This paper proposes a hybrid COA-QNN approach for an interleaved KY converter with closed-loop control for a single-phase grid-connected photovoltaic (PV) system. The proposed strategy combines both Cheetah Optimizer algorithm (COA) and Quantum Neural Network (QNN), and it is commonly named the COA-QNN technique. The interleaved KY converter is connected in the converter side. The primary goal of the COA-QNN technique is to enhance PQ while maximizing PV electricity being transferred to the grid. The proposed COA is utilized to identify the optimal closed-loop controller enhancements for on-grid solar photovoltaic systems. The QNN is used to predict the optimal control parameter. The PV-interleaved KY converter is managed by a predictive control mechanism to carry out both tasks of PQ enhancement. By then the COA-QNN technique is implemented on the MATLAB platform and compared with existing methods. Finally, the proposed method shows better results in all methods, such as GWO, FF optimization and combined GWO-FF.
本文针对单相并网光伏（PV）系统的闭环控制交错 KY 转换器提出了一种 COA-QNN 混合方法。所提出的策略结合了猎豹优化算法（COA）和量子神经网络（QNN），通常称为 COA-QNN 技术。交错式 KY 转换器连接在转换器侧。COA-QNN 技术的主要目标是提高 PQ，同时最大限度地向电网输送光伏电力。所提出的 COA 可用于确定并网太阳能光伏系统的最佳闭环控制器增强功能。QNN 用于预测最佳控制参数。光伏交错 KY 转换器由预测控制机制管理，以执行 PQ 增强的两项任务。然后，在 MATLAB 平台上实现了 COA-QNN 技术，并与现有方法进行了比较。最后，在 GWO、FF 优化和 GWO-FF 组合等所有方法中，所提出的方法都显示出更好的效果。
{"title":"Enhancing performance of interleaved KY converter control in single phase grid-tied PV systems: A hybrid approach","authors":"R Shobha, N Narmadhai","doi":"10.1002/oca.3116","DOIUrl":"https://doi.org/10.1002/oca.3116","url":null,"abstract":"This paper proposes a hybrid COA-QNN approach for an interleaved KY converter with closed-loop control for a single-phase grid-connected photovoltaic (PV) system. The proposed strategy combines both Cheetah Optimizer algorithm (COA) and Quantum Neural Network (QNN), and it is commonly named the COA-QNN technique. The interleaved KY converter is connected in the converter side. The primary goal of the COA-QNN technique is to enhance PQ while maximizing PV electricity being transferred to the grid. The proposed COA is utilized to identify the optimal closed-loop controller enhancements for on-grid solar photovoltaic systems. The QNN is used to predict the optimal control parameter. The PV-interleaved KY converter is managed by a predictive control mechanism to carry out both tasks of PQ enhancement. By then the COA-QNN technique is implemented on the MATLAB platform and compared with existing methods. Finally, the proposed method shows better results in all methods, such as GWO, FF optimization and combined GWO-FF.","PeriodicalId":501055,"journal":{"name":"Optimal Control Applications and Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140325943","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}
The process of smelting non‐ferrous metals results in significant emissions of flue gas that contains sulfur dioxide (SO), which is very harmful to the environment. Through precise control of converter inlet temperature, it is feasible to enhance the conversion ratio of SO and simultaneously mitigate environmental pollution by generating acid from flue gas. Because of the high degree of uncertainty in smelting process, converter inlet temperature is challenging to regulate and controller frequently needs updating. To improve control performance and decrease controller update times, an event‐triggered neural network model predictive control (ETNMPC) strategy is proposed. First, long short‐term memory (LSTM) prediction model and model predictive controller are developed. Second, it is decided whether to update the existing controller by designing an event‐triggered mechanism. Finally, using real data from a copper facility in Jiangxi Province, the temperature control experiment of converter inlet is carried out. Simulation results demonstrate that the proposed ETNMPC outperforms conventional time‐triggered method in terms of control performance, greatly lowers the times of controller updates, and significantly lowers computation costs and communication burden.
