Pub Date : 2024-11-21DOI: 10.1016/j.rico.2024.100501
Nhat M. Nguyen, Minh Tran
This paper proposes a novel framework for optimizing trading indicators using a multi-objective Particle Swarm Optimization approach. By simultaneously optimizing multiple technical indicators, the method overcomes the limitations of single-objective optimization and complex strategies, resulting in a more robust trading approach. Experiments on VN30-Index daily data demonstrate that the optimized strategy outperforms benchmark and buy-and-hold strategies in terms of returns and Sharpe ratios. Our findings prove that the multi-objective Particle Swarm Optimization method efficiently balances the complexity to combine various technical indicators in a way that keeps the logic of the strategy simple. The technique not only reduces the risks of relying on one indicator but also reduces behavioral influences in the stock selection process. Furthermore, our study adds to the literature a simple and effective method that helps traders identify profitable investment opportunities in different market scenarios.
{"title":"Simultaneous multi-objective optimization method for trading indicators","authors":"Nhat M. Nguyen, Minh Tran","doi":"10.1016/j.rico.2024.100501","DOIUrl":"10.1016/j.rico.2024.100501","url":null,"abstract":"<div><div>This paper proposes a novel framework for optimizing trading indicators using a multi-objective Particle Swarm Optimization approach. By simultaneously optimizing multiple technical indicators, the method overcomes the limitations of single-objective optimization and complex strategies, resulting in a more robust trading approach. Experiments on VN30-Index daily data demonstrate that the optimized strategy outperforms benchmark and buy-and-hold strategies in terms of returns and Sharpe ratios. Our findings prove that the multi-objective Particle Swarm Optimization method efficiently balances the complexity to combine various technical indicators in a way that keeps the logic of the strategy simple. The technique not only reduces the risks of relying on one indicator but also reduces behavioral influences in the stock selection process. Furthermore, our study adds to the literature a simple and effective method that helps traders identify profitable investment opportunities in different market scenarios.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"17 ","pages":"Article 100501"},"PeriodicalIF":0.0,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142705808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-19DOI: 10.1016/j.rico.2024.100493
Suvetha R. , Rangarajan K. , Rajadurai P.
The economic vitality of a nation is contingent upon the advancement of its manufacturing sectors, given their pivotal role in fostering economic growth. These industries frequently encounter challenges such as mitigating deterioration rates, enhancing revenue and reducing overall costs to optimize profits. Consequently, should an item deteriorate while in stock within manufacturing facilities, it results in a gradual escalation of holding costs and total expenses. In this paper discusses determining the most effective production policy for items prone to degradation, analyzing depreciating items using three-stage production inventory models with trapezoidal demand to minimize holding costs based on time-dependent factors in the manufacturing sector. This model aims to decrease overall costs and production time periods, contrasting with the higher cost values of the price-based constant method. Mathematical formulas were developed using MATLAB R2023b to validate the models findings and minimize the inventory systems cost.
{"title":"A sustainable three-stage production inventory model with trapezoidal demand and time-dependent holding cost","authors":"Suvetha R. , Rangarajan K. , Rajadurai P.","doi":"10.1016/j.rico.2024.100493","DOIUrl":"10.1016/j.rico.2024.100493","url":null,"abstract":"<div><div>The economic vitality of a nation is contingent upon the advancement of its manufacturing sectors, given their pivotal role in fostering economic growth. These industries frequently encounter challenges such as mitigating deterioration rates, enhancing revenue and reducing overall costs to optimize profits. Consequently, should an item deteriorate while in stock within manufacturing facilities, it results in a gradual escalation of holding costs and total expenses. In this paper discusses determining the most effective production policy for items prone to degradation, analyzing depreciating items using three-stage production inventory models with trapezoidal demand to minimize holding costs based on time-dependent factors in the manufacturing sector. This model aims to decrease overall costs and production time periods, contrasting with the higher cost values of the price-based constant method. Mathematical formulas were developed using MATLAB R2023b to validate the models findings and minimize the inventory systems cost.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"17 ","pages":"Article 100493"},"PeriodicalIF":0.0,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142705912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-19DOI: 10.1016/j.rico.2024.100494
Nadia Bounouara, Mouna Ghanai, Kheireddine Chafaa
In this paper, a continuous–discrete time observer using an optimized high gain is proposed for a robotic manipulator where the output is time sampled. The main contribution of this approach is to improve the value of the high gain that corresponds to the minimum value of the cost function by using some metaheuristic algorithms. The observer is characterized by an optimal high gain that is optimized by biogeography-based optimization (BBO) algorithm, particle swarm optimization (PSO) method and genetic algorithms (GA). Through this investigation, it is proven that the best optimization results are obtained through the process based on the BBO algorithm. BBO is a relatively new nature-inspired optimization algorithm used to find the best and optimal value for an optimization problem. The introduced method is implemented in two steps. In the first step the high gain is optimized in an off-line way by the BBO algorithm. In the second step, the obtained optimal value is inserted on-line in a feedback control loop. The suggested optimized observer is used for two purposes: first it ensures an accurate estimation of state variables that are not physically measurable; despite the presence of disturbances and measurement noises; second it guarantees a stability of the considered system and the convergence of the estimation error. Results of simulated experimentations for robot manipulators are presented in order to demonstrate the performance and effectiveness of the proposed observer optimization.
