Results in Control and Optimization最新文献

Robust switching control design for matrix second order systems: Application to robotic cane platform 矩阵二阶系统的鲁棒切换控制设计：在机器人手杖平台上的应用

Results in Control and Optimization Pub Date : 2025-07-16 DOI: 10.1016/j.rico.2025.100597

Ivan Yupanqui, Macarena Vilca, Renzo Mendoza, Alain Chupa, Diego Arce, Jesús Alan Calderón, Bryan Bastidas, Miguel Badillo

{"title":"Robust switching control design for matrix second order systems: Application to robotic cane platform","authors":"Ivan Yupanqui, Macarena Vilca, Renzo Mendoza, Alain Chupa, Diego Arce, Jesús Alan Calderón, Bryan Bastidas, Miguel Badillo","doi":"10.1016/j.rico.2025.100597","DOIUrl":"10.1016/j.rico.2025.100597","url":null,"abstract":"<div><div>This paper addresses the switching control design problem for a class of nonlinear matrix second-order systems that characterize the dynamics of robotic and multibody systems. These systems are inherently characterized by significant nonlinearities and are subject to uncertainties, parameter variations, and external disturbances, which pose substantial challenges for control design. Analytical solutions for such control problems are often intractable, necessitating the use of numerical optimization techniques. This study presents sufficient conditions, derived from Lyapunov stability theory, for synthesizing switching feedback controllers that ensure system stability with guaranteed <span><math><msub><mrow><mi>H</mi></mrow><mrow><mi>∞</mi></mrow></msub></math></span> performance. The approach leverages the Linear Parameter Varying (LPV) representation of the nonlinear dynamics through Takagi–Sugeno (T-S) modeling methodology. The proposed stability conditions are formulated as Linear Matrix Inequalities (LMIs), enabling efficient computation using standard convex optimization software. Comprehensive simulation studies demonstrate that the proposed switching control strategy, applicable to a broad class of nonlinear matrix second-order systems, significantly outperforms conventional weighted gain-scheduling approaches in terms of feasibility regions and <span><math><msub><mrow><mi>H</mi></mrow><mrow><mi>∞</mi></mrow></msub></math></span> performance indices. Experimental validation on a robotic cane platform confirms the practical effectiveness of the proposed methodology, achieving nice dynamic performance and robust disturbance rejection capabilities.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"20 ","pages":"Article 100597"},"PeriodicalIF":0.0,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144654049","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep learning approach for automated ‘Kent’ mango maturity grading in compliance with Peruvian standards 深度学习方法自动“肯特”芒果成熟度分级符合秘鲁标准

Results in Control and Optimization Pub Date : 2025-07-03 DOI: 10.1016/j.rico.2025.100589

Orlando Salazar-Campos , Javier Moran Ruiz , José Luis Peralta , Mirian Rubio Cieza , Breysi Salazar Medina , Johonathan Salazar-Campos

{"title":"Deep learning approach for automated ‘Kent’ mango maturity grading in compliance with Peruvian standards","authors":"Orlando Salazar-Campos , Javier Moran Ruiz , José Luis Peralta , Mirian Rubio Cieza , Breysi Salazar Medina , Johonathan Salazar-Campos","doi":"10.1016/j.rico.2025.100589","DOIUrl":"10.1016/j.rico.2025.100589","url":null,"abstract":"<div><div>Deep learning, particularly convolutional neural networks (CNNs), has significantly advanced automated fruit classification based on image analysis. However, accurate classification of <em>Mangifera indica</em> L. remains challenging due to high variability in external appearance and the subjectivity of visual maturity assessment. Misclassification contributes to post-harvest losses, reduced market value, and inconsistencies in quality control. This study develops a CNN-based model for classifying 'Kent' mangoes according to the Peruvian Technical Standard (NTP) 011.025:2023. A dataset of 603 labelled images was used to optimise the CNN architecture, systematically evaluating convolutional and pooling layers, image resolution, and training cycles. The optimised model, trained on 32× 32 pixel images, achieved 96.04 % classification accuracy, 90.91 % recall, and an F1-score of 93.57 %. To validate model robustness, 5-fold cross-validation demonstrated minimal accuracy variation (±0.5 %), while external evaluation achieved 95.8 % accuracy, confirming its real-world applicability. The lightweight single-layer CNN ensures scalable, low-cost implementation for automated sorting systems, reducing computational demands while enhancing classification efficiency. These findings establish deep learning as a viable and cost-effective solution for post-harvest fruit classification, ensuring greater consistency in quality control and supporting sustainable agricultural practices.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"20 ","pages":"Article 100589"},"PeriodicalIF":0.0,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144614218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Effectiveness of bed nets and media awareness in dengue control: A fuzzy analysis 蚊帐和媒体意识在登革热控制中的有效性：一个模糊分析

