Optimal Control Applications and Methods最新文献_第6页

A numerical method for ℋ2$$ {mathscr{H}}_2 $$ control of linear delay systems 线性时滞系统的h 2 $$ {mathscr{H}}_2 $$控制的数值方法

Optimal Control Applications and Methods Pub Date : 2023-07-07 DOI: 10.1002/oca.3027

Renhong Hu, Jie Mei, G. Ma

引用次数: 0

Attack‐defense differential game to strength allocation strategies generation 攻防差异博弈的力量分配策略生成

Optimal Control Applications and Methods Pub Date : 2023-07-06 DOI: 10.1002/oca.3035

Lingwei Li, Bing Xiao, S. Su, Haichao Zhang, Xiwei Wu, Yiming Guo

{"title":"Attack‐defense differential game to strength allocation strategies generation","authors":"Lingwei Li, Bing Xiao, S. Su, Haichao Zhang, Xiwei Wu, Yiming Guo","doi":"10.1002/oca.3035","DOIUrl":"https://doi.org/10.1002/oca.3035","url":null,"abstract":"This paper addresses a difficult problem of strength allocation strategies generation with various adversaries and complex factors. Firstly, to investigate the strength allocation strategies generation problem, an attack‐defense differential game problem is formulated based on an improved Lanchester equation. Secondly, a numerical method, multi‐intervals simultaneous orthogonal collocation decomposition (MISOCD) method, is proposed to obtain the strength allocation strategies from the constructed model. Compared with the analytical method, MISOCD does not need to derive the necessary conditions. Thirdly, this study designs an approximated solution generation strategy based on adaptive learning pigeon‐inspired optimization algorithm to pregenerate the approximated strength allocation strategies in order to solve the initial value sensitivity problem. The approximated strategies are then used as the initial value guess of MISOCD method to generate optimal strength allocation strategies. Finally, two attack‐defense numerical simulations verify the effectiveness of strength allocation strategies generated by the proposed approach. Our proposed results provide a theoretical guide for both making attack‐defense strength allocation strategies and assessing confrontation actions.","PeriodicalId":105945,"journal":{"name":"Optimal Control Applications and Methods","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125894634","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 event‐triggering mechanism‐based N‐step predictive load frequency control for power systems with cyber attack 基于自适应事件触发机制的网络攻击电力系统N阶预测负荷频率控制

Optimal Control Applications and Methods Pub Date : 2023-07-06 DOI: 10.1002/oca.3033

Yuehua Wu, Xiaoming Tang, Jialiang Wang, Hongchun Qu

{"title":"Adaptive event‐triggering mechanism‐based N‐step predictive load frequency control for power systems with cyber attack","authors":"Yuehua Wu, Xiaoming Tang, Jialiang Wang, Hongchun Qu","doi":"10.1002/oca.3033","DOIUrl":"https://doi.org/10.1002/oca.3033","url":null,"abstract":"Although there have already been many works on the model predictive control (MPC) for load frequency control (LFC) of modern power systems, limited works can be found in the literature related to the output feedback MPC. This article studies an N‐step synthesis approach of output feedback MPC for LFC systems considering the problems of communication efficiency and network security. First, to improve the communication efficiency, an adaptive event‐triggering (AET) scheme involving two adaptive laws is designed to reduce the number of transmitted data packages which offers more flexible compared with existing event‐triggering schemes; Second, to handle the network security, a new model of LFC power system combining the AET scheme and random deception attack under an unified framework is established; Moreover, a synthesis approach of output feedback MPC with N‐step strategy is addressed for LFC of power system by parameterizing the infinite control moves into a series of output feedback laws. Compared with the existing predictive load frequency control methods, the present technique is shown as an useful way to improve the control performance since more degrees freedom is introduced by the N‐step strategy. Finally, the simulation experiment is carried out to verify our technique.","PeriodicalId":105945,"journal":{"name":"Optimal Control Applications and Methods","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124944058","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

Space−time spectral approximations of a parabolic optimal control problem with an L2‐norm control constraint 具有L2范数控制约束的抛物型最优控制问题的时空谱逼近

Optimal Control Applications and Methods Pub Date : 2023-07-04 DOI: 10.1002/oca.3022

Zhenzhen Tao, Bing Sun

引用次数: 0

The numerical treatment of fractal‐fractional 2D optimal control problems by Müntz–Legendre polynomials 用m<s:1> ntz - legendre多项式数值处理分形-分数形二维最优控制问题

Optimal Control Applications and Methods Pub Date : 2023-07-02 DOI: 10.1002/oca.3024

P. Rahimkhani, Y. Ordokhani, S. Sedaghat

引用次数: 0

Optimal allocation of distributed generation for power flow enhancement in distribution network 配电网中增强潮流的分布式发电优化配置