{"title":"Optimization of a high gain observer for feedback linearization control","authors":"Nadia Bounouara, Mouna Ghanai, Kheireddine Chafaa","doi":"10.1016/j.rico.2024.100494","DOIUrl":"10.1016/j.rico.2024.100494","url":null,"abstract":"<div><div>In this paper, a continuous–discrete time observer using an optimized high gain is proposed for a robotic manipulator where the output is time sampled. The main contribution of this approach is to improve the value of the high gain that corresponds to the minimum value of the cost function by using some metaheuristic algorithms. The observer is characterized by an optimal high gain that is optimized by biogeography-based optimization (BBO) algorithm, particle swarm optimization (PSO) method and genetic algorithms (GA). Through this investigation, it is proven that the best optimization results are obtained through the process based on the BBO algorithm. BBO is a relatively new nature-inspired optimization algorithm used to find the best and optimal value for an optimization problem. The introduced method is implemented in two steps. In the first step the high gain is optimized in an off-line way by the BBO algorithm. In the second step, the obtained optimal value is inserted on-line in a feedback control loop. The suggested optimized observer is used for two purposes: first it ensures an accurate estimation of state variables that are not physically measurable; despite the presence of disturbances and measurement noises; second it guarantees a stability of the considered system and the convergence of the estimation error. Results of simulated experimentations for robot manipulators are presented in order to demonstrate the performance and effectiveness of the proposed observer optimization.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"17 ","pages":"Article 100494"},"PeriodicalIF":0.0,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142705809","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-19DOI: 10.1016/j.rico.2024.100499
Osama Ala’yed
In this study, we develop a modified version of the two-dimensional differential transform (TDDT) method for solving proportional delay partial differential equations (PDPDEs) that frequently arise in engineering and scientific models. This modification is achieved by integrating the TDDT method with the Laplace transform and the Padé approximant, thereby leveraging the strengths of each technique to improve overall performance. Theorems are provided in a general manner to cover various types of PDEs, with constant or variable coefficients. To validate the approach, we apply it to three test problems, demonstrating its effectiveness in extending the convergence domain of the traditional TDDT approach, reducing computational complexity, and yielding analytic solutions with fewer computational steps. Results indicate that the method is a viable alternative for addressing PDPDEs, especially in scenarios where traditional analytic solutions are challenging to obtain. This combination opens new avenues for efficiently solving complex delayed systems in engineering and science, potentially outperforming existing numerical and analytical techniques in both speed and reliability.