有色金属冶炼过程会排放大量含二氧化硫（SO）的烟气，对环境造成极大危害。通过精确控制转炉入口温度，可以提高 SO 的转化率，同时通过烟气制酸来减轻环境污染。由于冶炼过程具有高度不确定性，因此转炉入口温度的调节具有挑战性，控制器需要频繁更新。为了提高控制性能并减少控制器更新时间，提出了一种事件触发神经网络模型预测控制（ETNMPC）策略。首先，开发了长短期记忆（LSTM）预测模型和模型预测控制器。其次，决定是否通过设计事件触发机制来更新现有控制器。最后，利用江西省某铜厂的真实数据，进行了转炉入口温度控制实验。仿真结果表明，所提出的 ETNMPC 在控制性能上优于传统的时间触发方法，大大减少了控制器更新的次数，并显著降低了计算成本和通信负担。
{"title":"Neural network predictive control of converter inlet temperature based on event‐triggered mechanism in flue gas acid production","authors":"Minghua Liu, Xiaoli Li, Kang Wang","doi":"10.1002/oca.3124","DOIUrl":"https://doi.org/10.1002/oca.3124","url":null,"abstract":"The process of smelting non‐ferrous metals results in significant emissions of flue gas that contains sulfur dioxide (SO), which is very harmful to the environment. Through precise control of converter inlet temperature, it is feasible to enhance the conversion ratio of SO and simultaneously mitigate environmental pollution by generating acid from flue gas. Because of the high degree of uncertainty in smelting process, converter inlet temperature is challenging to regulate and controller frequently needs updating. To improve control performance and decrease controller update times, an event‐triggered neural network model predictive control (ETNMPC) strategy is proposed. First, long short‐term memory (LSTM) prediction model and model predictive controller are developed. Second, it is decided whether to update the existing controller by designing an event‐triggered mechanism. Finally, using real data from a copper facility in Jiangxi Province, the temperature control experiment of converter inlet is carried out. Simulation results demonstrate that the proposed ETNMPC outperforms conventional time‐triggered method in terms of control performance, greatly lowers the times of controller updates, and significantly lowers computation costs and communication burden.","PeriodicalId":501055,"journal":{"name":"Optimal Control Applications and Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140316407","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 study focuses on the analysis of a linear quadratic zero-sum game (LQZSG) for time-delayed uncertain stochastic systems. To begin, we introduce a general time-delayed zero-sum game. Employing an algebraic transformation method, we transform the time-delayed zero-sum game into an equivalent uncertain random zero-sum game without time delay. Subsequently, we present equilibrium equations that streamline the transformation of the uncertain random zero-sum game into problems solvable as deterministic difference equations. We then investigate the LQZSG involving a linear time-delayed uncertain stochastic system with a quadratic objective function. Within this framework, we provide a unified framework for solving this type of game and obtaining the analytic expression for the saddle-point equilibrium of the LQZSG. Additionally, we present a numerical example and a counter-terrorism economic game to illustrate the applicability of our findings.
{"title":"Linear quadratic zero-sum game for time-delayed uncertain stochastic systems","authors":"Xin Chen, Yue Yuan, Dongmei Yuan, Yu Shao","doi":"10.1002/oca.3123","DOIUrl":"https://doi.org/10.1002/oca.3123","url":null,"abstract":"This study focuses on the analysis of a linear quadratic zero-sum game (LQZSG) for time-delayed uncertain stochastic systems. To begin, we introduce a general time-delayed zero-sum game. Employing an algebraic transformation method, we transform the time-delayed zero-sum game into an equivalent uncertain random zero-sum game without time delay. Subsequently, we present equilibrium equations that streamline the transformation of the uncertain random zero-sum game into problems solvable as deterministic difference equations. We then investigate the LQZSG involving a linear time-delayed uncertain stochastic system with a quadratic objective function. Within this framework, we provide a unified framework for solving this type of game and obtaining the analytic expression for the saddle-point equilibrium of the LQZSG. Additionally, we present a numerical example and a counter-terrorism economic game to illustrate the applicability of our findings.","PeriodicalId":501055,"journal":{"name":"Optimal Control Applications and Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140325711","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}
Román Comelli, Sorin Olaru, María M. Seron, Ernesto Kofman
This work addresses the problem of path following for a car‐like agricultural robot by means of a finite control set model predictive control (FCS‐MPC) strategy that considers the control actions in a set composed of a limited amount of elements. Recent results on a stabilizing MPC formulation that replaces the classical control invariant set by a pair of inner‐outer sets are extended to preserve stability properties with different control and prediction horizons and are then used for the aforementioned application. Being particularly simple, the presented approach can explicitly deal with nonlinearities and constraints at the expense of resolution in the vehicle steering system, which in practice does not affect the controller performance as will be shown. In addition to describing the control method, simulations and a comparison with another nonlinear MPC strategy are presented to illustrate the advantages of the proposed scheme.
