We introduce the theoretical framework from geometric optimal control for a control system modeled by the generalized Lotka–Volterra equation, motivated by restoring the gut microbiota infected by Clostridium difficile combining antibiotic treatment and fecal injection. We consider both permanent control and sampled-data control related to the medical protocols.
{"title":"Geometric optimal control of the generalized Lotka–Volterra model of the intestinal microbiome","authors":"Bernard Bonnard, Jérémy Rouot, Cristiana J Silva","doi":"10.1002/oca.3089","DOIUrl":"https://doi.org/10.1002/oca.3089","url":null,"abstract":"We introduce the theoretical framework from geometric optimal control for a control system modeled by the generalized Lotka–Volterra equation, motivated by restoring the gut microbiota infected by Clostridium difficile combining antibiotic treatment and fecal injection. We consider both permanent control and sampled-data control related to the medical protocols.","PeriodicalId":501055,"journal":{"name":"Optimal Control Applications and Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139764116","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}
This paper proposes a hybrid technique for load frequency control (LFC) in an inter-connected deregulated power-system. The proposed method is the combination of a gannet optimization algorithm (GOA) and spiking neural network (SNN), hence, it is named as GOA-SNN technique. The objective of the proposed method is to minimize frequency deviations within the power system (PS). By lessening the frequency-deviation and tie-line power variation, this approach ensures system frequency-control under the effect of load disturbances. The GOA method is utilized to generate the set of control signals of the controller. The SNN method is used to predict the optimum gain parameter of the controller. By then the proposed method is run in MATLAB software and evaluated their performance with various existing approaches. The proposed method shows better results than other existing methods, such as Ant Lion Optimization (ALO), particle swarm optimization (PSO), and Salp Swarm Algorithm (SSA). The GOA-SNN approach shows a low Area control error is 0.48% and a high efficiency is 96% compared with other existing approaches.
{"title":"Load frequency control in deregulated power system with renewable energy sources: Hybrid GOA-SNN technique","authors":"C. Srisailam, M. Manjula, K. Muralidhar Goud","doi":"10.1002/oca.3099","DOIUrl":"https://doi.org/10.1002/oca.3099","url":null,"abstract":"This paper proposes a hybrid technique for load frequency control (LFC) in an inter-connected deregulated power-system. The proposed method is the combination of a gannet optimization algorithm (GOA) and spiking neural network (SNN), hence, it is named as GOA-SNN technique. The objective of the proposed method is to minimize frequency deviations within the power system (PS). By lessening the frequency-deviation and tie-line power variation, this approach ensures system frequency-control under the effect of load disturbances. The GOA method is utilized to generate the set of control signals of the controller. The SNN method is used to predict the optimum gain parameter of the controller. By then the proposed method is run in MATLAB software and evaluated their performance with various existing approaches. The proposed method shows better results than other existing methods, such as Ant Lion Optimization (ALO), particle swarm optimization (PSO), and Salp Swarm Algorithm (SSA). The GOA-SNN approach shows a low Area control error is 0.48% and a high efficiency is 96% compared with other existing approaches.","PeriodicalId":501055,"journal":{"name":"Optimal Control Applications and Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139764063","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}
This manuscript proposes a novel technique for the precise model of electric vehicles (EVs) in the reliability and adequacy model of smart grids (SG). The proposed method combines forensic-based investigation (FBI) and Archimedes optimization algorithm (AOA), named the FBIAOA technique. The objective of the proposed method is to rise the profit of fast charging stations and lessen the rising energy demand on the grid that is made up of storage systems and renewable energy generation (wind and PV). The demand for EVs and renewable generation is calculated using the FBI algorithm method. The growth of the proposed method is to examine the reliability of SG depending on the aggregation of the state matrices of EV stochastic parameters. The proposed method can help accelerate the reliability calculations by determining the desired count of EV states. The proposed strategy is run in MATLAB and is evaluated in its performance with existing methods. The proposed method gives a lower cost than the existing genetic algorithm, cuttlefish algorithm, and tunicate swarm algorithm methods.