{"title":"Modified two-dimensional differential transform method for solving proportional delay partial differential equations","authors":"Osama Ala’yed","doi":"10.1016/j.rico.2024.100499","DOIUrl":"10.1016/j.rico.2024.100499","url":null,"abstract":"<div><div>In this study, we develop a modified version of the two-dimensional differential transform (TDDT) method for solving proportional delay partial differential equations (PDPDEs) that frequently arise in engineering and scientific models. This modification is achieved by integrating the TDDT method with the Laplace transform and the Padé approximant, thereby leveraging the strengths of each technique to improve overall performance. Theorems are provided in a general manner to cover various types of PDEs, with constant or variable coefficients. To validate the approach, we apply it to three test problems, demonstrating its effectiveness in extending the convergence domain of the traditional TDDT approach, reducing computational complexity, and yielding analytic solutions with fewer computational steps. Results indicate that the method is a viable alternative for addressing PDPDEs, especially in scenarios where traditional analytic solutions are challenging to obtain. This combination opens new avenues for efficiently solving complex delayed systems in engineering and science, potentially outperforming existing numerical and analytical techniques in both speed and reliability.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"17 ","pages":"Article 100499"},"PeriodicalIF":0.0,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142705807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-19DOI: 10.1016/j.rico.2024.100495
Vassilios Yfantis , Achim Wagner , Martin Ruskowski
This paper presents a benchmark study of dual decomposition-based distributed optimization algorithms applied to constraint-coupled model predictive control problems. These problems can be interpreted as multiple subsystems which are coupled through constraints on the availability of shared limited resources. In a dual decomposition-based framework the production and consumption of these resources can be coordinated by iteratively computing their prices and sharing them with the involved subsystems. Following a brief introduction to model predictive control different architectures and communication topologies for a distributed setting are presented. After decomposing the system-wide control problem into multiple subproblems by introducing dual variables, several distributed optimization algorithms, including the recently proposed quasi-Newton dual ascent algorithm, are discussed. Furthermore, an epigraph formulation of the bundle cuts as well as a line search strategy are proposed for the quasi-Newton dual ascent algorithm, which increase its numerical robustness and speed up its convergence compared to the previously used trust region. Finally, the quasi-Newton dual ascent algorithm is compared to the subgradient method, the bundle trust method and the alternating direction method of multipliers for a large number of benchmark problems. The used benchmark problems are publicly available on GitHub.
{"title":"Numerical benchmarking of dual decomposition-based optimization algorithms for distributed model predictive control","authors":"Vassilios Yfantis , Achim Wagner , Martin Ruskowski","doi":"10.1016/j.rico.2024.100495","DOIUrl":"10.1016/j.rico.2024.100495","url":null,"abstract":"<div><div>This paper presents a benchmark study of dual decomposition-based distributed optimization algorithms applied to constraint-coupled model predictive control problems. These problems can be interpreted as multiple subsystems which are coupled through constraints on the availability of shared limited resources. In a dual decomposition-based framework the production and consumption of these resources can be coordinated by iteratively computing their prices and sharing them with the involved subsystems. Following a brief introduction to model predictive control different architectures and communication topologies for a distributed setting are presented. After decomposing the system-wide control problem into multiple subproblems by introducing dual variables, several distributed optimization algorithms, including the recently proposed quasi-Newton dual ascent algorithm, are discussed. Furthermore, an epigraph formulation of the bundle cuts as well as a line search strategy are proposed for the quasi-Newton dual ascent algorithm, which increase its numerical robustness and speed up its convergence compared to the previously used trust region. Finally, the quasi-Newton dual ascent algorithm is compared to the subgradient method, the bundle trust method and the alternating direction method of multipliers for a large number of benchmark problems. The used benchmark problems are publicly available on GitHub.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"17 ","pages":"Article 100495"},"PeriodicalIF":0.0,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142705803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-19DOI: 10.1016/j.rico.2024.100496
K. Ramalakshmi , B. Sundara Vadivoo , Kottakkaran Sooppy Nisar , Suliman Alsaeed
This study examines the mathematical model of Hepatitis B Virus (HBV) dynamics, focusing on its various stages of infection, including acute and chronic phases, and transmission pathways. By utilizing mathematical modeling and fractional calculus techniques with the -Hilfer operator, we analyze the epidemic’s behavior. The research proposes control strategies, such as treatment and vaccination, aimed at reducing both acute and chronic infections. To achieve optimal control, we employ Pontryagin’s Maximum Principle. Through simulations, we demonstrate the effectiveness of our approach using the Non-Standard Two-Step Lagrange Interpolation Method (NS2LIM), supported by numerical findings and graphical representations. Additionally, we identify two control variables to minimize the populations of acute and chronic infections while enhancing recovery rates.
