Results in Control and Optimization最新文献_第10页

Fuzzy PD control for a quadrotor with experimental results 四旋翼飞行器的模糊PD控制及实验结果

Q3 Mathematics

Results in Control and Optimization

Pub Date : 2025-06-01 Epub Date: 2025-04-30 DOI: 10.1016/j.rico.2025.100568

Anh T. Nguyen , Nam H. Nguyen , Mien L. Trinh

Quadrotor is an unmanned aerial vehicle widely used in traffic construction monitoring, volcano monitoring, forest fire, power line inspection, missing person search and disaster relief. The dynamic model of quadrotor becomes complex and non-linear due to four motors with four propellers to control and stabilize the motion. One disadvantage of the traditional PID controller is that its parameters are tuned based on trials and errors, but the fuzzy PID controller will automatically adjust its PID gains based on the IF-THEN rules and the parameters of the fuzzy systems are designed beforehand. For other adaptive fuzzy controllers, their parameters are online updated with large computational load. In this paper, we design an intelligent controller to manage the operating state of quadrotor (UAV) by combining the advantages of traditional PD controller with fuzzy logic inference systems to tune its parameters. These Fuzzy PD controllers performs control of the movement of the quadrotor along three axes to follow the desired trajectory. The proposed Fuzzy PD control system for the quadrotor is simulated and evaluated on Matlab-Simulink, then conducted with real-time experiments on QDrone2 physical system. Simulation and experimental results with comparisons to the PD controller have proven the effectiveness of the proposed control method with small tracking error under the impact of time-varying disturbance and additional load.

四旋翼无人机是一种广泛应用于交通建设监控、火山监测、森林火灾、电力线检查、失踪人员搜索和救灾等领域的无人机。四旋翼飞行器由于有四个电机和四个螺旋桨来控制和稳定运动，使其动力学模型变得复杂和非线性。传统PID控制器的一个缺点是其参数是基于试错调整的，而模糊PID控制器会根据IF-THEN规则自动调整PID增益，并且模糊系统的参数是事先设计好的。其他自适应模糊控制器的参数在线更新，计算量大。本文结合传统PD控制器和模糊逻辑推理系统的优点，设计了一种四旋翼无人机（UAV）运行状态的智能控制器。这些模糊PD控制器执行沿三轴四旋翼的运动控制，以遵循所需的轨迹。在Matlab-Simulink中对所提出的四旋翼模糊PD控制系统进行了仿真和评估，并在QDrone2物理系统上进行了实时实验。仿真和实验结果表明，该控制方法在时变扰动和附加负载的影响下具有较小的跟踪误差。

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引用次数: 0

Experimental realization of PSO-based hybrid adaptive sliding mode control for force impedance control systems 基于pso的力阻抗混合自适应滑模控制的实验实现

Q3 Mathematics

Results in Control and Optimization

Pub Date : 2025-06-01 Epub Date: 2025-03-22 DOI: 10.1016/j.rico.2025.100548

Sarucha Yanyong, Somyot Kaitwanidvilai

This paper presents a practical solution for an adaptive impedance force controller with online learning capabilities, designed to mitigate the effects of inaccuracies in system identification models. The proposed hybrid algorithm addresses the challenges associated with online learning in real-world machines. Additionally, the system demonstrates the ability to adapt to environmental changes, maintaining high-quality performance despite variations. A sliding surface guarantees system stability, while Particle Swarm Optimization (PSO) optimizes impedance parameters, reducing the risk of local minima. The hybrid algorithm also reduces overshoot and undershoot, resulting in faster system responses. Simulation and experimental results demonstrate that the proposed technique outperforms conventional force control systems in terms of learning ability and overall performance.