{"title":"Impact of fast charging station for electric vehicles with grid integration: Forensic-based investigation and Archimedes optimization algorithm approach","authors":"Abhishek Kumar Singh, Ashwani Kumar","doi":"10.1002/oca.3100","DOIUrl":"https://doi.org/10.1002/oca.3100","url":null,"abstract":"This manuscript proposes a novel technique for the precise model of electric vehicles (EVs) in the reliability and adequacy model of smart grids (SG). The proposed method combines forensic-based investigation (FBI) and Archimedes optimization algorithm (AOA), named the FBIAOA technique. The objective of the proposed method is to rise the profit of fast charging stations and lessen the rising energy demand on the grid that is made up of storage systems and renewable energy generation (wind and PV). The demand for EVs and renewable generation is calculated using the FBI algorithm method. The growth of the proposed method is to examine the reliability of SG depending on the aggregation of the state matrices of EV stochastic parameters. The proposed method can help accelerate the reliability calculations by determining the desired count of EV states. The proposed strategy is run in MATLAB and is evaluated in its performance with existing methods. The proposed method gives a lower cost than the existing genetic algorithm, cuttlefish algorithm, and tunicate swarm algorithm methods.","PeriodicalId":501055,"journal":{"name":"Optimal Control Applications and Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139665696","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}
A new method is developed for accurately approximating the solution to state-variable inequality path constrained optimal control problems using a multiple-domain adaptive Legendre–Gauss–Radau collocation method. The method consists of the following parts. First, a structure detection method is developed to estimate switch times in the activation and deactivation of state-variable inequality path constraints. Second, using the detected structure, the domain is partitioned into multiple-domains where each domain corresponds to either a constrained or an unconstrained segment. Furthermore, additional decision variables are introduced in the multiple-domain formulation, where these additional decision variables represent the switch times of the detected active state-variable inequality path constraints. Within a constrained domain, the path constraint is differentiated with respect to the independent variable until the control appears explicitly, and this derivative is set to zero along the constrained arc while all preceding derivatives are set to zero at the start of the constrained arc. The time derivatives of the active state-variable inequality path constraints are computed using automatic differentiation and the properties of the chain rule. The method is demonstrated on two problems, the first being a benchmark optimal control problem which has a known analytical solution and the second being a challenging problem from the field of aerospace engineering in which there is no known analytical solution. When compared against previously developed adaptive Legendre–Gauss–Radau methods, the results show that the method developed in this paper is capable of computing accurate solutions to problems whose solution contain active state-variable inequality path constraints.
{"title":"Method for solving state-path constrained optimal control problems using adaptive Radau collocation","authors":"Cale A. Byczkowski, Anil V. Rao","doi":"10.1002/oca.3097","DOIUrl":"https://doi.org/10.1002/oca.3097","url":null,"abstract":"A new method is developed for accurately approximating the solution to state-variable inequality path constrained optimal control problems using a multiple-domain adaptive Legendre–Gauss–Radau collocation method. The method consists of the following parts. First, a structure detection method is developed to estimate switch times in the activation and deactivation of state-variable inequality path constraints. Second, using the detected structure, the domain is partitioned into multiple-domains where each domain corresponds to either a constrained or an unconstrained segment. Furthermore, additional decision variables are introduced in the multiple-domain formulation, where these additional decision variables represent the switch times of the detected active state-variable inequality path constraints. Within a constrained domain, the path constraint is differentiated with respect to the independent variable until the control appears explicitly, and this derivative is set to zero along the constrained arc while all preceding derivatives are set to zero at the start of the constrained arc. The time derivatives of the active state-variable inequality path constraints are computed using automatic differentiation and the properties of the chain rule. The method is demonstrated on two problems, the first being a benchmark optimal control problem which has a known analytical solution and the second being a challenging problem from the field of aerospace engineering in which there is no known analytical solution. When compared against previously developed adaptive Legendre–Gauss–Radau methods, the results show that the method developed in this paper is capable of computing accurate solutions to problems whose solution contain active state-variable inequality path constraints.","PeriodicalId":501055,"journal":{"name":"Optimal Control Applications and Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139665577","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}
