Pub Date : 2025-07-16DOI: 10.1016/j.rico.2025.100597
Ivan Yupanqui, Macarena Vilca, Renzo Mendoza, Alain Chupa, Diego Arce, Jesús Alan Calderón, Bryan Bastidas, Miguel Badillo
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 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 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.
{"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}
Pub Date : 2025-07-11DOI: 10.1016/j.rico.2025.100598
Sarasanabelli Prasanna Kumari , Ali B.M. Ali , Madhusmita Mohanty , Bibhuti Bhusan Dash , Muhammad Rafiq , Sachi Nandan Mohanty , Iskandar Shernazarov , Nashwan Adnan Othman , Nadia Batool
This study examines customer experience and satisfaction with peer-to-peer (P2P) lending platforms in India by analyzing user-generated online reviews. Despite the rapid expansion of India’s P2P lending market, few studies have analyzed consumer feedback to evaluate platform performance. To address this gap, 11,000 customer reviews were scraped from nine leading Indian P2P platforms. Text mining and sentiment analysis techniques, specifically Frequency Analysis, Convergence of Iterated Correlations (CONCOR) cluster analysis, and Exploratory Factor Analysis (EFA) were employed to extract latent satisfaction drivers. The analysis identified key experience drivers such as customer support, loan processing speed, usability, and fraud-related concerns. EFA distilled these into three underlying satisfaction factors: Positive Experiences and Core Functionalities, Customer Support and Overall Experience, and Efficiency in Application Interaction. The study reveals India-specific insights into digital lending behavior and provides targeted recommendations for improving platform trust, responsiveness, and financial accessibility, essential to user retention and financial inclusion in India’s evolving FinTech ecosystem.
{"title":"Customer satisfaction in peer-to-peer lending platforms: A text mining and sentiment analysis approach","authors":"Sarasanabelli Prasanna Kumari , Ali B.M. Ali , Madhusmita Mohanty , Bibhuti Bhusan Dash , Muhammad Rafiq , Sachi Nandan Mohanty , Iskandar Shernazarov , Nashwan Adnan Othman , Nadia Batool","doi":"10.1016/j.rico.2025.100598","DOIUrl":"10.1016/j.rico.2025.100598","url":null,"abstract":"<div><div>This study examines customer experience and satisfaction with peer-to-peer (P2P) lending platforms in India by analyzing user-generated online reviews. Despite the rapid expansion of India’s P2P lending market, few studies have analyzed consumer feedback to evaluate platform performance. To address this gap, 11,000 customer reviews were scraped from nine leading Indian P2P platforms. Text mining and sentiment analysis techniques, specifically Frequency Analysis, Convergence of Iterated Correlations (CONCOR) cluster analysis, and Exploratory Factor Analysis (EFA) were employed to extract latent satisfaction drivers. The analysis identified key experience drivers such as customer support, loan processing speed, usability, and fraud-related concerns. EFA distilled these into three underlying satisfaction factors: Positive Experiences and Core Functionalities, Customer Support and Overall Experience, and Efficiency in Application Interaction. The study reveals India-specific insights into digital lending behavior and provides targeted recommendations for improving platform trust, responsiveness, and financial accessibility, essential to user retention and financial inclusion in India’s evolving FinTech ecosystem.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"20 ","pages":"Article 100598"},"PeriodicalIF":3.2,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144828521","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}
Tea is widely regarded as one of the most popular beverages globally, and Bangladesh plays a significant role both as a producer and consumer of this renowned drink. However, diseases that impact the quality and productivity of crops can greatly impede the production of tea, impacting the final product’s quantity and quality. To prevent and control tea leaf diseases, a reliable and precise diagnosis and identification system is needed. Tea leaf infections are discovered manually, which takes time and affects crop quality and production. Detecting tea leaf disease early can lead to decreased damage to overall tea production. Advanced deep learning methods are simplifying the identification and categorization of specific illnesses in tea plants. This current study introduces TeaNet8, a deep learning-based approach for identifying and classifying eight tea leaf disease classes using a fine-tuned ResNet50V2 model. Moreover, this study employs 2824 images of eight different types of leaf diseases. Preprocessing techniques such as Contrast Limited Adaptive Histogram Equalization (CLAHE), brightness adjustment, and unsharp masking were applied to enhance the dataset. Additionally, data augmentation techniques were used to increase its diversity. The proposed model identify the differnt type of tea leaf disease with 97% accuracy.Gradient-Weighted Class Activation Mapping (Grad-CAM) visualization was employed to interpret and understand model predictions. The model demonstrated perfect accuracy for Algal Spot, Anthracnose, Gray Blight, and White Spot, with accuracy rates of 97.14% for Brown Blight, 94.59% for Healthy leaves, 94.12% for Red Spot, and 92.31% for Bird Eye Spot. Furthermore, the proposed model’s performance was compared against three pre-trained fine-tuning models. Various performance measurement indicators were used to evaluate the performance of the proposed model. The results showed that the proposed model is effective in categorizing diseases in tea leaves.Finally, An Android-based system was developed employing the most effective model to aid farmers for detecting tea leaf diseases.
