Pub Date : 2025-12-01Epub Date: 2025-10-10DOI: 10.1016/j.rico.2025.100622
Faisal Yousafzai , Muhammad Danish Zia , Yuan Miao , Saleem Abdullah , Yiu-Yin Lee
Decision-making is a complex process influenced by multiple factors, including available data, expert knowledge, contextual variables, stakeholder preferences, and emotional considerations. Despite the numerous methodologies proposed in recent years, there remains a need for more effective and intelligent techniques that address uncertainty in nonlinear, sentiment-based decision-making environments. In this paper, we develop and apply advanced nonlinear fuzzy models using quadratic Diophantine fuzzy soft sets and quadratic Diophantine fuzzy cognitive maps, with a particular focus on patient well-being through medical sentiment analysis. We begin with the quadratic Diophantine fuzzy soft set method to enhance the assessment of patient conditions through the analysis of symptoms, providing a precise evaluation of health status by considering multiple key factors. Next, the quadratic Diophantine fuzzy cognitive map method is used to identify the main elements influencing patient satisfaction by analyzing reviews on depression medications. This analysis also incorporates VADER-based sentiment analysis, implemented in Python, along with correlation analysis to quantify sentiment polarity in patient feedback on depression treatments. Collectively, these methods introduce nonlinear fuzzy tools that enhance evaluations and satisfaction assessments for effective sentiment-based decision-making.
{"title":"Quadratic Diophantine fuzzy sentiment-based nonlinear decision-making for medical diagnostics through soft sets and cognitive maps","authors":"Faisal Yousafzai , Muhammad Danish Zia , Yuan Miao , Saleem Abdullah , Yiu-Yin Lee","doi":"10.1016/j.rico.2025.100622","DOIUrl":"10.1016/j.rico.2025.100622","url":null,"abstract":"<div><div>Decision-making is a complex process influenced by multiple factors, including available data, expert knowledge, contextual variables, stakeholder preferences, and emotional considerations. Despite the numerous methodologies proposed in recent years, there remains a need for more effective and intelligent techniques that address uncertainty in nonlinear, sentiment-based decision-making environments. In this paper, we develop and apply advanced nonlinear fuzzy models using quadratic Diophantine fuzzy soft sets and quadratic Diophantine fuzzy cognitive maps, with a particular focus on patient well-being through medical sentiment analysis. We begin with the quadratic Diophantine fuzzy soft set method to enhance the assessment of patient conditions through the analysis of symptoms, providing a precise evaluation of health status by considering multiple key factors. Next, the quadratic Diophantine fuzzy cognitive map method is used to identify the main elements influencing patient satisfaction by analyzing reviews on depression medications. This analysis also incorporates VADER-based sentiment analysis, implemented in Python, along with correlation analysis to quantify sentiment polarity in patient feedback on depression treatments. Collectively, these methods introduce nonlinear fuzzy tools that enhance evaluations and satisfaction assessments for effective sentiment-based decision-making.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"21 ","pages":"Article 100622"},"PeriodicalIF":3.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145269090","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}
This study aims to develop a multi-objective decision-making technique, called Simultaneous Evaluation of Criteria and Alternatives (SECA), to optimally rank green construction project contractors. The SECA approach is formulated as a multi-objective nonlinear programming problem comprising three objective functions: (1) maximizing the overall performance of alternatives, (2) minimizing the deviation of criteria weights from a reference point based on intra-criteria variation information, and (3) minimizing the deviation of criteria weights based on inter-criteria variation information, subject to two constraints on criteria weights. To implement the method, 16 key contractor prequalification criteria were first identified, followed by the selection of 10 qualified applicants. A decision matrix was constructed, and reference points were calculated. Variable weights for criteria and rankings of alternatives were obtained by coding the SECA method in Lingo software, considering different values of β. Determining the optimal β was a critical step, with values ranging from 0.1 to 7 evaluated. Results indicated that the maximum objective function value of 0.786 was achieved at β = 6, with the weight effect of the proposed price criterion being 0.0737. Contractor A3, with a score of 0.8887, was identified as the top-ranked contractor. Overall, the findings indicate that the SECA-based optimization method not only supports decision-makers in improving quality and reducing costs but also enhances transparency and trust in the selection process. By simultaneously evaluating criteria and alternatives and determining objective weights based on standard deviation and inter-criteria correlations, the method strengthens both transparency and reliability through reproducible and comparable analyses.