Optimal Control Applications and Methods Pub Date : 2023-07-01 DOI: 10.1002/oca.3031

Srividhya Jayaraman Panneerselvam, Yogitha Krishnamoorthi, Suresh Ramamoorthy, Jaisiva Selvaraj

{"title":"Optimal allocation of distributed generation for power flow enhancement in distribution network","authors":"Srividhya Jayaraman Panneerselvam, Yogitha Krishnamoorthi, Suresh Ramamoorthy, Jaisiva Selvaraj","doi":"10.1002/oca.3031","DOIUrl":"https://doi.org/10.1002/oca.3031","url":null,"abstract":"This article articulates the enhancement of voltage profiles in transmission systems by reducing the voltage deviation and system power losses. Electric power flows uniformly throughout Regional Distribution Networks in the power grid station, resulting in low voltage consistency, significant voltage dips and power losses in the distribution network, highlighting high R/X ratios even with a heavy load or power failure issues, despite installing distributed generation in the radial distribution system for enhancing voltage profile in the grid network. The meta‐heuristic optimization algorithm plays a vital role in determining optimal location of DG for attaining the objective of research. In the practical network, a single objective optimization strategy cannot be employed to solve of power system optimization problems of all kinds. Hence multi‐objective function needs to be addressed. The prime task of this research is to investigate and allocate an optimal location to connect the Distributed Generation, with evaluation of the best DG configuration, with minimized power losses and improved voltage profile of the distribution network using the Hummingbird Optimization Algorithm. A standard IEEE 37‐bus Radial Distribution system is employed as the test bus system to test the performance and effectiveness of the optimization technique. To show the stiffness of the proposed algorithm the results are compared with various optimization approaches that employ similar objective functions.","PeriodicalId":105945,"journal":{"name":"Optimal Control Applications and Methods","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121515973","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

Solvability of general fully coupled forward–backward stochastic difference equations with delay and applications 一般正-倒向时滞全耦合随机差分方程的可解性及其应用

Optimal Control Applications and Methods Pub Date : 2023-06-28 DOI: 10.1002/oca.3023

Teng Song

引用次数: 0

A modified reptile search algorithm for parametric estimation of fractional order model of lithium battery 锂电池分数阶模型参数估计的改进爬行动物搜索算法

Optimal Control Applications and Methods Pub Date : 2023-06-28 DOI: 10.1002/oca.3034

Jie Ding, Shimeng Huang, Yuefei Hao, Min Xiao

引用次数: 0

Synchronizing lockdown and vaccination policies for COVID‐19: An optimal control approach based on piecewise constant strategies COVID - 19的封锁和疫苗接种策略同步:基于分段常数策略的最优控制方法

Optimal Control Applications and Methods Pub Date : 2023-06-28 DOI: 10.1002/oca.3032

Gabriel A. Salcedo‐Varela, F. Peñuñuri, D. González‐Sánchez, Saúl Díaz-Infante

引用次数: 0

Model‐free optimal tracking over finite horizon using adaptive dynamic programming 基于自适应动态规划的有限视界无模型最优跟踪

Optimal Control Applications and Methods Pub Date : 2023-06-27 DOI: 10.1002/oca.3028

M. Jha, D. Theilliol, P. Weber

{"title":"Model‐free optimal tracking over finite horizon using adaptive dynamic programming","authors":"M. Jha, D. Theilliol, P. Weber","doi":"10.1002/oca.3028","DOIUrl":"https://doi.org/10.1002/oca.3028","url":null,"abstract":"Adaptive dynamic programming (ADP) based approaches are effective for solving nonlinear Hamilton–Jacobi–Bellman (HJB) in an approximative sense. This paper develops a novel ADP‐based approach, in that the focus is on minimizing the consecutive changes in control inputs over a finite horizon to solve the optimal tracking problem for completely unknown discrete time systems. To that end, the cost function considers within its arguments: tracking performance, energy consumption and as a novelty, consecutive changes in the control inputs. Through suitable system transformation, the optimal tracking problem is transformed to a regulation problem with respect to state tracking error. The latter leads to a novel performance index function over finite horizon and corresponding nonlinear HJB equation that is solved in an approximative iterative sense using a novel iterative ADP‐based algorithm. A suitable Neural network‐based structure is proposed to learn the initial admissible one step zero control law. The proposed iterative ADP is implemented using heuristic dynamic programming technique based on actor‐critic Neural Network structure. Finally, simulation studies are presented to illustrate the effectiveness of the proposed algorithm.","PeriodicalId":105945,"journal":{"name":"Optimal Control Applications and Methods","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133716334","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: 1