本文提出了一种具有在线学习能力的自适应阻抗力控制器的实用解决方案，旨在减轻系统识别模型中不准确性的影响。提出的混合算法解决了与现实世界机器在线学习相关的挑战。此外，该系统证明了适应环境变化的能力，在变化的情况下保持高质量的性能。滑动表面保证了系统的稳定性，而粒子群优化（PSO）优化了阻抗参数，降低了局部最小值的风险。混合算法还减少了超调和欠调，从而提高了系统响应速度。仿真和实验结果表明，该方法在学习能力和综合性能方面都优于传统的力控制系统。

引用次数: 0

Portfolio optimization with MOPSO-Shrinkage hybrid model 基于mopso -收缩混合模型的投资组合优化

Q3 Mathematics

Results in Control and Optimization

Pub Date : 2025-06-01 Epub Date: 2025-03-28 DOI: 10.1016/j.rico.2025.100553

Minh Tran, Nhat M. Nguyen

This paper introduces a novel framework for portfolio optimization that integrates Multi-Objective Particle Swarm Optimization (MOPSO) with shrinkage covariance estimators, referred to as the MOPSO-Shrinkage hybrid model. The main contribution of this study lies in combining the adaptive search capabilities of evolutionary algorithms with robust covariance estimation techniques to enhance portfolio allocation in mature financial markets. Unlike traditional shrinkage covariance models, which struggle in highly dynamic environments, our hybrid model optimally selects stocks and improves risk-adjusted returns. Empirical analysis on US stock market data from 2013 to 2023 demonstrates that MOPSO-Shrinkage models consistently outperform traditional shrinkage models, achieving higher returns, lower volatility, and superior Sharpe ratios. Among the hybrid models, MOPSO-SSIM exhibits the best performance, with an average annual return of 18.86% and a Sharpe ratio of 1.27, while significantly reducing portfolio risk. Rigorous statistical tests confirm the robustness of the model, showing that MOPSO-Shrinkage significantly outperforms traditional methods. These findings suggest that the proposed approach is well-suited for traders seeking higher risk-adjusted returns and portfolio stability in volatile markets.

本文提出了一种将多目标粒子群算法（MOPSO）与收缩协方差估计相结合的组合优化框架，称为MOPSO-收缩混合模型。本研究的主要贡献在于将进化算法的自适应搜索能力与稳健协方差估计技术相结合，以提高成熟金融市场的投资组合配置。与传统的收缩协方差模型不同，我们的混合模型在高度动态的环境中挣扎，优化选择股票并提高风险调整后的回报。对2013 - 2023年美国股市数据的实证分析表明，MOPSO-Shrinkage模型持续优于传统的收缩模型，实现了更高的收益、更低的波动性和更优的夏普比率。在混合模型中，MOPSO-SSIM表现最好，年均收益率为18.86%，夏普比率为1.27，同时显著降低了投资组合风险。严格的统计测试证实了模型的稳健性，表明mopso收缩显著优于传统方法。这些发现表明，所提出的方法非常适合交易者在波动的市场中寻求更高的风险调整回报和投资组合稳定性。

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引用次数: 0

A novel swarm intelligence-based approach for solving optimal power flow problems in modern power systems 基于群智能的现代电力系统最优潮流问题求解方法

Q3 Mathematics

Results in Control and Optimization

Pub Date : 2025-06-01 Epub Date: 2025-03-22 DOI: 10.1016/j.rico.2025.100555

Haewon Byeon , Wajdi Alghamdi , Munni Evin , M. Sucharitha , D. David Neels Ponkumar , A. Prakash , J. Sunil

Optimal power flow is the major concern with electric power systems. According to the OPF issue solution, the most suitable points are the compensator output, transformer tap, generator voltage, and generator output powers. In order to solve the optimum power flow difficulties related to the C-UPFC, this research suggested an Improved Pelican optimization algorithm (IPOA). Install the superior flexible AC transmission system (FACTS) in accordance with the transmission line (TL) in a series configuration to provide independent voltage control that combines power flow regulation. The suggested POA method ensures efficiency over the conventional IEEE 57 bus systems. Define the emission fuel cost and fuel cost during the C-UPFC installation. The suggested C-UPFC, when integrated optimally, considerably improves the voltage profile by decreasing power loss, as shown in the experiments. In addition, compared to all previous methods, the suggested algorithm produces superior outcomes, with the suggested method, you may expect to pay 202 tons per hour for emissions and 799.56 tons per hour for gasoline.