{"title":"Development of SECA multi criteria decision making method to optimally select contractors for green construction projects (Case study for Iran)","authors":"S․Ali Moayeripour , S․Mohammad Mirhosseini , Mohammad Ehsanifar , Ehsanollah Zeighami","doi":"10.1016/j.rico.2025.100617","DOIUrl":"10.1016/j.rico.2025.100617","url":null,"abstract":"<div><div>This study aims to develop a multi-objective decision-making technique, called Simultaneous Evaluation of Criteria and Alternatives (SECA), to optimally rank green construction project contractors. The SECA approach is formulated as a multi-objective nonlinear programming problem comprising three objective functions: (1) maximizing the overall performance of alternatives, (2) minimizing the deviation of criteria weights from a reference point based on intra-criteria variation information, and (3) minimizing the deviation of criteria weights based on inter-criteria variation information, subject to two constraints on criteria weights. To implement the method, 16 key contractor prequalification criteria were first identified, followed by the selection of 10 qualified applicants. A decision matrix was constructed, and reference points were calculated. Variable weights for criteria and rankings of alternatives were obtained by coding the SECA method in Lingo software, considering different values of β. Determining the optimal β was a critical step, with values ranging from 0.1 to 7 evaluated. Results indicated that the maximum objective function value of 0.786 was achieved at β = 6, with the weight effect of the proposed price criterion being 0.0737. Contractor A3, with a score of 0.8887, was identified as the top-ranked contractor. Overall, the findings indicate that the SECA-based optimization method not only supports decision-makers in improving quality and reducing costs but also enhances transparency and trust in the selection process. By simultaneously evaluating criteria and alternatives and determining objective weights based on standard deviation and inter-criteria correlations, the method strengthens both transparency and reliability through reproducible and comparable analyses.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"21 ","pages":"Article 100617"},"PeriodicalIF":3.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145269089","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 : 2025-12-01Epub Date: 2025-11-07DOI: 10.1016/j.rico.2025.100633
Mohammad Hosein Sabzalian , Changdong Du , Ardashir Mohammadzadeh
The formation control (FC) for nonlinear mobile robots (MRs) during various operations is studied in this paper. An interval type-3 (T3) fuzzy logic system (FLS) based controller is introduced to enable the multiple MRs to follow the desired formation, without requiring measurement the relative pose or velocity of the follower robots. A camera is used to coordinate the motion between the leader and the followers. Additionally, the robustness of the system is analyzed in the presence of external disturbances and unknown uncertainties. T3-FLSs with novel online optimized tuning rules and adaptive mechanisms serve the dual purpose of approximating the unknown dynamics of MRs with nonholonomic constraints and implementing a fuzzy-based controller. By utilizing the Lyapunov approach, the adaptation mechanisms of the FLSs are computed, and it is proven that the closed-loop system achieves asymptotic stability. Furthermore, computer simulations are conducted to test the system’s performance in terms of appropriate transient responses and robust tracking against unknown dynamics and disturbances. Simulation results demonstrate accurate tracking, and robustness under various uncertainties. The proposed method provides a computationally efficient, adaptive, and theoretically sound solution for multi-robot formation control, highlighting its potential for practical cooperative robotics applications.
