncontrolled dynamics, yet they may produce unequal benefits across regions. Numerical simulations show that Nash controls reduce exposure and infection compared with uncontrolled dynamics, while coordinated team-optimal controls provide substantially greater reductions in outbreak magnitude and duration. This stochastic game-theoretic framework offers a robust extension of existing Mpox models by integrating Markov-jump uncertainty, multi-agent control, and analytically derived equilibrium strategies, providing practical insights for coordinated epidemic interventions.

麻疹作为一种重新出现的传染病，由于传播动力学的不确定性和突发的疫情冲击，带来了相当大的挑战，经典的确定性控制模型无法充分解决这一问题。为了克服这些限制，我们开发了一个基于线性随机控制和马尔可夫跳变扰动的动态纳什博弈框架。该框架整合了一个受控制的SEIR系统，在该系统中，区域决策者采取涉及疫苗接种、保持社会距离和提高认识运动的战略，通过竞争和合作相互作用。应用庞特里亚金极大值原理，导出了纳什均衡控制和团队最优控制的哈密顿量和协态方程，得到了两者的显式表达式。采用Euler-Maruyama方法对具有马尔可夫跳变的随机SEIR模型进行了数值求解。模拟结果表明，与不受控制的动态相比，纳什策略显著降低了感染率，但它们可能在不同地区产生不平等的效益。数值模拟表明，与不受控制的动态相比，纳什控制减少了暴露和感染，而协调的团队最优控制在爆发规模和持续时间方面提供了更大的减少。这个随机博弈论框架通过整合马尔可夫跳变不确定性、多智能体控制和解析导出的均衡策略，对现有的Mpox模型进行了鲁棒扩展，为协调流行病干预提供了实用的见解。

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引用次数: 0

A spectral collocation method via Krawtchouk polynomials for two-dimensional Liouville–Caputo fractional optimal control problems 二维Liouville-Caputo分数阶最优控制问题的Krawtchouk多项式谱配置方法

IF 3.2 Q3 Mathematics

Results in Control and Optimization

Pub Date : 2025-12-01 DOI: 10.1016/j.rico.2025.100639

Hajar Mohammadi, Habibollah Saeedi, Mohammad Izadi

This paper introduces an efficient spectral collocation method for solving two-dimensional fractional optimal control problems involving Liouville–Caputo derivatives. The proposed approach employs a Krawtchouk polynomials basis, taking advantage of its discrete orthogonality and strong localization properties. An operational matrix for the Riemann–Liouville fractional integral is developed, enabling accurate and efficient handling of the non-local memory effect. By approximating the state and control variables with Krawtchouk polynomials, the original problem is transformed into an algebraic system solved using Newton’s method. Theoretical convergence analysis and numerical stability tests confirm the method’s reliability. Numerical experiments demonstrate its high accuracy, computational efficiency, and robustness. These results indicate that the Krawtchouk polynomials framework is a promising tool for modeling and controlling complex fractional-order systems in science and engineering.

本文介绍了一种求解含Liouville-Caputo导数的二维分数阶最优控制问题的高效谱配置方法。该方法采用克rawtchouk多项式基，利用其离散正交性和强局域性。开发了Riemann-Liouville分数积分的运算矩阵，实现了对非局部记忆效应的准确有效处理。通过用克劳楚克多项式逼近状态变量和控制变量，将原问题转化为用牛顿法求解的代数系统。理论收敛分析和数值稳定性试验验证了该方法的可靠性。数值实验证明了该方法具有较高的精度、计算效率和鲁棒性。这些结果表明，Krawtchouk多项式框架是科学和工程中复杂分数阶系统建模和控制的一个很有前途的工具。

引用次数: 0

LQR, LQG, Extremum Seeking and Sliding Mode controls combined with Fuzzy for a rotary inverted pendulum stabilization LQR、LQG、极值搜索和滑模控制与模糊控制相结合用于旋转倒立摆稳定

