Optimal Control Applications and Methods最新文献

An optimal demand side management for microgrid cost minimization considering renewables 考虑到可再生能源的微电网成本最小化的最佳需求侧管理

Optimal Control Applications and Methods Pub Date : 2024-09-19 DOI: 10.1002/oca.3205

Swarupa Pinninti, Srinivasa Rao Sura

{"title":"An optimal demand side management for microgrid cost minimization considering renewables","authors":"Swarupa Pinninti, Srinivasa Rao Sura","doi":"10.1002/oca.3205","DOIUrl":"https://doi.org/10.1002/oca.3205","url":null,"abstract":"In an ordinary microgrid configuration, the required load changes from hour to hour. The power system firms determine the cost of energy at different times of day by considering the highest and lowest points of the consumption curve. This is referred to as time‐of‐use (TOU) pricing for power. The hourly basis load demand is divided into flexible and inflexible categories. Demand side management (DSM) lowers peak demand while rewarding customers for their participation based on load lowering. His rebuilds the whole load model on the pillars of demand cost movement. The research recommends a DSM methodology based on a combined intellect method to lower the total cost of employing loads in a microgrid (MG) structure while considering carbon tax as an unavoidable constraint to lower the emission of pollutants. This is because 40% of microgrid customers are willing to participate in the DSM scheme. The results obtained in each illustration demonstrate that the suggested DSM technique is suitable in terms of cost reduction. The generating cost was decreased from $15,488 to $15,354 when 0%–40% of clients engaged in the DSM programme. With just a 3% compromised increase in generation costs, a carbon price combined with economic emission dispatch reduced the pollutants emitted by up to 78%, from 70 to 15 tons.","PeriodicalId":501055,"journal":{"name":"Optimal Control Applications and Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142257398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Output feedback control of anti‐linear systems using adaptive dynamic programming 利用自适应动态编程实现反线性系统的输出反馈控制

Optimal Control Applications and Methods Pub Date : 2024-09-12 DOI: 10.1002/oca.3203

Li Yu, Hai Wang

引用次数: 0

Reachable set estimation of delayed Markovian jump neural networks based on an augmented zero equality approach 基于增强零等式方法的延迟马尔可夫跃迁神经网络可达集估计

Optimal Control Applications and Methods Pub Date : 2024-09-06 DOI: 10.1002/oca.3206

S. H. Kim, Y. J. Kim, S. H. Lee, O. M. Kwon

引用次数: 0

Intelligent integration of ANN and H‐infinity control for optimal enhanced performance of a wind generation unit linked to a power system 智能集成 ANN 和 H-infinity 控制，优化提升与电力系统相连的风力发电机组的性能

Optimal Control Applications and Methods Pub Date : 2024-09-03 DOI: 10.1002/oca.3199

Mohamed Abd‐El‐Hakeem Mohamed, Salah Kamel, Hamed Zeinoddini‐Meymand

{"title":"Intelligent integration of ANN and H‐infinity control for optimal enhanced performance of a wind generation unit linked to a power system","authors":"Mohamed Abd‐El‐Hakeem Mohamed, Salah Kamel, Hamed Zeinoddini‐Meymand","doi":"10.1002/oca.3199","DOIUrl":"https://doi.org/10.1002/oca.3199","url":null,"abstract":"This article focuses on utilizing intelligent H‐∞ synthesis to create a controller for a wind generation system linked to a power system via a static VAR compensator. The purpose of the control approach is twofold: firstly, to enhance the system's dynamic reactions to turbulent wind variations, and secondly, to elevate the quality of power generation. To achieve optimal control of the system, an Artificial Neural Network (ANN) is combined with the H‐∞ control method. This integration leverages the strengths of both ANN, which excels in modeling and optimization, and H‐∞, which prioritizes robustness to enhance dynamic performance. The resultant control strategy, connecting ANN and H‐∞, demonstrates the capability to deliver superior performance, precise tracking, and minimal overshooting. This approach is adaptive to changing control signals and exhibits robust characteristics, effectively managing uncertainties and disturbances. Through a simulation study, the effectiveness of this presented technique is showcased in enhancing the dynamic response of the system when compared to alternative control strategies.","PeriodicalId":501055,"journal":{"name":"Optimal Control Applications and Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142219640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adaptive neural network dynamic surface optimal saturation control for single‐phase grid‐connected photovoltaic systems 单相并网光伏系统的自适应神经网络动态表面优化饱和控制