最优潮流是电力系统的主要问题。根据OPF问题的解决方案，最合适的点是补偿器输出、变压器抽头、发电机电压和发电机输出功率。为了解决与C-UPFC相关的最优潮流问题，本研究提出了一种改进的Pelican优化算法（IPOA）。根据传输线（TL）串联配置，安装高级柔性交流输电系统（FACTS），提供独立的电压控制，结合潮流调节。建议的POA方法确保了传统IEEE 57总线系统的效率。定义C-UPFC安装期间的排放燃料成本和燃料成本。如实验所示，所建议的C-UPFC在优化集成时，通过降低功率损耗大大改善了电压分布。此外，与之前的所有方法相比，建议的算法产生了更好的结果，使用建议的方法，您可能期望每小时支付202吨的排放量和799.56吨的汽油费用。

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引用次数: 0

Effect of fear in a fractional order prey–predator model with time delayed carrying capacity 具有时间延迟承载能力的分数阶捕食模型中恐惧的影响

Q3 Mathematics

Results in Control and Optimization

Pub Date : 2025-06-01 Epub Date: 2025-04-25 DOI: 10.1016/j.rico.2025.100567

Pramodh Bharati , Subrata Paul , Animesh Mahata , Supriya Mukherjee , Subhabrata Mondal , Banamali Roy

The Caputo technique is used in this article to analyze the fractional-order predator–prey scenario. Incorporating a delayed carrying capacity for the prey population and posing the impact of individual prey fear on predators are two aspects of this. We first provide the model’s formulation in terms of an integer order derivative, and subsequently we expand it to a fractional order system in terms of the Caputo derivative. The article contains a number of conclusions about the prerequisites for the model’s existence and uniqueness as well as the restrictions on the boundedness and positivity of the solution. To satisfy the requirements for the existence and uniqueness of the precise solution, the Lipschitz condition is applied. Within the local context, we have examined the stability of equilibrium points. Additionally, we investigated whether Hopf bifurcation may occur at the interior equilibrium point of our suggested model. We have used the Generalised Euler technique to approximatively solve the model. The suggested scheme’s dependability is indicated by the fact that the results produced using the current numerical approach converge to equilibrium for the fractional order. For our research, MATLAB was used to enable graphical representations and numerical simulations.

本文使用Caputo技术来分析分数阶捕食者-猎物场景。将猎物种群的延迟承载能力与单个猎物对捕食者的恐惧的影响结合起来是这方面的两个方面。我们首先提供了一个整数阶导数的模型公式，随后我们将其扩展到一个分数阶系统的Caputo导数。本文给出了模型存在唯一性的先决条件以及解的有界性和正性的限制条件。为了满足精确解的存在唯一性要求，应用了Lipschitz条件。在局部情况下，我们考察了平衡点的稳定性。此外，我们还研究了Hopf分岔是否可能发生在我们建议的模型的内部平衡点上。我们使用广义欧拉技术对模型进行了近似求解。采用现有数值方法得到的结果收敛于分数阶的平衡态，表明了所提方案的可靠性。在我们的研究中，使用MATLAB进行图形表示和数值模拟。

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引用次数: 0

Decision-maker’s behavioral preferences modeling in fuzzy goal programming through linear-nonlinear membership functions 基于线性-非线性隶属函数的模糊目标规划中决策者行为偏好建模

Q3 Mathematics

Results in Control and Optimization

Pub Date : 2025-06-01 Epub Date: 2025-05-21 DOI: 10.1016/j.rico.2025.100576

Mohamed Sadok Cherif

A relevant extension of traditional goal programming (GP), fuzzy goal programming (FGP) can handle uncertainty and imprecision in multi-objective optimization problems. Based on fuzzy set theory, the notion of membership functions has been introduced to consider the fuzziness related to objectives and constraints. These membership functions are mainly intended for fuzziness in the GP rather than modeling the decision-maker’s (DM’s) preferences and his/her attitude toward risk in the decision-making process. In the satisfying philosophy of FGP, little attention has been given to how preferences evolve in terms of the behavior of the decision-maker (DM) and how these preferences may affect decisions in risky scenarios. To address this issue, we suggest novel behavioral-type utility functions for the FGP approach by introducing the concept of behavioral membership functions. This concept offers an innovative procedure for simulating the DM’s behavioral preferences in the FGP approach. First, two main categories of objectives in relation to the DM’s behavioral preferences are distinguished in this work. A risk aversion parameter is integrated into membership functions according to the nature of each objective type, obtaining the so-called behavioral membership functions. A behavioral FGP approach is subsequently formulated. Finally, an illustrative example of venture capital investments, a sensitivity analysis, and comparisons with other FGP approaches are provided to demonstrate the validity and practicality of our proposed approach.