IF 3.2 Q3 Mathematics

Results in Control and Optimization

Pub Date : 2025-12-01 DOI: 10.1016/j.rico.2025.100629

Erwin Susanto , Sony Sumaryo , Mohd Fadzil Hassan

This study considers the stabilization performance of a rotary type inverted pendulum (rotary inverted pendulum, RIP) using fuzzy control combined with linear quadratic regulator (LQR), linear quadratic Gaussian (LQG), extremum seeking and sliding mode controllers, respectively. Stability on the controlled systems is verified by a Lyapunov stability function. The performances of developed systems are visualized using Simscape Multibody in Matlab®’s environment. The control and statistical performances such as overshoot, root mean square, integral time absolute, integral square, and integral absolute errors are presented to show the success of the developed systems. This study mainly contributes to modeling the RIP with 3D visualization using Simscape Multibody to verify the success of the applied control schemes and to show the improvement of fuzzy control modified with some controls over the control techniques without fuzzy. In addition, the swing-up control scheme adopted the homoclinic orbit strategy and was presented via the hardware in the loop (HIL) mechanism.

本文分别采用线性二次型调节器（LQR）、线性二次型高斯调节器（LQG）、极值搜索控制器和滑模控制器相结合的模糊控制来研究旋转式倒立摆（rotary inverted pendulum， RIP）的镇定性能。通过李雅普诺夫稳定性函数验证了被控系统的稳定性。在Matlab®环境下使用Simscape Multibody对开发系统的性能进行可视化。系统的超调量、均方根误差、积分时间绝对误差、积分平方误差和积分绝对误差等控制性能和统计性能均显示了系统的成功。本研究主要是利用Simscape Multibody对RIP进行三维可视化建模，以验证所应用控制方案的成功，并展示通过一些控制改进的模糊控制对无模糊控制技术的改进。此外，摆动控制方案采用了同斜轨道策略，并通过硬件在环（HIL）机制进行了阐述。

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引用次数: 0

Input-constrained fixed-time fault-tolerant tracking control of autonomous wheeled mobile robot 自主轮式移动机器人的输入约束定时容错跟踪控制

IF 3.2 Q3 Mathematics

Results in Control and Optimization

Pub Date : 2025-12-01 DOI: 10.1016/j.rico.2025.100636

Naiemeh Ahmadlou , Mehdi Mirzaei, Sadra Rafatnia , Somayeh Jamshidi

This paper presents an active fault-tolerant control strategy for trajectory tracking in autonomous wheeled mobile robots, ensuring fixed-time convergence regardless of initial conditions, external perturbations and control input limitations. The approach combines optimization techniques with a continuous predictive control strategy to ensure rapid convergence to the desired path considering limited control inputs. The proposed fixed-time control law is derived by introducing a novel performance index, which is minimized within the framework of a constrained optimization problem solved using the Karush–Kuhn–Tucker conditions. The practically fixed-time stability of the tracking error is demonstrated through rigorous mathematical analysis. In the proposed scheme, an observer is developed to compensate for model perturbations, including environmental disturbances, modeling uncertainties, and actuator faults. When the estimated perturbations surpass predefined thresholds, potential actuators’ faults are detected. To ensure the robustness of the fault detection mechanism, threshold bounds for external disturbances and uncertainties are statistically determined using the Monte Carlo simulation approach. The results demonstrate the high sensitivity, accuracy, and robustness of the proposed method in fault detection for nonlinear uncertain wheeled robots, achieved within a simplified control architecture. The proposed constrained fault-tolerant control system, which achieves fixed-time tracking convergence, demonstrates superior performance over existing control methods across various scenarios.

针对自主轮式移动机器人的轨迹跟踪问题，提出了一种主动容错控制策略，该策略在不考虑初始条件、外部扰动和控制输入限制的情况下，保证了系统的固定时间收敛性。该方法将优化技术与连续预测控制策略相结合，以确保在控制输入有限的情况下快速收敛到期望路径。通过引入一种新的性能指标，推导出固定时间控制律，该指标在使用Karush-Kuhn-Tucker条件求解的约束优化问题框架内最小化。通过严格的数学分析，证明了跟踪误差的实际定时稳定性。在该方案中，开发了一个观测器来补偿模型扰动，包括环境扰动、建模不确定性和执行器故障。当估计的扰动超过预定义的阈值时，检测到潜在的执行器故障。为了保证故障检测机制的鲁棒性，采用蒙特卡罗模拟方法统计确定外部干扰和不确定性的阈值边界。结果表明，该方法在非线性不确定轮式机器人故障检测中具有较高的灵敏度、精度和鲁棒性，并能在简化的控制体系中实现。所提出的约束容错控制系统实现了固定时间跟踪收敛，在各种场景下都优于现有的控制方法。

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引用次数: 0