Optimal Control Applications and Methods Pub Date : 2024-09-03 DOI: 10.1002/oca.3204

Hongyang Zhang, Tiechao Wang

{"title":"Adaptive neural network dynamic surface optimal saturation control for single‐phase grid‐connected photovoltaic systems","authors":"Hongyang Zhang, Tiechao Wang","doi":"10.1002/oca.3204","DOIUrl":"https://doi.org/10.1002/oca.3204","url":null,"abstract":"An adaptive neural network (NN) based optimal saturation control scheme is investigated for single‐phase grid‐connected photovoltaic (PV) systems by incorporating dynamic surface control (DSC) and adaptive dynamic programming (ADP) based on the backstepping control design framework. For each backstepping step, a critic‐actor architecture is constructed via reinforcement learning (RL), and the PV system is optimized according to the cost function in the architecture. Due to the nonlinearity, it is difficult to solve the Hamilton–Jacobi–Bellman (HJB) equation. The neural networks (NNs) are employed to approximate the solution of the HJB equation such that the optimal virtual control and the actual controller are obtained. By considering control input symmetric saturation nonlinearity link, constraints on pulse width modulation (PWM) are ensured. On this basis, the combination of backstepping control design and dynamic surface technique is used to overcome the shortcomings of “differential explosion” and simplify calculations. Based on the Lyapunov method, the stability analysis proves that all signals of the closed‐loop PV systems are semiglobally uniformly ultimately bounded (SGUUB). Simulation experiments and comparative results are given to verify the efficacy of the studied control strategy.","PeriodicalId":501055,"journal":{"name":"Optimal Control Applications and Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142219638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Taylor‐based smart flower optimization algorithm with the deep residual network to predict mechanical materials properties 基于泰勒的智能花优化算法与深度残差网络用于预测机械材料性能

Optimal Control Applications and Methods Pub Date : 2024-08-28 DOI: 10.1002/oca.3195

Oshin Sharma, Deepak Sharma

{"title":"Taylor‐based smart flower optimization algorithm with the deep residual network to predict mechanical materials properties","authors":"Oshin Sharma, Deepak Sharma","doi":"10.1002/oca.3195","DOIUrl":"https://doi.org/10.1002/oca.3195","url":null,"abstract":"The expedience of materials processing is of great significance and increased the industrial interest in meeting the needs of contemporary engineering applications. The inspection of mechanical properties is extensively explored by scientists, but the prediction of properties with the deep model is limited. This article presents an optimized deep residual network (DRN) to predict mechanical properties of materials. The quantile normalization is applied for improved processing. The DRN is pre‐trained with an optimization model for initializing the best set of attributes and tuning the parameters of the model. Here, Taylor‐Smart Flower Optimization Algorithm (Taylor‐SFOA) is adapted for training DRN by tuning optimum weights. The proposed Taylor‐SFOA helps to effectively offer precise mapping amidst mechanical properties and processing parameters. The optimal features are selected with the Ruzicka and Motyka. The selected features are fused with a dice coefficient to choose distinct features for attaining effective performance. The method yielded better outcomes with improved generalization. The Taylor‐SFOA‐based DRN provided better outcomes with smallest Mean absolute error (MAE) of 0.049, Mean square error (MSE) of 0.116, Root Mean square error (RMSE) of 0.340, memory footprint of 37.700 MB, and training time of 9.633 Sec.","PeriodicalId":501055,"journal":{"name":"Optimal Control Applications and Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142219639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Event‐triggered optimal control based on prescribed performance for a two‐link robotic manipulator 基于双连杆机械手规定性能的事件触发优化控制