Time-delayed systems frequently appear, especially in sectors such as fluid flow processes, chemical procedures, and the food industry. This paper addresses the optimization of parameters for a fractional order PID (FOPID) controller, which is used to control a time-delayed system, using five distinct algorithms inspired by nature. These algorithms are NewBAT, Cuckoo search (CS), Firefly (FF), Gray Wolf Optimizer (GWO), and Whale optimization algorithm (WOA). The FOPID controller parameters, namely KP, KI, KD, λ and μ, have been optimized using these algorithms. During the optimization process, the integral of the time absolute error (ITAE) was considered as the primary measurement criterion. In addition to this value, the maximum overshoot, settling time, time to reach the maximum value, and error values were examined. Simulations conducted with the obtained parameters tested the system's resilience to disturbances introduced at the output, and the controller responses were also evaluated during these tests. The reactions of the determined parameters to different reference inputs were analyzed, and the results are presented in graphs and tables. The efficiency and reliability of the optimization algorithms were substantiated by comprehensive statistical analyses. These analyses play a critical role in algorithm selection and objective evaluation of the results. Simulation studies were conducted in the Matlab and Simulink environments. The FOMCON Toolbox was used for fractional-order processes.
{"title":"Nature-ınspired algorithms for optimizing fractional order PID controllers in time-delayed systems","authors":"Aykut Fatih Güven, Onur Özdal Mengi","doi":"10.1002/oca.3101","DOIUrl":"https://doi.org/10.1002/oca.3101","url":null,"abstract":"Time-delayed systems frequently appear, especially in sectors such as fluid flow processes, chemical procedures, and the food industry. This paper addresses the optimization of parameters for a fractional order PID (FOPID) controller, which is used to control a time-delayed system, using five distinct algorithms inspired by nature. These algorithms are NewBAT, Cuckoo search (CS), Firefly (FF), Gray Wolf Optimizer (GWO), and Whale optimization algorithm (WOA). The FOPID controller parameters, namely <i>K</i><sub>P</sub>, <i>K</i><sub>I</sub>, <i>K</i><sub>D</sub>, <i>λ</i> and <i>μ</i>, have been optimized using these algorithms. During the optimization process, the integral of the time absolute error (ITAE) was considered as the primary measurement criterion. In addition to this value, the maximum overshoot, settling time, time to reach the maximum value, and error values were examined. Simulations conducted with the obtained parameters tested the system's resilience to disturbances introduced at the output, and the controller responses were also evaluated during these tests. The reactions of the determined parameters to different reference inputs were analyzed, and the results are presented in graphs and tables. The efficiency and reliability of the optimization algorithms were substantiated by comprehensive statistical analyses. These analyses play a critical role in algorithm selection and objective evaluation of the results. Simulation studies were conducted in the Matlab and Simulink environments. The FOMCON Toolbox was used for fractional-order processes.","PeriodicalId":501055,"journal":{"name":"Optimal Control Applications and Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139579515","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}
In this study, we introduce transition density function expansion methods inspired from Yang et al. (J Econom. 2019;209(2):256–288.) to stochastic control issues related to utility maximization, without imposing limitations on the variety of asset price models and utility functions. Utilizing Bellman's dynamic programming principle, we initially recast the conditional expectation via the transition density function pertinent to the diffusion process. Subsequently, we employ the Itô-Taylor expansion and Delta expansion techniques to the transition density function associated with the multivariate diffusion process, facilitated by a quasi-Lamperti transformation, aiming to derive explicit recursive expressions for expansion coefficient functions. Our main contributions are that we articulate detailed algorithms, stemming from the backward recursive formulations of the value function and optimal strategies, achieved through discretization methodologies with rigorous proof of expansion convergence in portfolio optimization. Both theoretical and practical demonstrations are presented to validate the convergence of these approximate techniques in addressing stochastic control challenges. To underscore the efficiency and precision of our proposed methods, we apply them to portfolio selection problems within several benchmark models, and highlight the reduced complexity in comparison to the current methodologies.