Results in Control and Optimization Pub Date : 2025-06-26 DOI: 10.1016/j.rico.2025.100593

Shobha Islam, Md. Shahidul Islam, Md. Kamrujjaman

{"title":"Effectiveness of bed nets and media awareness in dengue control: A fuzzy analysis","authors":"Shobha Islam, Md. Shahidul Islam, Md. Kamrujjaman","doi":"10.1016/j.rico.2025.100593","DOIUrl":"10.1016/j.rico.2025.100593","url":null,"abstract":"<div><div>The incorporation of fuzzy analysis in the mathematical modeling of disease outbreaks has brought a paradigm shift in epidemiological research, offering a sophisticated approach to understanding and addressing the complexities inherent in disease dynamics. Unlike traditional mathematical models, which often rely on deterministic assumptions and precise parameter values, fuzzy analysis provides a flexible framework capable of accommodating uncertainty and imprecision within epidemiological systems. This paper presents a novel SVEIR-SEI compartmental model for dengue disease where five key parameters such as transmission rate, mortality rate, recovery rate, biting rate, and vaccination rate are treated as fuzzy numbers. Among these, transmission rate, mortality rate, and recovery rate are defined as a function of virus load whereas biting rate and vaccinations rate are defined as a function of number of bed net users and media awareness regarding vaccination, respectively. Crisp reproduction number is determined using next generation matrix method and hence fuzzy reproduction number is derived as a triangular fuzzy number (TFN). Numerical results show that biting rate and vaccination rate are the two most sensitive parameters to crisp reproduction number. We also examine the impact of using bed nets and media awareness regarding vaccination on the model system under fuzzy environment. It is found that using bed nets is a more effective strategy for dengue control than media coverage regarding vaccination.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"20 ","pages":"Article 100593"},"PeriodicalIF":0.0,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144518612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Mathematical modeling of refugee population dynamics and its impact on deforestation in Tanzania: An ODE-based and neural network-enhanced approach 坦桑尼亚难民人口动态及其对森林砍伐影响的数学建模：基于ode和神经网络增强的方法

Results in Control and Optimization Pub Date : 2025-06-26 DOI: 10.1016/j.rico.2025.100591

Joseph Kajuli, Maranya Mayengo, Ibrahim Fanuel

{"title":"Mathematical modeling of refugee population dynamics and its impact on deforestation in Tanzania: An ODE-based and neural network-enhanced approach","authors":"Joseph Kajuli, Maranya Mayengo, Ibrahim Fanuel","doi":"10.1016/j.rico.2025.100591","DOIUrl":"10.1016/j.rico.2025.100591","url":null,"abstract":"<div><div>Understanding the interplay between refugee population dynamics and environmental factors is crucial for sustainable policy planning and public health preparedness. This study integrates an ordinary differential equation (ODE)-based model with a Neural Network-Enhanced Approach to estimate key parameters governing these interactions. A system of differential equations models refugee settlement, land-use changes, and deforestation, while Physics-Informed Neural Networks (PINNs) refine parameter estimates by minimizing discrepancies between observed and predicted states. Results show that combining traditional ODE modeling with neural networks improves predictive accuracy, capturing nonlinear interactions more effectively than regression-based methods. Specifically, the study examines bifurcation behavior concerning the refugee influx rate (<span><math><mi>μ</mi></math></span>), deforestation rate (<span><math><mi>β</mi></math></span>), and reforestation effort coefficient (<span><math><mi>γ</mi></math></span>). The analysis reveals that all three distributions are unimodal, peaking around 0.10 for <span><math><mi>α</mi></math></span>, 0.12 for <span><math><mi>β</mi></math></span>, and 0.08 for <span><math><mi>γ</mi></math></span>, with positive skewness indicating longer tails towards higher values. These findings underscore the urgent need for policy interventions to curb deforestation while enhancing reforestation efforts. Importantly, environmental degradation and rapid population pressures identified in the model have direct implications for public health, including increased risk of waterborne and vector-borne diseases, reduced access to clean air and food sources, and long-term mental and physical health challenges for displaced populations. Overall, this study highlights key environmental impact drivers and their health consequences, emphasizing the necessity of integrated, cross-sectoral planning in refugee-hosting regions.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"20 ","pages":"Article 100591"},"PeriodicalIF":0.0,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144502345","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A fractional order PID-based sliding mode controller approach for chemical processes 基于分数阶pid的化工过程滑模控制器方法