模糊目标规划是传统目标规划的相关扩展，它可以处理多目标优化问题中的不确定性和不精确性。在模糊集合理论的基础上，引入隶属函数的概念来考虑目标和约束的模糊性。这些隶属度函数主要用于GP中的模糊性，而不是建模决策者（DM）在决策过程中的偏好和他/她对风险的态度。在FGP的满意哲学中，很少有人关注偏好是如何随着决策者的行为而演变的，以及这些偏好是如何影响风险情景下的决策的。为了解决这个问题，我们通过引入行为隶属函数的概念，为FGP方法提出了新的行为型效用函数。这个概念为FGP方法中模拟DM的行为偏好提供了一个创新的过程。首先，本研究区分了与DM行为偏好相关的两类主要目标。根据每个目标类型的性质，将风险规避参数集成到隶属函数中，得到所谓的行为隶属函数。随后制定了行为FGP方法。最后，以风险投资为例，进行了敏感性分析，并与其他FGP方法进行了比较，以证明我们提出的方法的有效性和实用性。

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引用次数: 0

Localization of partial discharge in the high voltage apparatus 高压设备局部放电的定位

Q3 Mathematics

Results in Control and Optimization

Pub Date : 2025-06-01 Epub Date: 2025-05-03 DOI: 10.1016/j.rico.2025.100562

Amir Ghaedi , Mehrdad Mahmoudian , Eduardo M.G. Rodrigues , Rui Melicio

Partial discharges (PD) in high-voltage (HV) devices can be used as indicators of insulation deterioration, often preceding catastrophic failures in power systems. This paper presents a novel approach for locating and identifying PD sources in HV equipment, targeting insulation condition monitoring in power transformers, XLPE cables, and generators. The new technique utilizes the correlation between PD signal energy characteristics to estimate the source location. The effectiveness of the proposed method is validated through selected case studies involving XLPE cables, transformers, and generators. The results demonstrate that the technique can accurately locate PD sources, potentially enhancing the reliability and longevity of HV power equipment. Key contributions of this work include: a comprehensive review of PD detection sensors, with a focus on electrical methods for high-frequency pulse measurement; experimental characterization of PD signals in XLPE cables and transformers, providing insights into their frequency properties; development of a correlation-based algorithm for PD localization, utilizing a database of simulated PD signals; and validation of the proposed method through EMTP-RV simulations and MATLAB signal processing, showing high accuracy in PD source localization. However, the proposed technique has limitations that prevents its generalized which are highlighted in the paper.

高压（HV）设备的局部放电（PD）可以作为绝缘劣化的指标，通常在电力系统发生灾难性故障之前发生。本文提出了一种定位和识别高压设备中局部放电源的新方法，用于电力变压器、交联聚乙烯电缆和发电机的绝缘状态监测。该方法利用PD信号能量特性之间的相关性来估计源位置。通过对XLPE电缆、变压器和发电机的案例研究，验证了所提出方法的有效性。结果表明，该技术可以准确定位局部放电源，有可能提高高压电力设备的可靠性和寿命。这项工作的主要贡献包括：PD检测传感器的全面回顾，重点是高频脉冲测量的电气方法；XLPE电缆和变压器中PD信号的实验表征，提供对其频率特性的见解；利用模拟PD信号的数据库，开发了一种基于相关性的PD定位算法；并通过EMTP-RV仿真和MATLAB信号处理验证了该方法的有效性，表明该方法具有较高的PD源定位精度。然而，所提出的技术有局限性，阻碍了它的推广，这是本文强调的。