{"title":"TeaNet8: A real time Android application-based Tea Leaf Disease detection using fine-tuned transfer learning and Gradient-Weighted Class Activation Mapping visualization","authors":"Ismotara Dipty , Md Assaduzzaman , Nafiz Fahad , Md. Jakir Hossen , Md. Farhatul Haider , Fiaj Rahman","doi":"10.1016/j.rico.2025.100577","DOIUrl":"10.1016/j.rico.2025.100577","url":null,"abstract":"<div><div>Tea is widely regarded as one of the most popular beverages globally, and Bangladesh plays a significant role both as a producer and consumer of this renowned drink. However, diseases that impact the quality and productivity of crops can greatly impede the production of tea, impacting the final product’s quantity and quality. To prevent and control tea leaf diseases, a reliable and precise diagnosis and identification system is needed. Tea leaf infections are discovered manually, which takes time and affects crop quality and production. Detecting tea leaf disease early can lead to decreased damage to overall tea production. Advanced deep learning methods are simplifying the identification and categorization of specific illnesses in tea plants. This current study introduces TeaNet8, a deep learning-based approach for identifying and classifying eight tea leaf disease classes using a fine-tuned ResNet50V2 model. Moreover, this study employs 2824 images of eight different types of leaf diseases. Preprocessing techniques such as Contrast Limited Adaptive Histogram Equalization (CLAHE), brightness adjustment, and unsharp masking were applied to enhance the dataset. Additionally, data augmentation techniques were used to increase its diversity. The proposed model identify the differnt type of tea leaf disease with 97% accuracy.Gradient-Weighted Class Activation Mapping (Grad-CAM) visualization was employed to interpret and understand model predictions. The model demonstrated perfect accuracy for Algal Spot, Anthracnose, Gray Blight, and White Spot, with accuracy rates of 97.14% for Brown Blight, 94.59% for Healthy leaves, 94.12% for Red Spot, and 92.31% for Bird Eye Spot. Furthermore, the proposed model’s performance was compared against three pre-trained fine-tuning models. Various performance measurement indicators were used to evaluate the performance of the proposed model. The results showed that the proposed model is effective in categorizing diseases in tea leaves.Finally, An Android-based system was developed employing the most effective model to aid farmers for detecting tea leaf diseases.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"20 ","pages":"Article 100577"},"PeriodicalIF":0.0,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144711397","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}
Pub Date : 2025-07-03DOI: 10.1016/j.rico.2025.100589
Orlando Salazar-Campos , Javier Moran Ruiz , José Luis Peralta , Mirian Rubio Cieza , Breysi Salazar Medina , Johonathan Salazar-Campos
Deep learning, particularly convolutional neural networks (CNNs), has significantly advanced automated fruit classification based on image analysis. However, accurate classification of Mangifera indica 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.
{"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}
Pub Date : 2025-06-26DOI: 10.1016/j.rico.2025.100593
Shobha Islam, Md. Shahidul Islam, Md. Kamrujjaman
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.
{"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}
Pub Date : 2025-06-26DOI: 10.1016/j.rico.2025.100591
Joseph Kajuli, Maranya Mayengo, Ibrahim Fanuel
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 (), deforestation rate (), and reforestation effort coefficient (). The analysis reveals that all three distributions are unimodal, peaking around 0.10 for , 0.12 for , and 0.08 for , 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.