{"title":"A camera-based type-3 fuzzy formation control of multiple robots","authors":"Mohammad Hosein Sabzalian , Changdong Du , Ardashir Mohammadzadeh","doi":"10.1016/j.rico.2025.100633","DOIUrl":"10.1016/j.rico.2025.100633","url":null,"abstract":"<div><div>The formation control (FC) for nonlinear mobile robots (MRs) during various operations is studied in this paper. An interval type-3 (T3) fuzzy logic system (FLS) based controller is introduced to enable the multiple MRs to follow the desired formation, without requiring measurement the relative pose or velocity of the follower robots. A camera is used to coordinate the motion between the leader and the followers. Additionally, the robustness of the system is analyzed in the presence of external disturbances and unknown uncertainties. T3-FLSs with novel online optimized tuning rules and adaptive mechanisms serve the dual purpose of approximating the unknown dynamics of MRs with nonholonomic constraints and implementing a fuzzy-based controller. By utilizing the Lyapunov approach, the adaptation mechanisms of the FLSs are computed, and it is proven that the closed-loop system achieves asymptotic stability. Furthermore, computer simulations are conducted to test the system’s performance in terms of appropriate transient responses and robust tracking against unknown dynamics and disturbances. Simulation results demonstrate accurate tracking, and robustness under various uncertainties. The proposed method provides a computationally efficient, adaptive, and theoretically sound solution for multi-robot formation control, highlighting its potential for practical cooperative robotics applications.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"21 ","pages":"Article 100633"},"PeriodicalIF":3.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145520087","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 : 2025-12-01Epub Date: 2025-09-26DOI: 10.1016/j.rico.2025.100616
Siwei Liu, Qing-Shan Jia
With the continuous development of autonomous vehicles technology, the feasibility probability estimation of the sample paths has become a key requirement in the performance evaluation of its control policies and the policy optimization with chance constraints. Aiming at the defect that current autonomous driving testing methods generally rely on human prior knowledge for sampling allocation, this paper proposes a method that can allocate the number of samples according to the feasibility probability and state occurrence probability, and proves its optimality. In this paper, we first propose an optimal sampling times allocation method to minimize probabilistic estimation variance, which can obtain an acceleration effect that is reciprocal to the probability of occurrence of the most critical state. For the actual task requirement, we also propose algorithms with iterative estimation and low-fidelity models. The results from numerical experiments with two initial states and intelligent vehicle cornering cruise experiments under ten initial states demonstrate that our method can achieve the same prediction estimation error with fewer samples.
{"title":"An accelerated black-box sample paths feasibility probability estimation method for control policies of autonomous vehicles","authors":"Siwei Liu, Qing-Shan Jia","doi":"10.1016/j.rico.2025.100616","DOIUrl":"10.1016/j.rico.2025.100616","url":null,"abstract":"<div><div>With the continuous development of autonomous vehicles technology, the feasibility probability estimation of the sample paths has become a key requirement in the performance evaluation of its control policies and the policy optimization with chance constraints. Aiming at the defect that current autonomous driving testing methods generally rely on human prior knowledge for sampling allocation, this paper proposes a method that can allocate the number of samples according to the feasibility probability and state occurrence probability, and proves its optimality. In this paper, we first propose an optimal sampling times allocation method to minimize probabilistic estimation variance, which can obtain an acceleration effect that is reciprocal to the probability of occurrence of the most critical state. For the actual task requirement, we also propose algorithms with iterative estimation and low-fidelity models. The results from numerical experiments with two initial states and intelligent vehicle cornering cruise experiments under ten initial states demonstrate that our method can achieve the same prediction estimation error with fewer samples.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"21 ","pages":"Article 100616"},"PeriodicalIF":3.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221912","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 : 2025-12-01Epub Date: 2025-10-24DOI: 10.1016/j.rico.2025.100626
Muhammad Farman , Ali Hasan , Sana Ullah Saqib , Ali Akbar , Aceng Sambas , Mohamed Hafez
<div><div>In this paper, we developed a framework that describes the transmission of a hepatitis C model with fractional and machine learning approach for analysis and numerical outcome. The model consists of four groups: viral load, susceptible hepatocytes, infected hepatocytes, and the humoral immune response that the host triggers to fight the virus. Biological feasibility of the model, such as positivity, uniqueness solution through fixed point results. The fractional-order power law kernel solution function was used to set up the numerical simulation. Using data collected by Fractional Order Differential Equations (FODEs) with a fractional order power law kernel solution function, MATLAB was implemented to perform the simulations in question. This is accomplished through the application of the Bayesian Regularization Backpropagation Artificial Neural Network (BRB-ANN) intelligent computing technique. The data set for training the BRB-ANNs is created using Fractional Order Differential Equations (FODEs). The Bayesian Regularization Method with Backpropagation Artificial Neural Nets (BRB-ANNs), the fractional-order hepatitis C virus (FOHCV) model’s precision and effectiveness were significantly improved. The effectiveness of the proposed strategies is evidenced by achieving exceptionally low absolute errors ranging from <span><math><mrow><mo>(</mo><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mo>−</mo><mn>3</mn></mrow></msup><mo>)</mo></mrow></math></span> to <span><math><mrow><mo>(</mo><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mo>−</mo><mn>9</mn></mrow></msup><mo>)</mo></mrow></math></span>, minimal Mean Square Error (MSE) values between <span><math><mrow><mo>(</mo><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mo>−</mo><mn>7</mn></mrow></msup><mo>)</mo></mrow></math></span> and <span><math><mrow><mo>(</mo><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mo>−</mo><mn>10</mn></mrow></msup><mo>)</mo></mrow></math></span>, and an almost perfect coefficient of determination <span><math><mrow><mo>(</mo><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup><mo>≈</mo><mn>0</mn><mo>.