本研究探讨了乙型肝炎病毒（HBV）动态的数学模型，重点是其各个感染阶段，包括急性期和慢性期，以及传播途径。通过利用数学建模和带有 Θ-Hilfer 算子的分数微积分技术，我们分析了流行病的行为。研究提出了治疗和疫苗接种等控制策略，旨在减少急性和慢性感染。为了实现最优控制，我们采用了庞特里亚金最大原则（Pontryagin's Maximum Principle）。通过模拟，我们利用非标准两步拉格朗日插值法（NS2LIM）证明了我们方法的有效性，并辅以数值结果和图形表示。此外，我们还确定了两个控制变量，以尽量减少急性和慢性感染人群，同时提高恢复率。
{"title":"The Θ-Hilfer fractional order model for the optimal control of the dynamics of Hepatitis B virus transmission","authors":"K. Ramalakshmi , B. Sundara Vadivoo , Kottakkaran Sooppy Nisar , Suliman Alsaeed","doi":"10.1016/j.rico.2024.100496","DOIUrl":"10.1016/j.rico.2024.100496","url":null,"abstract":"<div><div>This study examines the mathematical model of Hepatitis B Virus (HBV) dynamics, focusing on its various stages of infection, including acute and chronic phases, and transmission pathways. By utilizing mathematical modeling and fractional calculus techniques with the <span><math><mi>Θ</mi></math></span>-Hilfer operator, we analyze the epidemic’s behavior. The research proposes control strategies, such as treatment and vaccination, aimed at reducing both acute and chronic infections. To achieve optimal control, we employ Pontryagin’s Maximum Principle. Through simulations, we demonstrate the effectiveness of our approach using the Non-Standard Two-Step Lagrange Interpolation Method (NS2LIM), supported by numerical findings and graphical representations. Additionally, we identify two control variables to minimize the populations of acute and chronic infections while enhancing recovery rates.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"17 ","pages":"Article 100496"},"PeriodicalIF":0.0,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142705805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-19DOI: 10.1016/j.rico.2024.100491
Ravi Sekhar , Sharnil Pandya , Pritesh Shah , Hemant Ghayvat , Deepak Sharma , Matthias Renz , Deep Shah , Adeeth Jagdale , Devansh Hukmani , Santosh Saxena , Neeraj Kumar
Acoustics based smart condition monitoring is a viable alternative to mechanical vibrations or image-capture based predictive maintenance methods. In this study, a texture analysis based transfer learning methodology was applied to classify tool wear based on the noise generated during mild steel machining. The machining acoustics were converted to spectrogram images and transfer learning was applied for their classification into high/medium/low tool wear using four pre-trained deep learning models (SqueezeNet, ResNet50, InceptionV3, GoogLeNet). Moreover, three optimizers (RMSPROP, ADAM, SGDM) were applied to each of the deep learning models to enhance classification accuracies. Primary results indicate that the InceptionV3-RMSPROP obtained the highest testing accuracy of 87.50%, followed by the SqueezeNet-RMSPROP and ResNet50-SGDM at 75.00% and 62.50% respectively. However, SqueezeNet-RMSPROP was determined to be more desirable from a practical machining quality and safety perspective, owing to its greater recall value for the highest tool wear class. The proposed acoustics-texture extraction-transfer learning approach is especially suitable for cost effective tool wear condition monitoring involving limited datasets.