Results in Control and Optimization Pub Date : 2025-06-24 DOI: 10.1016/j.rico.2025.100592

Oscar Camacho , Sebastian Vega , Marco Herrera , Antonio Di Teodoro , Juan J. Gude

{"title":"A fractional order PID-based sliding mode controller approach for chemical processes","authors":"Oscar Camacho , Sebastian Vega , Marco Herrera , Antonio Di Teodoro , Juan J. Gude","doi":"10.1016/j.rico.2025.100592","DOIUrl":"10.1016/j.rico.2025.100592","url":null,"abstract":"<div><div>This paper proposes a novel control strategy for chemical processes by integrating fractional-order PID (FOPID) controllers with sliding mode control (SMC). Through the use of the enhanced flexibility and superior tuning capabilities of FOPID controllers over traditional PID schemes, the method replaces the classical discontinuous switching mechanism of SMC with a smooth fractional-order control action. The proposed hybrid approach is evaluated through simulations in two nonlinear systems, a mixing tank with variable time delay and a pH neutralization process, and experimentally validated using the TCLab device. Throughout three case studies, the method demonstrates improvements in performance and response between 40% and 10% compared to the other two SMC alternatives. Furthermore, the approach effectively reduces chattering, improves convergence speed, and improves robustness to measurement noise, contributing to extended actuator lifespan. This makes the proposed methodology particularly attractive for chemical process applications, offering a practical and accessible solution for plant operators by enabling the utilization of robust control techniques without requiring deep expertise in nonlinear control design.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"20 ","pages":"Article 100592"},"PeriodicalIF":0.0,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144480325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Estimation of performance measures in a novel M/M/1 queueing model with reverse balking: A simulation-based approach 基于仿真的新型M/M/1排队模型的性能评估

Results in Control and Optimization Pub Date : 2025-06-24 DOI: 10.1016/j.rico.2025.100590

Asmita Tamuli , Dhruba Das , V. Deepthi , Amit Choudhury , Dibyajyoti Bora , Bhushita Patowari , Supahi Mahanta

{"title":"Estimation of performance measures in a novel M/M/1 queueing model with reverse balking: A simulation-based approach","authors":"Asmita Tamuli , Dhruba Das , V. Deepthi , Amit Choudhury , Dibyajyoti Bora , Bhushita Patowari , Supahi Mahanta","doi":"10.1016/j.rico.2025.100590","DOIUrl":"10.1016/j.rico.2025.100590","url":null,"abstract":"<div><div>This article introduces a novel M/M/1 queueing model that incorporates the concept of reverse balking, where customers are more likely to join the queue as the system size increases. Traditional queuing models often assume a constant balking rate or state-dependent balking rate where the balking rate decreases with increase in system size. In contrast, reverse balking reflects scenarios where customer behavior is influenced by more number of customers present in the system. We use a simulation-based approach to estimate key performance measures, including traffic intensity, average system size and average queue length of the proposed model using both classical and Bayesian approaches. In the classical approach, we used the Maximum Likelihood (ML) Estimation procedure to estimate the parameters using the Metropolis-Hastings (MH) algorithm. Moreover, the Bayesian approach employed the Sampling Importance Resampling (SIR) technique to estimate the parameters. The effectiveness of all the estimation techniques has been evaluated based on the root mean squared error (RMSE) of the estimates. The computational results demonstrate that estimates under both approaches converge to the true values as the sample size increases. Moreover, Bayesian estimates yield lower RMSE compared to ML estimates, highlighting their superior accuracy and robustness. Additionally, predictive probabilities for the number of customers in the system are obtained. A real-life application is presented to demonstrate the practical relevance of the proposed study. The findings offer valuable implications for managing and optimizing service systems where reverse balking is common.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"20 ","pages":"Article 100590"},"PeriodicalIF":0.0,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144480326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimal design of vertical slot fishways by using shallow water equations 基于浅水方程的垂直槽型鱼道优化设计

Results in Control and Optimization Pub Date : 2025-06-21 DOI: 10.1016/j.rico.2025.100582

Mostafa Kadiri , Mohammed Louaked , Houari Mechkour

引用次数: 0

Extended state observer based output feedback control of 2 DoF electro hydraulic servo system 基于扩展状态观测器的二自由度电液伺服系统输出反馈控制