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引用次数: 0

Bayesian topology inference of regulatory networks under partial observability 部分可观测条件下调控网络的贝叶斯拓扑推断

Q3 Mathematics

Results in Control and Optimization

Pub Date : 2025-06-01 Epub Date: 2025-05-01 DOI: 10.1016/j.rico.2025.100570

Mohammad Alali, Mahdi Imani

Biological systems, such as microbial communities in metagenomics and gene regulatory networks (GRNs) in genomics, are composed of a vast number of interacting components observed through inherently noisy data. These systems play a critical role in understanding fundamental biological processes, including gene regulation, microbial interactions, and cellular dynamics. For example, microbial communities involve complex interactions between microbes, bacteria, genes, and small molecules observed through omics data, while GRNs consist of numerous interacting genes observed via various gene-expression technologies. However, reconstructing the topology of such networks poses significant challenges due to their large scale, high dimensionality, and the presence of noise. Existing inference techniques often struggle with scalability, interpretability, and overfitting, making them unsuitable for analyzing large and complex biological systems. To overcome these challenges, this paper proposes a Bayesian topology optimization framework for efficient and scalable inference of regulatory networks modeled as partially-observed Boolean dynamical systems (POBDS). The method combines the Boolean Kalman Filter (BKF) as an optimal estimator for POBDS, with Bayesian optimization, which employs Gaussian Process regression and a topology-inspired kernel function to model the log-likelihood function. Numerical experiments demonstrate the superior performance of our framework. In the p53-MDM2 network, our method accurately infers topology with 8 and 16 unknown regulations, achieving higher log-likelihood with 100 and 200 evaluations, respectively. For the mammalian cell cycle network with 10 unknown regulations, proposed method identifies the correct topology among 59,049 possibilities with lower error and faster convergence.

生物系统，如宏基因组学中的微生物群落和基因组学中的基因调控网络（grn），由大量通过固有噪声数据观察到的相互作用成分组成。这些系统在理解基本的生物过程中起着关键作用，包括基因调控、微生物相互作用和细胞动力学。例如，微生物群落涉及微生物、细菌、基因和通过组学数据观察到的小分子之间复杂的相互作用，而grn由通过各种基因表达技术观察到的众多相互作用基因组成。然而，由于这些网络的大规模、高维和存在噪声，重建这些网络的拓扑结构面临着重大挑战。现有的推理技术经常与可扩展性、可解释性和过拟合作斗争，使它们不适合分析大型和复杂的生物系统。为了克服这些挑战，本文提出了一个贝叶斯拓扑优化框架，用于部分观测布尔动力系统（POBDS）模型的有效和可扩展的调节网络推理。该方法将布尔卡尔曼滤波（BKF）作为POBDS的最优估计器，与贝叶斯优化相结合，采用高斯过程回归和拓扑启发核函数对对数似然函数进行建模。数值实验证明了该框架的优越性能。在p53-MDM2网络中，我们的方法准确地推断出具有8个和16个未知规则的拓扑，分别获得了100次和200次评估的更高的对数似然。对于含有10个未知规则的哺乳动物细胞周期网络，该方法在59049种可能性中识别出正确的拓扑结构，误差更小，收敛速度更快。

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引用次数: 0

Optimal control of interactions between invasive alien and native species in a certain time period with the r-PINN approach 用 r-PINN 方法优化控制外来入侵物种和本地物种在一定时期内的相互作用

Q3 Mathematics

Results in Control and Optimization

Pub Date : 2025-06-01 Epub Date: 2025-04-12 DOI: 10.1016/j.rico.2025.100557

Yudi Ari Adi , Danang A. Pratama , Maharani A. Bakar , Sugiyarto Surono , Suparman , Agung Budiantoro