Optimal Control Applications and Methods Pub Date : 2024-08-27 DOI: 10.1002/oca.3200

Xinying Chen, Baili Su

引用次数: 0

Experience replay based online adaptive robust tracking control for partially unknown nonlinear systems with asymmetric constrained‐input 基于经验重放的部分未知非线性系统在线自适应鲁棒跟踪控制与非对称约束输入

Optimal Control Applications and Methods Pub Date : 2024-08-26 DOI: 10.1002/oca.3202

Chong Liu, Yalun Li, Zhongxing Duan, Zhousheng Chu, Zongfang Ma

{"title":"Experience replay based online adaptive robust tracking control for partially unknown nonlinear systems with asymmetric constrained‐input","authors":"Chong Liu, Yalun Li, Zhongxing Duan, Zhousheng Chu, Zongfang Ma","doi":"10.1002/oca.3202","DOIUrl":"https://doi.org/10.1002/oca.3202","url":null,"abstract":"This article solves the robust tracking problem (RTP) for a type of partially unknown nonlinear systems with asymmetric constrained‐input by utilizing an improved adaptive dynamic programming (ADP) method based on experience replay (ER) technique and critic‐only neural network (NN). Initially, an identifier neural network (INN) is used to identify the unknown part of the system dynamics. Subsequently, the tracking error and the desired trajectory are used to construct an augmented system, so that the robust tracking problem (RTP) is transformed into a constrained optimal control problem (OCP). It is proved that the designed control policy of OCP can make the tracking error to be uniformly ultimately bounded (UUB). Then, using the framework of ADP and critic‐only NN to solve the derived Hamilton–Jacobi–Bellman equation (HJBE). The NN weight regulation law is partially derived by using gradient descent algorithm (GDA) and then is improved by using the ER technique and the Lyapunov stability theory, which no longer need the conditions of persistence of excitation (PE) and the initial admissible control. Besides, the total system states and NN weights are proved to be closed‐loop stable by utilizing the Lyapunov technique. Finally, through two simulation examples, it is demonstrated that the proposed control scheme is effective.","PeriodicalId":501055,"journal":{"name":"Optimal Control Applications and Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142219642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Self‐organizing cooperative hunting for unmanned surface vehicles with constrained kinematics 具有约束运动学的无人水面飞行器的自组织合作狩猎

Optimal Control Applications and Methods Pub Date : 2024-08-12 DOI: 10.1002/oca.3194

Qun Deng, Yan Peng, Tingke Mo, Jinduo Wang, Dong Qu, Yangmin Xie

引用次数: 0

Model‐based reinforcement learning control of reaction‐diffusion problems 基于模型的强化学习控制反应扩散问题

Optimal Control Applications and Methods Pub Date : 2024-08-08 DOI: 10.1002/oca.3196

Christina Schenk, Aditya Vasudevan, Maciej Haranczyk, Ignacio Romero

{"title":"Model‐based reinforcement learning control of reaction‐diffusion problems","authors":"Christina Schenk, Aditya Vasudevan, Maciej Haranczyk, Ignacio Romero","doi":"10.1002/oca.3196","DOIUrl":"https://doi.org/10.1002/oca.3196","url":null,"abstract":"Mathematical and computational tools have proven to be reliable in decision‐making processes. In recent times, in particular, machine learning‐based methods are becoming increasingly popular as advanced support tools. When dealing with control problems, reinforcement learning has been applied to decision‐making in several applications, most notably in games. The success of these methods in finding solutions to complex problems motivates the exploration of new areas where they can be employed to overcome current difficulties. In this article, we explore the use of automatic control strategies to initial boundary value problems in thermal and disease transport. Specifically, in this work, we adapt an existing reinforcement learning algorithm using a stochastic policy gradient method and we introduce two novel reward functions to drive the flow of the transported field. The new model‐based framework exploits the interactions between a reaction‐diffusion model and the modified agent. The results show that certain controls can be implemented successfully in these applications, although model simplifications had to be assumed.","PeriodicalId":501055,"journal":{"name":"Optimal Control Applications and Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141946346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0