</mo><mn>999</mn><mo>)</mo></mrow></math></span>. Furthermore, the error histograms (Er.Hgs), ranging from <span><math><mrow><mo>(</mo><mo>−</mo><mn>9</mn><mo>.</mo><mn>4</mn><mo>×</mo><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mo>−</mo><mn>10</mn></mrow></msup><mo>)</mo></mrow></math></span> to <span><math><mrow><mo>(</mo><mo>−</mo><mn>4</mn><mo>.</mo><mn>12</mn><mo>×</mo><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mo>−</mo><mn>6</mn></mrow></msup><mo>)</mo></mrow></math></span>, along with the corresponding time series plots (TSP), further validate the precision and reliability of the developed models. Dynamically and graphically, demonstrations indicate the achievement of AI with BRB-ANNs compared to the standard solution, and 3D Lorenz curves for FOHCV are analyzed. These results support the theoretical observation of Hepatitis C disease epidemics and the pr
{"title":"ANN computing framework for modeling and predicting the dynamics of fractional order hepatitis C virus model","authors":"Muhammad Farman , Ali Hasan , Sana Ullah Saqib , Ali Akbar , Aceng Sambas , Mohamed Hafez","doi":"10.1016/j.rico.2025.100626","DOIUrl":"10.1016/j.rico.2025.100626","url":null,"abstract":"<div><div>In this paper, we developed a framework that describes the transmission of a hepatitis C model with fractional and machine learning approach for analysis and numerical outcome. The model consists of four groups: viral load, susceptible hepatocytes, infected hepatocytes, and the humoral immune response that the host triggers to fight the virus. Biological feasibility of the model, such as positivity, uniqueness solution through fixed point results. The fractional-order power law kernel solution function was used to set up the numerical simulation. Using data collected by Fractional Order Differential Equations (FODEs) with a fractional order power law kernel solution function, MATLAB was implemented to perform the simulations in question. This is accomplished through the application of the Bayesian Regularization Backpropagation Artificial Neural Network (BRB-ANN) intelligent computing technique. The data set for training the BRB-ANNs is created using Fractional Order Differential Equations (FODEs). The Bayesian Regularization Method with Backpropagation Artificial Neural Nets (BRB-ANNs), the fractional-order hepatitis C virus (FOHCV) model’s precision and effectiveness were significantly improved. The effectiveness of the proposed strategies is evidenced by achieving exceptionally low absolute errors ranging from <span><math><mrow><mo>(</mo><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mo>−</mo><mn>3</mn></mrow></msup><mo>)</mo></mrow></math></span> to <span><math><mrow><mo>(</mo><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mo>−</mo><mn>9</mn></mrow></msup><mo>)</mo></mrow></math></span>, minimal Mean Square Error (MSE) values between <span><math><mrow><mo>(</mo><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mo>−</mo><mn>7</mn></mrow></msup><mo>)</mo></mrow></math></span> and <span><math><mrow><mo>(</mo><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mo>−</mo><mn>10</mn></mrow></msup><mo>)</mo></mrow></math></span>, and an almost perfect coefficient of determination <span><math><mrow><mo>(</mo><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup><mo>≈</mo><mn>0</mn><mo>.</mo><mn>999</mn><mo>)</mo></mrow></math></span>. Furthermore, the error histograms (Er.Hgs), ranging from <span><math><mrow><mo>(</mo><mo>−</mo><mn>9</mn><mo>.</mo><mn>4</mn><mo>×</mo><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mo>−</mo><mn>10</mn></mrow></msup><mo>)</mo></mrow></math></span> to <span><math><mrow><mo>(</mo><mo>−</mo><mn>4</mn><mo>.</mo><mn>12</mn><mo>×</mo><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mo>−</mo><mn>6</mn></mrow></msup><mo>)</mo></mrow></math></span>, along with the corresponding time series plots (TSP), further validate the precision and reliability of the developed models. Dynamically and graphically, demonstrations indicate the achievement of AI with BRB-ANNs compared to the standard solution, and 3D Lorenz curves for FOHCV are analyzed. These results support the theoretical observation of Hepatitis C disease epidemics and the pr","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"21 ","pages":"Article 100626"},"PeriodicalIF":3.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145417191","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 : 2025-12-01Epub Date: 2025-11-07DOI: 10.1016/j.rico.2025.100631
Bulugu Ndulu Batume , Chacha Stephen Chacha
We present a matrix-free Newton–Krylov solver with exact line search (Algorithm 3) for algebraic Riccati equations and benchmark it against standard Newton variants and common baselines. Using the enhancement percentage metric, , the method delivers consistent and often dramatic gains across problem sizes and settings. On large problems (), wall-clock time improves by 99.8–99.9% relative to classical Newton methods while achieving up to 97% EP in accuracy (final/relative residuals). In an aircraft control instance (), Algorithm 3 attains 99.6% EP in time, reduces iterations by 50–57%, and improves residuals by 82–98%. For large diagonal families (), Algorithm 3 converges in approximately 5 Newton steps with predictable scaling (about 0.30 s, 3.92 s, and 36.72 s, respectively), remaining well-competitive with direct solvers (e.g., dare()) while avoiding Kronecker products and explicit Jacobians. Overall, the results indicate a robust, low-iteration, and near-instant approach that is attractive for real-time and embedded control contexts where both speed and solution quality are paramount.