The spread of invasive species poses a significant challenge to native biodiversity and ecosystem stability. An optimal control strategies to minimize the negative impacts of invasive species populations on native species and the ecosystem must be done in order to preserve the diversity in the ecosystem. This study proposes an optimal control framework to mitigate the impact of invasive species by enhancing native species preservation through a reaction–diffusion mathematical model. To solve the system efficiently, a restarting Physics-Informed Neural Network (r-PINN) is employed and benchmarked against the basic PINN. Numerical simulations reveal that r-PINN achieves a reduced training duration of 236.17 s compared to 289.18 s for the basic PINN, representing an 18.32% improvement in computational efficiency. Moreover, r-PINN demonstrates enhanced predictive accuracy, reducing the mean absolute error (MAE) by 4.12%, mean squared error (MSE) training loss by 12.04%, and MSE test loss by 5.11%. These results were validated against the Finite Difference Method (FDM), ensuring correctness of the proposed PINN-based approach. The implementation of the optimal control strategy led to a clear increase in native species populations and effective suppression of invasive species across spatial and temporal domains. Overall, the r-PINN framework offers a reliable and computationally efficient tool for solving nonlinear ecological models involving spatiotemporal control of species populations.

入侵物种的扩散对本地生物多样性和生态系统的稳定性构成了重大挑战。为了保护生态系统的多样性，必须采取最优控制策略，使入侵物种种群对本地物种和生态系统的负面影响最小化。本研究通过反应-扩散数学模型，提出了通过加强本土物种保护来减轻入侵物种影响的最优控制框架。为了有效地解决系统问题，采用了重新启动的物理信息神经网络（r-PINN），并对基本的PINN进行了基准测试。数值模拟表明，r-PINN的训练时间比基本PINN的289.18 s缩短了236.17 s，计算效率提高了18.32%。此外，r-PINN还能提高预测精度，将平均绝对误差（MAE）降低4.12%，均方误差（MSE）训练损失降低12.04%，MSE测试损失降低5.11%。这些结果与有限差分方法（FDM）进行了验证，确保了所提出的基于pup的方法的正确性。实施最优控制策略后，本地物种种群数量明显增加，入侵物种在不同时空域均得到有效抑制。总体而言，r-PINN框架为解决涉及物种种群时空控制的非线性生态模型提供了可靠且计算效率高的工具。

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引用次数: 0

Fuzzy TOPSIS technique for multi-criteria group decision-making: A study of crude oil price 多准则群体决策的模糊TOPSIS技术：原油价格研究

Q3 Mathematics

Results in Control and Optimization

Pub Date : 2025-06-01 Epub Date: 2025-04-09 DOI: 10.1016/j.rico.2025.100565

Sandhya Priya Baral, Prashanta Kumar Parida, Diptirekha Sahoo

Understanding the state of the world economy is improved by forecasting the price from oil industry. The field of crude oil price forecasting have recently heard about the technique for order preference by similarity to ideal solution (TOPSIS) and fuzzy TOPSIS (FTOPSIS) techniques; while choosing the crude oil that counteract in global oil spill reactions. A multi-criteria decision-making (MCDM) challenge has to weight several options according to various criteria. The present study, initially describes type-1 FTOPSIS technique. Secondly, it describes its extension to handle the uncertain data, known as type-1 FTOPSIS technique in multi-criteria group decision making (MCGDM). Thirdly, it also describes type-1 FTOPSIS for group decision-making (DM) to rating the response choices to a simulated crude oil price, which is one of the biggest crude oil reservoirs in the world. The outcome demonstrates the type-1 fuzzy TOPSIS framework for determining the optimal solution by considering the crude oil globally.

通过预测石油行业的价格，可以更好地了解世界经济状况。在原油价格预测领域，最近出现了基于理想解相似性的排序偏好技术（TOPSIS）和模糊TOPSIS （FTOPSIS）技术；同时选择原油来抵消全球石油泄漏反应。多标准决策（MCDM）挑战必须根据不同的标准对多个选项进行权衡。本研究首先描述了1型FTOPSIS技术。其次，介绍了该方法在多准则群决策（MCGDM）中对不确定数据处理的扩展，即type-1 FTOPSIS技术。第三，描述了1型FTOPSIS用于群体决策（DM），对模拟原油价格的响应选择进行评级，这是世界上最大的原油储层之一。结果表明，在全局考虑原油的情况下，一类模糊TOPSIS框架可用于确定最优解。

引用次数: 0