Results in Control and Optimization Pub Date : 2025-06-20 DOI: 10.1016/j.rico.2025.100588

Ashpana Shiralkar , Shailaja Kurode , Bhagyashri Tamhane

引用次数: 0

A study of adaptive population sizing in a self-adaptive differential evolution 自适应差异进化中适应性种群规模的研究

Results in Control and Optimization Pub Date : 2025-06-19 DOI: 10.1016/j.rico.2025.100585

Haldi Budiman , Shir Li Wang , Theam Foo Ng , Amr S. Ghoneim , Haidi Ibrahim , Bahbibi Rahmatullah

{"title":"A study of adaptive population sizing in a self-adaptive differential evolution","authors":"Haldi Budiman , Shir Li Wang , Theam Foo Ng , Amr S. Ghoneim , Haidi Ibrahim , Bahbibi Rahmatullah","doi":"10.1016/j.rico.2025.100585","DOIUrl":"10.1016/j.rico.2025.100585","url":null,"abstract":"<div><div>Differential Evolution (DE) is extensively applied due to its simplicity, robustness, and computational efficiency. However, the performance of DE is influenced by several factors, including the nature of the problem, the specific algorithm variant, and user-defined settings. Numerous studies have explored adaptive parameter settings to reduce the sensitivity of DE’s performance to user inputs, parameter choices, and problem characteristics. DE’s ability to find optimal solutions depends on offspring generation and population diversity. One of the ways to improve DE’s population diversity is by adjusting the population size, either by introducing new individuals or eliminating existing ones. This work investigates the adaptation of population sizing of a self-adaptive differential evolution algorithm called Self-Adaptive Ensemble-based DE with Enhanced Population Sizing (SAEDE-EP). The adaptation of population sizing in SAEDE-EP is influenced by two parameters: the threshold value for stagnation comparison of the best individual over generations and the population size’s growth rate. The effect of these two parameters on population sizing adaptation is evaluated using 26 benchmark single-objective unconstrained optimization functions consisting of unimodal, multimodal, hybrid, and composition functions. SAEDE-EP is compared against 18 state-of-the-art evolutionary algorithms on 10 functions from the 100-Digit Challenge on CEC 2019 single-objective real parameter optimization. Additionally, SAEDE-EP is tested on 57 problems from the CEC-2020 Competitions on Real-World Single Objective Constrained Optimization. Comparative analysis indicates that SAEDE-EP performs well in single-objective unconstrained optimization problems with various characteristics and solves 86% of the real-world single-objective constrained optimization, requiring less computational time and less exhaustive effort to set parameters.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"20 ","pages":"Article 100585"},"PeriodicalIF":0.0,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144470001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimizing photovoltaic performance: Data-driven maximum power point prediction via advanced regression models 优化光伏性能：基于先进回归模型的数据驱动最大功率点预测

Results in Control and Optimization Pub Date : 2025-06-19 DOI: 10.1016/j.rico.2025.100586

Maissa Farhat , Azzeddine Dekhane , Abdelhak Djellad , Maen Takruri , Aws Al-Qaisi , Oscar Barambones

{"title":"Optimizing photovoltaic performance: Data-driven maximum power point prediction via advanced regression models","authors":"Maissa Farhat , Azzeddine Dekhane , Abdelhak Djellad , Maen Takruri , Aws Al-Qaisi , Oscar Barambones","doi":"10.1016/j.rico.2025.100586","DOIUrl":"10.1016/j.rico.2025.100586","url":null,"abstract":"<div><div>The accurate prediction of the Maximum Power Point (PMPP) in photovoltaic (PV) systems is critical for optimizing energy yield and enhancing solar energy harvesting efficiency. This study explores the application of data-driven methods to improve PMPP prediction, utilizing advanced regression techniques such as Ridge Regression, Lasso Regression, Decision Tree Regression, and Random Forest Regression. By analyzing a dataset of irradiance, temperature, and PMPP measurements, the research compares the performance of these models in capturing complex nonlinear relationships between key variables. Results indicate that tree-based models, particularly Random Forest Regression, outperform linear models, demonstrating superior predictive accuracy and robustness. Feature importance analysis further highlights the dominant influence of irradiance (GPOA) on PMPP, emphasizing the value of precise irradiance data. These findings underscore the potential of machine learning techniques in optimizing PV system performance. Future research should focus on integrating additional features, such as atmospheric conditions and panel characteristics, and exploring deep learning methods to enhance prediction accuracy further.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"20 ","pages":"Article 100586"},"PeriodicalIF":0.0,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144489372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0