Results in Control and Optimization最新文献

{"title":"Optimal control analysis of a mathematical model for guava nutrients in an integrated farming with cost-effectiveness","authors":"Pulak Kundu,&nbsp;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}

{"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 ,&nbsp;Rachid EL Bachtiri ,&nbsp;Karima El Hammoumi ,&nbsp;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}

{"title":"Satellite imagery, big data, IoT and deep learning techniques for wheat yield prediction in Morocco","authors":"Abdelouafi Boukhris,&nbsp;Antari Jilali,&nbsp;Abderrahmane Sadiq","doi":"10.1016/j.rico.2024.100489","DOIUrl":"10.1016/j.rico.2024.100489","url":null,"abstract":"<div><div>In the domain of efficient management of resources and ensuring nutritional consistency, accuracy in predicting crop yields becomes crucial. The advancement of artificial intelligence techniques, synchronized with satellite imagery, has emerged as a potent approach for forecasting crop yields in modern times. We used two types of data: spatial data and temporal data. Spatial data are gathered from satellite imagery and processed using ArcGIS to extract data about crops based on several indices like NDVI and NWDI. Temporal data are gathered from agricultural sensors such as temperature sensors, rainfall sensor, precipitation sensor and soil moisture sensor. In our case we used Sentinel 2 satellite to extract vegetation indices. We have used IoT systems, especially Raspberry Pi <em>B</em>+ to collect and process data coming from sensors. All data collected are then stored into a NoSQL server to be analysed and processed. Several machine learning and deep learning algorithms have been used for the processing of crop recommendation system, such as logistic regression, KNN, decision tree, support vector machine, LSTM, and Bi-LSTM through the collected dataset. We used GRU deep learning model for the best performance, the RMSE and R² for this model was 0.00036 and 0.99 respectively.</div><div>The main contribution of our paper is the development of a new system that can predict several crop yields, such as wheat, maize, etc., using IoT, satellite imagery for spatial data and the use of sensors for temporal data. We are the first paper that has combined spatial data and temporal data to predict crop yield based on deep learning algorithms, unlike other works that uses only remote sensing data or temporal data. We created an E-monitoring crop yield prediction system that helps farmers track all information about crops and show the result of prediction in a mobile application. This system helps farmers with more efficient decision making to enhance crop production. The main production regions of wheat in Morocco are in the rainfed areas of the plains and plateaus of Chaouia, Abda, Haouz, Tadla, Gharb and Saïs. We studied three main regions well known for wheat production which are Rabat-Salé, Fez-Meknes, Casablanca-Settat.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"17 ","pages":"Article 100489"},"PeriodicalIF":0.0,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662666","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}

{"title":"Selective opposition based constrained barnacle mating optimization: Theory and applications","authors":"Marzia Ahmed ,&nbsp;Mohd Herwan Sulaiman ,&nbsp;Md. Maruf Hassan ,&nbsp;Md. Atikur Rahaman ,&nbsp;Masuk Abdullah","doi":"10.1016/j.rico.2024.100487","DOIUrl":"10.1016/j.rico.2024.100487","url":null,"abstract":"<div><div>Mathematical models of Barnacle Mating Optimization (BMO) are based on observations of real-world barnacle mating behaviors such as sperm casting and self-fertilization. Nevertheless, BMO considers penis length to produce new offspring through pseudo-copulated mating behavior, with no constraints like strong wave motion, food availability, or wind direction considered. Exploration and exploitation are two crucial optimization stages as we implement the constrained BMO. They are informed by models of navigational sperm casting properties, food availability, food attractiveness, wind direction, and intertidal zone wave movement experienced by barnacles during mating. We will later integrate opposition-based learning (OBL) with constrained BMO (C-BMO) to improve its exploratory behavior while retaining a quick convergence rate. Rather than opposing all barnacle dimensions, we just opposed those that went over the border. In addition to increasing efficiency by cutting down on wasted time spent exploring, this also increases the likelihood of stumbling onto optimal solutions. After that, it is put through its paces in a real-world case study, where it proves to be superior to the most cutting-edge algorithms available.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"17 ","pages":"Article 100487"},"PeriodicalIF":0.0,"publicationDate":"2024-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142538416","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}