{"title":"A fast-converging Newton-based iterative scheme for the algebraic Riccati equation with step-size optimization","authors":"Bulugu Ndulu Batume , Chacha Stephen Chacha","doi":"10.1016/j.rico.2025.100631","DOIUrl":"10.1016/j.rico.2025.100631","url":null,"abstract":"<div><div>We present a matrix-free Newton–Krylov solver with exact line search (Algorithm 3) for algebraic Riccati equations and benchmark it against standard Newton variants and common baselines. Using the enhancement percentage metric, <span><math><mrow><mi>EP</mi><mrow><mo>(</mo><mtext>%</mtext><mo>)</mo></mrow><mo>=</mo><mn>100</mn><mrow><mo>(</mo><mn>1</mn><mo>−</mo><mtext>Proposed</mtext><mo>/</mo><mtext>Other</mtext><mo>)</mo></mrow></mrow></math></span>, the method delivers consistent and often dramatic gains across problem sizes and settings. On large problems (<span><math><mrow><mi>n</mi><mo>=</mo><mn>100</mn></mrow></math></span>), wall-clock time improves by 99.8–99.9% relative to classical Newton methods while achieving up to 97% EP in accuracy (final/relative residuals). In an aircraft control instance (<span><math><mrow><mi>n</mi><mo>=</mo><mn>70</mn><mo>,</mo><mi>m</mi><mo>=</mo><mn>35</mn></mrow></math></span>), Algorithm 3 attains 99.6% EP in time, reduces iterations by 50–57%, and improves residuals by 82–98%. For large diagonal families (<span><math><mrow><mi>n</mi><mo>=</mo><mn>500</mn><mo>,</mo><mn>1000</mn><mo>,</mo><mn>2000</mn></mrow></math></span>), Algorithm 3 converges in approximately 5 Newton steps with predictable scaling (about 0.30<!--> <!-->s, 3.92<!--> <!-->s, and 36.72<!--> <!-->s, respectively), remaining well-competitive with direct solvers (e.g., <span>dare()</span>) while avoiding Kronecker products and explicit Jacobians. Overall, the results indicate a robust, low-iteration, and near-instant approach that is attractive for real-time and embedded control contexts where both speed and solution quality are paramount.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"21 ","pages":"Article 100631"},"PeriodicalIF":3.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145568189","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 : 2025-12-01Epub Date: 2025-12-08DOI: 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.
{"title":"A spectral collocation method via Krawtchouk polynomials for two-dimensional Liouville–Caputo fractional optimal control problems","authors":"Hajar Mohammadi, Habibollah Saeedi, Mohammad Izadi","doi":"10.1016/j.rico.2025.100639","DOIUrl":"10.1016/j.rico.2025.100639","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"21 ","pages":"Article 100639"},"PeriodicalIF":3.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145736393","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 : 2025-12-01Epub Date: 2025-11-22DOI: 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.
