Optimal Control Applications and Methods最新文献_第8页

A combined adaptive entropy‐TOPSIS and model predictive control strategy for mixed loading and delay operations in the reheating furnace 基于自适应熵- TOPSIS和模型预测相结合的加热炉混合加载和延迟控制策略

Optimal Control Applications and Methods Pub Date : 2023-05-02 DOI: 10.1002/oca.3004

Zhi Yang, Xiaochuan Luo, Jinwei Qiao

引用次数: 0

Impact of emission and service constraint for an imperfect production system under two level Hamiltonian 两能级哈密顿量下不完全生产系统排放和服务约束的影响

Optimal Control Applications and Methods Pub Date : 2023-04-30 DOI: 10.1002/oca.3003

Subhajit Das, Hachen Ali, A. Shaikh, A. K. Bhunia

{"title":"Impact of emission and service constraint for an imperfect production system under two level Hamiltonian","authors":"Subhajit Das, Hachen Ali, A. Shaikh, A. K. Bhunia","doi":"10.1002/oca.3003","DOIUrl":"https://doi.org/10.1002/oca.3003","url":null,"abstract":"Considering the impact of emission on environment, over the last few decades, reduction of emission during production process (or emission free production process) appears to be one of the most growing preferences for every manufacturing firm. Again, manufacturing companies of different products are bounded to offer various facilities to their consumers in order to survive in the current highly competitive market situation. Primarily considering these two factors taken in to account, this study addresses an imperfect manufacturing inventory model in which customers' demand increases linearly w. r. to emission reduction level, service level from the manufacturer and decreases nonlinearly w. r. to price of the items. The analytical expressions of production rates, emission levels and service levels are derived by solving a number of optimum control problems using Hamiltonian maximum principle with a values for the business period and selling price. The basic goal is to maximize the average profit of the system. In addition, a number of numerical examples are considered to elaborate four distinct instances involved in the proposed model and these are solved numerically using teaching learning based optimization algorithm. Finally, sensitivity experiment is performed to investigate the influence of various system factors on the best‐found policies. The sensitivity analysis indicates that service rate provided by the manufacturer and selling price of the items impose a huge impact on the average profit of the system. Further, the carbon reduction affects the revenue of the system less significantly.","PeriodicalId":105945,"journal":{"name":"Optimal Control Applications and Methods","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128482284","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: 3

Robust weighted fusion Kalman estimators for systems with uncertain noise variances, multiplicative noises, missing measurements, packets dropouts and two‐step random measurement delays 具有不确定噪声方差、乘性噪声、缺失测量、包丢失和两步随机测量延迟的系统的鲁棒加权融合卡尔曼估计

Optimal Control Applications and Methods Pub Date : 2023-04-25 DOI: 10.1002/oca.3002

Chunshan Yang, Ying Zhao, Zheng Liu, Jianqi Wang

{"title":"Robust weighted fusion Kalman estimators for systems with uncertain noise variances, multiplicative noises, missing measurements, packets dropouts and two‐step random measurement delays","authors":"Chunshan Yang, Ying Zhao, Zheng Liu, Jianqi Wang","doi":"10.1002/oca.3002","DOIUrl":"https://doi.org/10.1002/oca.3002","url":null,"abstract":"For multisensor networked systems with uncertain noise variances, multiplicative noises and multiple networked‐induced uncertainties including missing measurements, packets dropouts and two‐step random measurement delays, the robust weighted fusion estimation problem is addressed in this article. More precisely, the system noise variances are assumed to be uncertain but bounded, the other four uncertainties are compensated into fictitious white noise by the proposed model transformation method, which includes the augmented method and extended fictitious noise technique. Then local multi‐model system is obtained, for which robust local Kalman estimator is obtained based on the minimax robust estimation principle and unified estimation method. Based on this, the six robust weighted fusion time‐varying Kalman estimators are presented in a unified form, which include robust weighted fusers weighted by matrices, diagonal, scalars, and a robust covariance intersection (CI) fuser and two fast CI (FCI) fusers. The robustness proving method, including the extended Lyapunov equation approach with two kinds of generalized Lyapunov equations, non‐negative matrix factorization and elementary transformation of matrix, is presented to prove that the actual estimation error variances are guaranteed to have minimal upper bounds for all admissible uncertainties. The accuracy relations are proved. Further, the robust local and fused steady‐state Kalman estimators are presented. Finally, a simulation example applied to Internet‐based three tank water system is given to demonstrate effectiveness of the proposed results.","PeriodicalId":105945,"journal":{"name":"Optimal Control Applications and Methods","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125484574","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

Reinforcement learning‐based optimized backstepping control for strict‐feedback nonlinear systems subject to external disturbances 外部扰动下严格反馈非线性系统的强化学习优化反步控制

Optimal Control Applications and Methods Pub Date : 2023-04-24 DOI: 10.1002/oca.3001

Y. Qin, Liang Cao, Qing Lu, Yingnan Pan

{"title":"Reinforcement learning‐based optimized backstepping control for strict‐feedback nonlinear systems subject to external disturbances","authors":"Y. Qin, Liang Cao, Qing Lu, Yingnan Pan","doi":"10.1002/oca.3001","DOIUrl":"https://doi.org/10.1002/oca.3001","url":null,"abstract":"This article investigates a reinforcement learning‐based optimal backstepping control strategy for strict‐feedback nonlinear systems, which contain output constraints, external disturbances and uncertain unknown dynamics. The simplified reinforcement learning algorithm with the identifier‐critic‐actor architecture is constructed in the control design to build optimal virtual and actual controllers. To compensate for the disturbance, a lemma is adopted to transform external disturbances into an unknown “bounding functions‘’, which satisfy a triangular condition. Moreover, the unknown nonlinear functions, which composed of unknown dynamics and external disturbances, approximated by neural networks. Meanwhile, in order to avoid violating output constraints, a barrier‐type Lyapunov function approach is integrated into the optimal control strategy to satisfy output constraints requirements under the framework of backstepping technique. Furthermore, the presented optimal control strategy guarantees that all signals in the closed‐loop system are semi‐globally uniformly ultimately bounded. Finally, the effectiveness of the proposed optimal control approach is performed by a numerical example.","PeriodicalId":105945,"journal":{"name":"Optimal Control Applications and Methods","volume":"40 37","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133390076","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