{"title":"Comparative exploration on EEG signal filtering using window control methods","authors":"Aruna Pant ,&nbsp;Adesh Kumar ,&nbsp;Chaman Verma ,&nbsp;Zoltán Illés","doi":"10.1016/j.rico.2024.100485","DOIUrl":"10.1016/j.rico.2024.100485","url":null,"abstract":"<div><div>Electroencephalography (EEG) is used to monitor brain activity. The brain signals consist of different frequency band signals delta, theta, alpha, beta, and gamma waves. The signals are affected by external noise which reduces the quality of the EEG signal due to which it becomes difficult to do further processing of EEG signals like feature extraction or extraction of meaningful features from EEG signal. Therefore, it becomes important to filter the noise from the EEG signal before feature extraction or classification of the EEG signal. The research article presents an overview of different types of windowing filter techniques like Rectangular, Bartlett, Hamming, Hanning, and Kaiser windows applied for finite impulse response (FIR) behavior which is used for EEG signal processing for different brain waves processed in different frequency bands. The comparative analysis is carried out in terms of the response time of brain frequency bands for different windowing filter techniques using the MATLAB 2023 signal processing simulation tool. The novelty of the work lies in estimating minimum latency and appropriate filter selection for various typical EEG waves, since the EEG signals are pre-supposed in the hardware chip design, noise elimination is the first step in high-performance computing applications. The Bartlett window band stop has an optimal response time of 12.666 s for delta waves, a highpass filter with a response time of 16.187 s for theta waves, a bandpass with a response time of 13.122 s for alpha waves, a highpass filter with a response time of 17.866 s for beta waves, and a highpass filter with a response time of 13.797 s for gamma waves. The Barlett window FIR filter is well-suited for EEG applications.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"17 ","pages":"Article 100485"},"PeriodicalIF":0.0,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142531384","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}

{"title":"Attitude control of UAV bicopter using adaptive LQG","authors":"Fahmizal ,&nbsp;Hanung Adi Nugroho ,&nbsp;Adha Imam Cahyadi ,&nbsp;Igi Ardiyanto","doi":"10.1016/j.rico.2024.100484","DOIUrl":"10.1016/j.rico.2024.100484","url":null,"abstract":"<div><div>This paper aims to design a controller that is able to maintain the stability of the unmanned aerial vehicle (UAV) bicopter attitude when carrying a payload. When the value of the payload inertia is in uncertainty, it is necessary to design a controller that can carry out the adaptation process. This paper proposes an Linear Quadratic Gaussian (LQG) adaptive controller to control the attitude of the bicopter with uncertain payload conditions. The proposed adaptive mechanism is a development of LQG control that can follow the response of the reference model. The success of LQG adaptive control is tested by providing uncertain payload parameters. The simulation results show that the LQG adaptive controller successfully overcomes the influence of inertial disturbances originating from the payload. There is a gain <span><math><mi>ρ</mi></math></span> in the LQG adaptive mechanism, this gain is influenced by the parameter <span><math><mi>σ</mi></math></span> which acts as a learning rate that produces a response to adapt to the response of the reference model. From the test results obtained when the value of <span><math><mi>σ</mi></math></span> is enlarged there is an increased overshoot condition/value but the root mean square error (RMSE) value decreases. That means when the RMSE decreases, the response is getting closer to the model reference. To reduce the overshoot effect of increasing the value of <span><math><mi>σ</mi></math></span>, an improvement is made in the search for the gain value of <span><math><mi>ρ</mi></math></span>. From the test results, the value of <span><math><mrow><mi>σ</mi><mo>=</mo><mn>1</mn></mrow></math></span> was chosen with the development of the gain equation <span><math><mi>ρ</mi></math></span>.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"17 ","pages":"Article 100484"},"PeriodicalIF":0.0,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142531383","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}

{"title":"Optimality results for nondifferentiable multiobjective fractional programming problems under E-B-invexity","authors":"Dhruv Singh ,&nbsp;Shashi Kant Mishra ,&nbsp;Pankaj Kumar ,&nbsp;Abdelouahed Hamdi","doi":"10.1016/j.rico.2024.100486","DOIUrl":"10.1016/j.rico.2024.100486","url":null,"abstract":"<div><div>In this article, we study nonconvex multiobjective fractional programming problems involving E-differentiable functions (MFP<span><math><msub><mrow></mrow><mrow><mi>E</mi></mrow></msub></math></span>). We establish the E-Karush–Kuhn–Tucker (E-KKT) sufficient E-optimality conditions for nonsmooth vector optimization problems under the assumption of E-B-invexity. To demonstrate the validity of the derived results, we provide an example where the involved functions exhibit E-B-invexity.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"17 ","pages":"Article 100486"},"PeriodicalIF":0.0,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442171","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}