{"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}
Pub Date : 2025-06-24DOI: 10.1016/j.rico.2025.100592
Oscar Camacho , Sebastian Vega , Marco Herrera , Antonio Di Teodoro , Juan J. Gude
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.
{"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}
Pub Date : 2025-06-24DOI: 10.1016/j.rico.2025.100590
Asmita Tamuli , Dhruba Das , V. Deepthi , Amit Choudhury , Dibyajyoti Bora , Bhushita Patowari , Supahi Mahanta
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.
{"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}
Pub Date : 2025-06-21DOI: 10.1016/j.rico.2025.100582
Mostafa Kadiri , Mohammed Louaked , Houari Mechkour
In this paper, we present a mathematical formulation of an optimal design problem related to a vertical slot fishway. The work involves modeling, mathematical analysis and numerical approximation of a coupled problem between a primal hyperbolic system and adjoint problem of shallow water for the cost function of the optimal structure. We express the shape gradient of the cost function by introducing the associated adjoint state system. We proceed with the study of the adjoint system by using the Lax symbolic symmetrizer for hyperbolic systems and pseudo-differential techniques. The numerical resolution of this problem combines two main approaches: The first one relies on the finite volume method with the Roe solver for the spatial and temporal discretization, and the second one uses a minimizing algorithm, the gradient of the objective function, evaluated by an adjoint problem. Numerical simulations are given which illustrate the accuracy of this technique.
{"title":"Optimal design of vertical slot fishways by using shallow water equations","authors":"Mostafa Kadiri , Mohammed Louaked , Houari Mechkour","doi":"10.1016/j.rico.2025.100582","DOIUrl":"10.1016/j.rico.2025.100582","url":null,"abstract":"<div><div>In this paper, we present a mathematical formulation of an optimal design problem related to a vertical slot fishway. The work involves modeling, mathematical analysis and numerical approximation of a coupled problem between a primal hyperbolic system and adjoint problem of shallow water for the cost function of the optimal structure. We express the shape gradient of the cost function by introducing the associated adjoint state system. We proceed with the study of the adjoint system by using the Lax symbolic symmetrizer for hyperbolic systems and pseudo-differential techniques. The numerical resolution of this problem combines two main approaches: The first one relies on the finite volume method with the Roe solver for the spatial and temporal discretization, and the second one uses a minimizing algorithm, the gradient of the objective function, evaluated by an adjoint problem. Numerical simulations are given which illustrate the accuracy of this technique.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"20 ","pages":"Article 100582"},"PeriodicalIF":0.0,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144502346","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}
Positioning a load in a two-dimensional subspace requires a two-degrees-of-freedom (2 DoF) position control system. The precise positioning of the load has been the driving motivation for electro-hydraulic actuation and its robust control. 2 DoF electro-hydraulic servo system (EHSS) is complex and nonlinear. Each of the 2 DoF is approximated by the second order model with uncertainty. A new sliding variable is proposed for precise finite-time positioning of a load. The extended state observer based controller is devised using higher-order sliding modes. Uncertainties and states are estimated to implement the controller in a finite time. The method is verified in both simulation and experiment. It is shown that the proposed method yield robust and precise positioning of load in two-dimensional subspace.
{"title":"Extended state observer based output feedback control of 2 DoF electro hydraulic servo system","authors":"Ashpana Shiralkar , Shailaja Kurode , Bhagyashri Tamhane","doi":"10.1016/j.rico.2025.100588","DOIUrl":"10.1016/j.rico.2025.100588","url":null,"abstract":"<div><div>Positioning a load in a two-dimensional subspace requires a two-degrees-of-freedom (2 DoF) position control system. The precise positioning of the load has been the driving motivation for electro-hydraulic actuation and its robust control. 2 DoF electro-hydraulic servo system (EHSS) is complex and nonlinear. Each of the 2 DoF is approximated by the second order model with uncertainty. A new sliding variable is proposed for precise finite-time positioning of a load. The extended state observer based controller is devised using higher-order sliding modes. Uncertainties and states are estimated to implement the controller in a finite time. The method is verified in both simulation and experiment. It is shown that the proposed method yield robust and precise positioning of load in two-dimensional subspace.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"20 ","pages":"Article 100588"},"PeriodicalIF":0.0,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144366591","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}
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