{"title":"Transition density function expansion methods for portfolio optimization","authors":"Yuxuan Lu, Qing Zhou, Weixing Wu, Weilin Xiao","doi":"10.1002/oca.3102","DOIUrl":"https://doi.org/10.1002/oca.3102","url":null,"abstract":"In this study, we introduce transition density function expansion methods inspired from Yang et al. (J Econom. 2019;209(2):256–288.) to stochastic control issues related to utility maximization, without imposing limitations on the variety of asset price models and utility functions. Utilizing Bellman's dynamic programming principle, we initially recast the conditional expectation via the transition density function pertinent to the diffusion process. Subsequently, we employ the Itô-Taylor expansion and Delta expansion techniques to the transition density function associated with the multivariate diffusion process, facilitated by a quasi-Lamperti transformation, aiming to derive explicit recursive expressions for expansion coefficient functions. Our main contributions are that we articulate detailed algorithms, stemming from the backward recursive formulations of the value function and optimal strategies, achieved through discretization methodologies with rigorous proof of expansion convergence in portfolio optimization. Both theoretical and practical demonstrations are presented to validate the convergence of these approximate techniques in addressing stochastic control challenges. To underscore the efficiency and precision of our proposed methods, we apply them to portfolio selection problems within several benchmark models, and highlight the reduced complexity in comparison to the current methodologies.","PeriodicalId":501055,"journal":{"name":"Optimal Control Applications and Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139579522","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}
This paper presents a differential equation including both random matrices and a Liu process. Then we demonstrate that the solution to this equation exists and is unique. Under the framework of chance theory, problems of optimal control and two-person zero-sum game subject to differential equations are considered. An equation of optimality is provided for solving a problem of optimal control. Then equilibrium equations are proposed to identify the saddle-point of a two-person zero-sum game problem. As an extension, we generalize the obtained results to the problems subject to differential equations including both random matrices and multiple Liu processes. Finally, we utilize the acquired theoretical results to analyze a portfolio selection game problem.
{"title":"Optimal control and zero-sum game subject to differential equations with Liu processes and random matrices","authors":"Xin Chen, Yuanguo Zhu","doi":"10.1002/oca.3098","DOIUrl":"https://doi.org/10.1002/oca.3098","url":null,"abstract":"This paper presents a differential equation including both random matrices and a Liu process. Then we demonstrate that the solution to this equation exists and is unique. Under the framework of chance theory, problems of optimal control and two-person zero-sum game subject to differential equations are considered. An equation of optimality is provided for solving a problem of optimal control. Then equilibrium equations are proposed to identify the saddle-point of a two-person zero-sum game problem. As an extension, we generalize the obtained results to the problems subject to differential equations including both random matrices and multiple Liu processes. Finally, we utilize the acquired theoretical results to analyze a portfolio selection game problem.","PeriodicalId":501055,"journal":{"name":"Optimal Control Applications and Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139501081","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}
Crossover behaviors have always existed in the history of infectious pandemics due to a few distinct, erratic spread outlines. This research aims to investigate the crossover behavior of the proposed SVICR commensurate fractional model for the COVID-19 delta variant, considering microbial coinfections. A mathematical model in terms of Atangana–Baleanu Caputo (ABC) category fractional integrals takes into account the co-infection of mucormycosis in immunocompromised COVID-19 patients caused by microbial infections. ABC operators preserve the intact history of the happenings under contemplation through its nonsingular kernel. It is observed that the framed five-compartmental SVICR model is positively bounded on R5, the solution space. Two equilibrium points