Optimal control under nonconvexity: A generalized Hamiltonian approach 非凸性下的最优控制:一种广义哈密顿方法

Optimal Control Applications and Methods Pub Date : 2023-04-20 DOI: 10.1002/oca.2998

J. Chavas

引用次数: 0

Robust formation control of uncertain linear multi‐agent systems in presence of time‐delay using output information 存在时滞的不确定线性多智能体系统鲁棒群体控制

Optimal Control Applications and Methods Pub Date : 2023-04-16 DOI: 10.1002/oca.2999

Arnab Pal, Asim Kr. Naskar

{"title":"Robust formation control of uncertain linear multi‐agent systems in presence of time‐delay using output information","authors":"Arnab Pal, Asim Kr. Naskar","doi":"10.1002/oca.2999","DOIUrl":"https://doi.org/10.1002/oca.2999","url":null,"abstract":"A major challenge in multi‐agent formation is the issue of delay and uncertainty. This article investigates a robust formation control problem for linear multi‐agent systems with input delay and model uncertainty. The strategy adopted by an agent in the system aims to predict the delayed state using output information from neighboring agents over a fixed communication network and generate the control input from the predictor output. The predictor is employed using the finite spectrum assignment (FSA) technique. The overall strategy leads to a unified framework representation, and the multi‐agent formation control problem is simplified to a closed‐loop stability problem for an agent. The overall problem comes down to deciding two feedback gains: predictor gain and controller gain. For a perfect plant model, any choice of stabilizing gains can achieve formation. But when agent models are uncertain, the gains need to be derived from the solutions of linear matrix inequalities (LMIs) containing network information and delay. LMIs are obtained from the bounded real lemma and the gains obtained from the solution guarantee to maximize the H∞$$ {H}_{infty } $$ norm bound of allowable perturbation, modeled as additive uncertainty, for a known delay size. Further, since the FSA technique is sensitive to discretization, a digital implementation of the overall scheme is elaborated, which may help to debug stability issues in the implementation process. Finally, numerical examples are presented to demonstrate the effectiveness of the proposed ideas.","PeriodicalId":105945,"journal":{"name":"Optimal Control Applications and Methods","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124966376","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

A fixed step distributed proximal gradient push‐pull algorithm based on integral quadratic constraint 基于积分二次约束的固定步长分布近端梯度推拉算法

Optimal Control Applications and Methods Pub Date : 2023-04-16 DOI: 10.1002/oca.3000

Wenhua Gao, Yibin Xie, Hongwei Ren

引用次数: 0

Discrete‐time mean field social control with a major agent 离散时间平均场社会控制与主要代理

Optimal Control Applications and Methods Pub Date : 2023-04-11 DOI: 10.1002/oca.2997

Xiao Ma, Bing‐Chang Wang, Huanshui Zhang

引用次数: 0

Near‐optimal control of a class of output‐constrained systems using recurrent neural network: A control‐barrier function approach 一类使用递归神经网络的输出约束系统的近最优控制:一种控制障碍函数方法

Optimal Control Applications and Methods Pub Date : 2023-03-28 DOI: 10.1002/oca.2995

Surena Rad-Moghadam, M. Farrokhi

{"title":"Near‐optimal control of a class of output‐constrained systems using recurrent neural network: A control‐barrier function approach","authors":"Surena Rad-Moghadam, M. Farrokhi","doi":"10.1002/oca.2995","DOIUrl":"https://doi.org/10.1002/oca.2995","url":null,"abstract":"This paper proposes a near‐optimal controller design for the constrained nonlinear affine systems based on a Recurrent Neural Network (RNN) and Extended State Observers (ESOs). For this purpose, after defining a finite‐horizon integral‐type performance index, the prediction over the horizon is performed using the Taylor expansion that converts the primary problem into a finite‐dimensional optimization. In comparison with other controllers of the similar structure, the proposed method is capable of dealing with output constraints by employing the Control Barrier Function (CBF). The class of the output and input constraints are of the box‐type. Moreover, whereas several safe control approaches are proposed regardless of the performance of the closed‐loop system, this paper aims at achieving a near‐optimal performance as far as the constraints permit. As a result, a constrained optimization problem is achieved, where the online solution is obtained using a rapidly convergent RNN. Stability and the ease of implementation are some of the advantages of this network making the algorithm more reliable. Moreover, integrated stability analysis of the closed‐loop system that includes the dynamic RNN reveals that the closed‐loop system is stable in the sense of the Lyapunov stability theory. The effectiveness of the proposed control method in terms of the tracking performance and constraint satisfaction is illustrated through a simulating example of two‐inverted pendulums system. The results indicated advantages of the proposed method as compared with the recently published methods in well‐known literature.","PeriodicalId":105945,"journal":{"name":"Optimal Control Applications and Methods","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115281782","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

Finite‐time fault detection for nonlinear delayed system with dynamic quantization and multiple packet dropouts 具有动态量化和多丢包的非线性延迟系统的有限时间故障检测

Optimal Control Applications and Methods Pub Date : 2023-03-23 DOI: 10.1002/oca.2996

Zhihui Wu, Xue Zhao, Cai Chen, Yue Zhao

引用次数: 2