{"title":"Noise robust classification of carbide tool wear in machining mild steel using texture extraction based transfer learning approach for predictive maintenance","authors":"Ravi Sekhar , Sharnil Pandya , Pritesh Shah , Hemant Ghayvat , Deepak Sharma , Matthias Renz , Deep Shah , Adeeth Jagdale , Devansh Hukmani , Santosh Saxena , Neeraj Kumar","doi":"10.1016/j.rico.2024.100491","DOIUrl":"10.1016/j.rico.2024.100491","url":null,"abstract":"<div><div>Acoustics based smart condition monitoring is a viable alternative to mechanical vibrations or image-capture based predictive maintenance methods. In this study, a texture analysis based transfer learning methodology was applied to classify tool wear based on the noise generated during mild steel machining. The machining acoustics were converted to spectrogram images and transfer learning was applied for their classification into high/medium/low tool wear using four pre-trained deep learning models (SqueezeNet, ResNet50, InceptionV3, GoogLeNet). Moreover, three optimizers (RMSPROP, ADAM, SGDM) were applied to each of the deep learning models to enhance classification accuracies. Primary results indicate that the InceptionV3-RMSPROP obtained the highest testing accuracy of 87.50%, followed by the SqueezeNet-RMSPROP and ResNet50-SGDM at 75.00% and 62.50% respectively. However, SqueezeNet-RMSPROP was determined to be more desirable from a practical machining quality and safety perspective, owing to its greater recall value for the highest tool wear class. The proposed acoustics-texture extraction-transfer learning approach is especially suitable for cost effective tool wear condition monitoring involving limited datasets.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"17 ","pages":"Article 100491"},"PeriodicalIF":0.0,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142705801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-17DOI: 10.1016/j.rico.2024.100497
Yuanshan Liu, Yude Xia
A novel approach is proposed for designing control strategies for time-varying cyber–physical systems (CPSs) with unknown dynamics, eliminating the need for system identification. Combining with the dynamic-triggered strategies (DTSs), the closed-loop system is parameterized using matrices that are depended on data obtained from a collection of input-state trajectories gathered offline. Additionally, the problem of data-driven optimization control is elegantly resolved through the utilization of classical linear quadratic regulator (LQR) technology, showcasing a remarkable innovation by obviating the necessity for the specific mathematical model of CPSs proposed in this paper. A numerical illustration is provided to illustrate these findings.
{"title":"Optimization control of time-varying cyber–physical systems via dynamic-triggered strategies","authors":"Yuanshan Liu, Yude Xia","doi":"10.1016/j.rico.2024.100497","DOIUrl":"10.1016/j.rico.2024.100497","url":null,"abstract":"<div><div>A novel approach is proposed for designing control strategies for time-varying cyber–physical systems (CPSs) with unknown dynamics, eliminating the need for system identification. Combining with the dynamic-triggered strategies (DTSs), the closed-loop system is parameterized using matrices that are depended on data obtained from a collection of input-state trajectories gathered offline. Additionally, the problem of data-driven optimization control is elegantly resolved through the utilization of classical linear quadratic regulator (LQR) technology, showcasing a remarkable innovation by obviating the necessity for the specific mathematical model of CPSs proposed in this paper. A numerical illustration is provided to illustrate these findings.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"17 ","pages":"Article 100497"},"PeriodicalIF":0.0,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142705802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-13DOI: 10.1016/j.rico.2024.100490
Pulak Kundu, Uzzwal Kumar Mallick
Because of its high nutritional value and easy availability, guava fruit has become more popular as a crop in tropical regions in recent decades. Unfortunately, its cultivation faces multifaceted challenges, with increasing the guava borer due to global warming posing a significant threat to crop yield, while the cost of pesticides adds to the economic burden on farmers. To overcome this difficulty, an integrated cultivation method has been devised to simultaneously cultivate guava and tuberose flowers for the purpose of biological pest management, while also earning money through the sale of the flowers to support the integrated agricultural plan. The present mathematical modeling study has focused on the optimal control problem using the strategy of spraying pesticides and flower harvesting, to achieve the highest possible profit. Characterization of the proposed optimal control was then carried out using Pontryagin’s maximum principle, where the objective of our farming would be higher when optimal management of our strategies would be provided compared to other scenarios. To find the most efficient and least expensive approach, cost-effectiveness analysis has been performed. According to the findings, an optimal strategy for harvesting flowers is the most economical and efficient way to increase the amount of earnings from this integrated farming. However, the results of this study can help the farmers in developing beneficial strategies to gain maximum profit by reducing the cost.