{"title":"LQR, LQG, Extremum Seeking and Sliding Mode controls combined with Fuzzy for a rotary inverted pendulum stabilization","authors":"Erwin Susanto , Sony Sumaryo , Mohd Fadzil Hassan","doi":"10.1016/j.rico.2025.100629","DOIUrl":"10.1016/j.rico.2025.100629","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"21 ","pages":"Article 100629"},"PeriodicalIF":3.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145614417","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 : 2025-12-01Epub Date: 2025-10-16DOI: 10.1016/j.rico.2025.100621
Sanju Sardar, Satyajit Mukherjee, Priti Kumar Roy
Methanol poisoning is a major and highly concerning public health issue worldwide, particularly in developing and underdeveloped countries. Factors, such as inadequate regulations, lack of awareness about its dangers, and deeply rooted cultural traditions play a major role in the widespread consumption of adulterated alcohol. To address such challenges, we propose a four-dimensional mathematical model to analyze the impact of awareness interventions on reducing methanol toxicity and illicit alcohol consumption. Some basic properties of the model, such as non-negativity, boundedness, existence of equilibria, and their stability, have been analyzed. An optimal control system is developed by incorporating two control measures—awareness campaigns and anti-drinking medication to minimize illegal alcohol consumption and related management costs. We establish the existence of an optimal control pair and characterize it through Pontryagin’s minimum principle. To ensure robustness and understand parameter influence, Latin Hypercube Sampling (LHS) and Sobol sensitivity analysis are used for model validation and sensitivity assessment. We also perform a cost-effectiveness analysis using the Average Cost-Effectiveness Ratio (ACER) to identify the most economically efficient intervention strategy. All analytical findings of the study are demonstrated and validated through numerical simulations.
{"title":"Controlling illicit alcohol consumption through awareness and disulfiram: A mathematical study","authors":"Sanju Sardar, Satyajit Mukherjee, Priti Kumar Roy","doi":"10.1016/j.rico.2025.100621","DOIUrl":"10.1016/j.rico.2025.100621","url":null,"abstract":"<div><div>Methanol poisoning is a major and highly concerning public health issue worldwide, particularly in developing and underdeveloped countries. Factors, such as inadequate regulations, lack of awareness about its dangers, and deeply rooted cultural traditions play a major role in the widespread consumption of adulterated alcohol. To address such challenges, we propose a four-dimensional mathematical model to analyze the impact of awareness interventions on reducing methanol toxicity and illicit alcohol consumption. Some basic properties of the model, such as non-negativity, boundedness, existence of equilibria, and their stability, have been analyzed. An optimal control system is developed by incorporating two control measures—awareness campaigns and anti-drinking medication to minimize illegal alcohol consumption and related management costs. We establish the existence of an optimal control pair and characterize it through Pontryagin’s minimum principle. To ensure robustness and understand parameter influence, Latin Hypercube Sampling (LHS) and Sobol sensitivity analysis are used for model validation and sensitivity assessment. We also perform a cost-effectiveness analysis using the Average Cost-Effectiveness Ratio (ACER) to identify the most economically efficient intervention strategy. All analytical findings of the study are demonstrated and validated through numerical simulations.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"21 ","pages":"Article 100621"},"PeriodicalIF":3.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145362462","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 : 2025-12-01Epub Date: 2025-10-22DOI: 10.1016/j.rico.2025.100623
Eiji Mizutani , Stuart Dreyfus
Backpropagation (BP) is widely employed for training deep neural networks with many layers (or stages). We describe a new efficient BP-based approach to a general multi-point boundary value (MPBV) problem for differential equations. Given an MPBV problem, we transform it via discretization to a discrete-stage problem involving many stages for integration, and then approach it by stage-wise BP-based gradient and Newton methods. Our BP formulas are derived from discrete-stage optimal-control gradient-based methods. Through numerical examples, we demonstrate how easy to implement our new BP-based approach is to MPBV problems, showing that the results are convincing.
{"title":"A new backpropagation approach to multi-point boundary value problems","authors":"Eiji Mizutani , Stuart Dreyfus","doi":"10.1016/j.rico.2025.100623","DOIUrl":"10.1016/j.rico.2025.100623","url":null,"abstract":"<div><div>Backpropagation (BP) is widely employed for training deep neural networks with many layers (or stages). We describe a new efficient BP-based approach to a general multi-point boundary value (MPBV) problem for differential equations. Given an MPBV problem, we transform it via <em>discretization</em> to a discrete-stage problem involving many stages for integration, and then approach it by stage-wise BP-based gradient and Newton methods. Our BP formulas are derived from discrete-stage optimal-control gradient-based methods. Through numerical examples, we demonstrate how easy to implement our new BP-based approach is to MPBV problems, showing that the results are convincing.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"21 ","pages":"Article 100623"},"PeriodicalIF":3.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145417190","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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