{"title":"Fractional commensurate model on COVID-19 with microbial co-infection: An optimal control analysis","authors":"G. M. Vijayalakshmi, P. Roselyn Besi, Ali Akgül","doi":"10.1002/oca.3093","DOIUrl":"https://doi.org/10.1002/oca.3093","url":null,"abstract":"Crossover behaviors have always existed in the history of infectious pandemics due to a few distinct, erratic spread outlines. This research aims to investigate the crossover behavior of the proposed SVICR commensurate fractional model for the COVID-19 delta variant, considering microbial coinfections. A mathematical model in terms of Atangana–Baleanu Caputo (ABC) category fractional integrals takes into account the co-infection of mucormycosis in immunocompromised COVID-19 patients caused by microbial infections. ABC operators preserve the intact history of the happenings under contemplation through its nonsingular kernel. It is observed that the framed five-compartmental SVICR model is positively bounded on R<sup>5</sup>, the solution space. Two equilibrium points <mjx-container aria-label=\"Menu available. Press control and space , or space\" ctxtmenu_counter=\"0\" ctxtmenu_oldtabindex=\"1\" jax=\"CHTML\" role=\"application\" sre-explorer- style=\"font-size: 103%; position: relative;\" tabindex=\"0\"><mjx-math aria-hidden=\"true\" location=\"graphic/oca3093-math-0001.png\"><mjx-semantics><mjx-mrow data-semantic-children=\"2,3,7\" data-semantic-collapsed=\"(10 (c 8 9) 2 3 7)\" data-semantic- data-semantic-role=\"text\" data-semantic-speech=\"upper E 0 and upper E Subscript e Baseline\" data-semantic-type=\"punctuated\"><mjx-msub data-semantic-children=\"0,1\" data-semantic- data-semantic-parent=\"10\" data-semantic-role=\"latinletter\" data-semantic-type=\"subscript\"><mjx-mi data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"italic\" data-semantic- data-semantic-parent=\"2\" data-semantic-role=\"latinletter\" data-semantic-type=\"identifier\"><mjx-c></mjx-c></mjx-mi><mjx-script style=\"vertical-align: -0.15em; margin-left: -0.026em;\"><mjx-mn data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"normal\" data-semantic- data-semantic-parent=\"2\" data-semantic-role=\"integer\" data-semantic-type=\"number\" size=\"s\"><mjx-c></mjx-c></mjx-mn></mjx-script></mjx-msub><mjx-mtext data-semantic-annotation=\"clearspeak:unit\" data-semantic-font=\"normal\" data-semantic- data-semantic-parent=\"10\" data-semantic-role=\"unknown\" data-semantic-type=\"text\"><mjx-c></mjx-c><mjx-c></mjx-c><mjx-c></mjx-c></mjx-mtext><mjx-mspace style=\"width: 0.25em;\"></mjx-mspace><mjx-msub data-semantic-children=\"5,6\" data-semantic- data-semantic-parent=\"10\" data-semantic-role=\"latinletter\" data-semantic-type=\"subscript\"><mjx-mi data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"italic\" data-semantic- data-semantic-parent=\"7\" data-semantic-role=\"latinletter\" data-semantic-type=\"identifier\"><mjx-c></mjx-c></mjx-mi><mjx-script style=\"vertical-align: -0.15em; margin-left: -0.026em;\"><mjx-mi data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"italic\" data-semantic- data-semantic-parent=\"7\" data-semantic-role=\"latinletter\" data-semantic-type=\"identifier\" size=\"s\"><mjx-c></mjx-c></mjx-mi></mjx-script></mjx-msub></mjx-mrow></mjx-semantics></mjx-math><mjx-assistive-mml aria-hidden=\"true\" disp","PeriodicalId":501055,"journal":{"name":"Optimal Control Applications and Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139415633","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}
This article proposes secure state estimation for cyber-physical systems against sensor attacks. The attack and defense strategies are established via additional historical data, and the defender aims to reduce the estimation error maximally while the attacker aims to degrade the system performance maximally. The algorithm is implemented in the Nash equilibrium framework where the defender first designs the defense strategy and then the attacker designs corresponding attack parameters to launch attacks. Then, a robust optimization problem is formulated using Wasserstein ambiguity sets, which turn out to be equivalent to a convex program. A novel secure observer is proposed, where the attack estimation is used to mitigate attacks. Moreover, the detector is to monitor system behavior and detects the existence of sensor attacks. Finally, simulation results and comparative results illustrate the effectiveness of the defense strategy.