{"title":"Optimal control analysis of a mathematical model for guava nutrients in an integrated farming with cost-effectiveness","authors":"Pulak Kundu, Uzzwal Kumar Mallick","doi":"10.1016/j.rico.2024.100490","DOIUrl":"10.1016/j.rico.2024.100490","url":null,"abstract":"<div><div>Because of its high nutritional value and easy availability, guava fruit has become more popular as a crop in tropical regions in recent decades. Unfortunately, its cultivation faces multifaceted challenges, with increasing the guava borer due to global warming posing a significant threat to crop yield, while the cost of pesticides adds to the economic burden on farmers. To overcome this difficulty, an integrated cultivation method has been devised to simultaneously cultivate guava and tuberose flowers for the purpose of biological pest management, while also earning money through the sale of the flowers to support the integrated agricultural plan. The present mathematical modeling study has focused on the optimal control problem using the strategy of spraying pesticides and flower harvesting, to achieve the highest possible profit. Characterization of the proposed optimal control was then carried out using Pontryagin’s maximum principle, where the objective of our farming would be higher when optimal management of our strategies would be provided compared to other scenarios. To find the most efficient and least expensive approach, cost-effectiveness analysis has been performed. According to the findings, an optimal strategy for harvesting flowers is the most economical and efficient way to increase the amount of earnings from this integrated farming. However, the results of this study can help the farmers in developing beneficial strategies to gain maximum profit by reducing the cost.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"17 ","pages":"Article 100490"},"PeriodicalIF":0.0,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-12DOI: 10.1016/j.rico.2024.100488
Redouane Chaibi , Rachid EL Bachtiri , Karima El Hammoumi , Mohamed Yagoubi
To improve the efficiency and performance of a photovoltaic system (PV) an observer-based fuzzy controller design methodology is provided in the study. The desired controller is achieved by employing a combination of linear matrix inequalities (LMIs). The system consists of a photovoltaic generator (PVG) connected to a boost converter. A battery is linked to the boost converter to stock additional energy for further use. A fuzzy controller based on a T–S fuzzy type observer that guarantees a predefined performance is suggested to achieve maximum power point tracking (MPPT) even under changing weather conditions. An optimal trajectory should be tracked to ensure maximum power operation. For this aim, a specific reference fuzzy model is proposed to create the aimed trajectories. Using this method, the system dynamics are precisely reproduced over a large range of operations. The whole T–S fuzzy methodology, suggested in this paper, aims to ensure the most efficient energy recovery to recharge a battery under partially shaded conditions, resulting in high system efficiency. The proposed method is simulated with MATLAB /SIMULINK and the simulation results, with realistic reference trajectories, are driven while taking into account climate variations. The analysis of these simulations, along with a comparison with two other commonly used approaches, led to the conclusion that the suggested strategy succeeded in reducing the tracking time, as well as eliminating the oscillation that often occurs around maximum power point (MPP).
{"title":"Observer-based fuzzy T–S control with an estimation error guarantee for MPPT of a photovoltaic battery charger in partial shade conditions","authors":"Redouane Chaibi , Rachid EL Bachtiri , Karima El Hammoumi , Mohamed Yagoubi","doi":"10.1016/j.rico.2024.100488","DOIUrl":"10.1016/j.rico.2024.100488","url":null,"abstract":"<div><div>To improve the efficiency and performance of a photovoltaic system (PV) an observer-based fuzzy controller design methodology is provided in the study. The desired controller is achieved by employing a combination of linear matrix inequalities (LMIs). The system consists of a photovoltaic generator (PVG) connected to a boost converter. A battery is linked to the boost converter to stock additional energy for further use. A fuzzy controller based on a T–S fuzzy type observer that guarantees a predefined <span><math><msub><mrow><mi>L</mi></mrow><mrow><mn>2</mn></mrow></msub></math></span> performance is suggested to achieve maximum power point tracking (MPPT) even under changing weather conditions. An optimal trajectory should be tracked to ensure maximum power operation. For this aim, a specific reference fuzzy model is proposed to create the aimed trajectories. Using this method, the system dynamics are precisely reproduced over a large range of operations. The whole T–S fuzzy methodology, suggested in this paper, aims to ensure the most efficient energy recovery to recharge a battery under partially shaded conditions, resulting in high system efficiency. The proposed method is simulated with MATLAB<!--> <!-->/SIMULINK <!--> <!-->and the simulation results, with realistic reference trajectories, are driven while taking into account climate variations. The analysis of these simulations, along with a comparison with two other commonly used approaches, led to the conclusion that the suggested strategy succeeded in reducing the tracking time, as well as eliminating the oscillation that often occurs around maximum power point (MPP).</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"17 ","pages":"Article 100488"},"PeriodicalIF":0.0,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662667","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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