{"title":"Hybridized Brazilian–Bowein type spectral gradient projection method for constrained nonlinear equations","authors":"Jitsupa Deepho ,&nbsp;Abdulkarim Hassan Ibrahim ,&nbsp;Auwal Bala Abubakar ,&nbsp;Maggie Aphane","doi":"10.1016/j.rico.2024.100483","DOIUrl":"10.1016/j.rico.2024.100483","url":null,"abstract":"<div><div>This paper proposes a hybridized Brazilian and Bowein derivative-free spectral gradient projection method for solving systems of convex-constrained nonlinear equations. The method avoids solving any subproblems in each iteration. Global convergence is established under appropriate assumptions on the functions involved. Additionally, numerical experiments are conducted to evaluate the algorithm’s performance, providing evidence of its efficiency compared to similar algorithms from the existing literature. The results demonstrate that the method outperforms some existing approaches in terms of the number of iterations, function evaluations, and time required to obtain a solution based on the examples considered.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"17 ","pages":"Article 100483"},"PeriodicalIF":0.0,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142445019","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}

{"title":"Spatiotemporal dynamics of toxin producing phytoplankton–zooplankton interactions with Holling Type II functional responses","authors":"Randhir Singh Baghel","doi":"10.1016/j.rico.2024.100478","DOIUrl":"10.1016/j.rico.2024.100478","url":null,"abstract":"<div><div>In this paper, we have studied the spatiotemporal dynamics of phytoplankton-zooplankton interactions using toxin-producing phytoplankton (TPP) with Holling type II functional responses. Toxin-producing phytoplankton (TPP) diffuses and reduces the grazing pressure on zooplankton. The grazing pressure of zooplankton is lessened by toxin-producing phytoplankton (TPP). The temporal system identifies all equilibrium points. Boundedness and local stability are established under specific parametric conditions. The conditions for the existence of a Hopf-bifurcation at the positive equilibrium by taking the half-saturation constant (<span><math><msub><mrow><mi>b</mi></mrow><mrow><mn>1</mn></mrow></msub></math></span>), are also discussed. In a spatial system, we explored Turing instability conditions and patterns with an emphasis on the effect of diffusion variation. Furthermore, we obtained the time evaluation pattern formation of the spatial system. Moreover, the communities of toxin-producing phytoplankton (TPP) are essential to the marine ecosystem because they lessen the mortality of zooplankton caused by grazing pressure. Finally, the basic outcomes are mentioned along with numerical results to provide some support to the analytical findings.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"17 ","pages":"Article 100478"},"PeriodicalIF":0.0,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142422177","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}

{"title":"Linear fractional transportation problem in bipolar fuzzy environment","authors":"Nilima Akhtar,&nbsp;Sahidul Islam","doi":"10.1016/j.rico.2024.100482","DOIUrl":"10.1016/j.rico.2024.100482","url":null,"abstract":"<div><div>This article proposes a solution methodology for the Linear Fractional Transportation Problem (LFTP) by incorporating bipolar fuzzy sets (BFSs) to accommodate both positive and negative judgmental perspectives. The approach explores Zimmermann's extension within bipolar fuzzy environment to compare outcomes. In this context, the cost function and constraint coefficients are depicted using interval-valued trapezoidal bipolar fuzzy numbers (IVTrBFNs) and defuzzified by (s, t)-cut. The initial approach employs the simplex method and fuzzy optimization method, renowned for its effectiveness in obtaining optimal solutions. In the alternative method, Bipolar fuzzy programming approach (BFPA) is utilized for better outcome. In this method the LFTP is altered to a Multi-Objective Transportation Problem (MOTP), and BFPA extends Zimmermann's technique under suitable positive and negative membership functions, converting MOTP to a one-objective transportation problem (TP) and solved using LINGO software. Supporting this proposed method some theorems are formulated to demonstrate that the most effective solution of the single-objective TP is a Pareto optimal solution for the corresponding MOTP. A quantitative example is provided for better understanding of the proposed BFPA method alongside two other approaches.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"17 ","pages":"Article 100482"},"PeriodicalIF":0.0,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142422252","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}