{"title":"Secure state estimation via robust optimization for nonlinear cyber-physical systems","authors":"Lexin Chen, Yongming Li, Shaocheng Tong","doi":"10.1002/oca.3096","DOIUrl":"https://doi.org/10.1002/oca.3096","url":null,"abstract":"This article proposes secure state estimation for cyber-physical systems against sensor attacks. The attack and defense strategies are established via additional historical data, and the defender aims to reduce the estimation error maximally while the attacker aims to degrade the system performance maximally. The algorithm is implemented in the Nash equilibrium framework where the defender first designs the defense strategy and then the attacker designs corresponding attack parameters to launch attacks. Then, a robust optimization problem is formulated using Wasserstein ambiguity sets, which turn out to be equivalent to a convex program. A novel secure observer is proposed, where the attack estimation is used to mitigate attacks. Moreover, the detector is to monitor system behavior and detects the existence of sensor attacks. Finally, simulation results and comparative results illustrate the effectiveness of the defense strategy.","PeriodicalId":501055,"journal":{"name":"Optimal Control Applications and Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139415613","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}
This article discusses the finite‐time mixed and passive filtering design problem for uncertain nonlinear singular systems. The parameter uncertainties are constrained by well‐defined upper bounds on their magnitudes. The objective is to design a full‐order filter to ensure that the augmented singular system to be finite‐time boundedness(FTB) with and passivity performance. Firstly, a sufficient condition for achieving singular finite‐time boundedness with mixed and passivity performance for uncertain nonlinear singular system is derived by introducing a free matrix. Secondly, a novel criterion is established for analyzing finite‐time and passivity problem of filtering error singular system based on augmented matrix technique. Thirdly, the filtering design conditions are presented and the relevant parameters of the desired filter are determined using linear matrix inequality decoupling principle. Finally, a practical circuit system and a numerical example are provided to demonstrate the feasibility of the proposed scheme.
{"title":"Mixed H∞‐based finite‐time passive filtering for a class of uncertain nonlinear singular systems","authors":"Meiqing Li, Wenbin Chen, Yu Shao","doi":"10.1002/oca.3092","DOIUrl":"https://doi.org/10.1002/oca.3092","url":null,"abstract":"This article discusses the finite‐time mixed and passive filtering design problem for uncertain nonlinear singular systems. The parameter uncertainties are constrained by well‐defined upper bounds on their magnitudes. The objective is to design a full‐order filter to ensure that the augmented singular system to be finite‐time boundedness(FTB) with and passivity performance. Firstly, a sufficient condition for achieving singular finite‐time boundedness with mixed and passivity performance for uncertain nonlinear singular system is derived by introducing a free matrix. Secondly, a novel criterion is established for analyzing finite‐time and passivity problem of filtering error singular system based on augmented matrix technique. Thirdly, the filtering design conditions are presented and the relevant parameters of the desired filter are determined using linear matrix inequality decoupling principle. Finally, a practical circuit system and a numerical example are provided to demonstrate the feasibility of the proposed scheme.","PeriodicalId":501055,"journal":{"name":"Optimal Control Applications and Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139381286","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}
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