{"title":"Application of a stabilizing model predictive controller to path following for a car‐like agricultural robot","authors":"Román Comelli, Sorin Olaru, María M. Seron, Ernesto Kofman","doi":"10.1002/oca.3126","DOIUrl":"https://doi.org/10.1002/oca.3126","url":null,"abstract":"This work addresses the problem of path following for a car‐like agricultural robot by means of a finite control set model predictive control (FCS‐MPC) strategy that considers the control actions in a set composed of a limited amount of elements. Recent results on a stabilizing MPC formulation that replaces the classical control invariant set by a pair of inner‐outer sets are extended to preserve stability properties with different control and prediction horizons and are then used for the aforementioned application. Being particularly simple, the presented approach can explicitly deal with nonlinearities and constraints at the expense of resolution in the vehicle steering system, which in practice does not affect the controller performance as will be shown. In addition to describing the control method, simulations and a comparison with another nonlinear MPC strategy are presented to illustrate the advantages of the proposed scheme.","PeriodicalId":501055,"journal":{"name":"Optimal Control Applications and Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140316327","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}
The sources of fossil fuel are impoverishing in upcoming future. In the current research scenario, sincere effort has been taken worldwide to explore the use of renewable energy sources in electrical power system for the economic benefits and environmental consciousness. The main contribution of the proposed work is first, to find optimal hydro‐thermal scheduling (HTS) with wind, solar, and electric vehicles (EVs) for variable load. The target is to find out maximum utilization of renewable energy sources for economic power generation with less emission. Thus, a new approach of EV to grid has been adopted with wind–solar based HTS system for improving grid reliability and resilience. Second, there is a requirement to overcome the local optima problems with less convergence speed. This is obtained by employing a relatively new methodology, known as chaotic‐quasi‐opposition‐based whale optimization algorithm (WOA) (CQOWOA). The proposed algorithm is tested on HTS and wind–solar‐electric vehicle‐based HTS (HTWSVS) for three different cases. Different nonlinearities like valve point effect of thermal units, transmission losses, spillage rate of hydro reservoir units and uncertainties of wind, solar as well as EV are considered to judge the effectiveness of the proposed CQOWOA technique on realistic problems. The presence of wind, solar, and EV energy sources with HTS is evident from the test results of CQOWOA, for multi‐objective problem where cost and emission both are reduced significantly. The robustness of the proposed solution has been verified by implementing the statistical analysis on two systems with least variation of mean and optimal values of cost with the tolerance of less than 0.025%. The comparative analysis of CQOWOA with the other optimization techniques validates its superiority on both the test systems by minimizing the generation cost and emission.
{"title":"Chaotic‐quasi‐opposition based whale optimization technique applied to multi‐objective complementary scheduling of grid connected hydro‐thermal–wind–solar‐electric vehicle system","authors":"Chandan Paul, Provas Kumar Roy, V. Mukherjee","doi":"10.1002/oca.3113","DOIUrl":"https://doi.org/10.1002/oca.3113","url":null,"abstract":"The sources of fossil fuel are impoverishing in upcoming future. In the current research scenario, sincere effort has been taken worldwide to explore the use of renewable energy sources in electrical power system for the economic benefits and environmental consciousness. The main contribution of the proposed work is first, to find optimal hydro‐thermal scheduling (HTS) with wind, solar, and electric vehicles (EVs) for variable load. The target is to find out maximum utilization of renewable energy sources for economic power generation with less emission. Thus, a new approach of EV to grid has been adopted with wind–solar based HTS system for improving grid reliability and resilience. Second, there is a requirement to overcome the local optima problems with less convergence speed. This is obtained by employing a relatively new methodology, known as chaotic‐quasi‐opposition‐based whale optimization algorithm (WOA) (CQOWOA). The proposed algorithm is tested on HTS and wind–solar‐electric vehicle‐based HTS (HTWSVS) for three different cases. Different nonlinearities like valve point effect of thermal units, transmission losses, spillage rate of hydro reservoir units and uncertainties of wind, solar as well as EV are considered to judge the effectiveness of the proposed CQOWOA technique on realistic problems. The presence of wind, solar, and EV energy sources with HTS is evident from the test results of CQOWOA, for multi‐objective problem where cost and emission both are reduced significantly. The robustness of the proposed solution has been verified by implementing the statistical analysis on two systems with least variation of mean and optimal values of cost with the tolerance of less than 0.025%. The comparative analysis of CQOWOA with the other optimization techniques validates its superiority on both the test systems by minimizing the generation cost and emission.","PeriodicalId":501055,"journal":{"name":"Optimal Control Applications and Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140204907","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}
Considering the influence of interval uncertainty, a set‐membership state estimation algorithm based on zonotope is proposed for discrete‐time delayed switched systems subject to unknown but bounded noises. To economize on communication resources, an event‐triggered mechanism is introduced to ensure the efficient transmission of the required observation information. For the channel fading phenomenon during data transmission, an interval channel fading model applicable to the zonotope set‐emmbership state estimation is proposed. The asynchronous state observer is constructed by considering the inconsistency between event‐triggered instant and subsystem switched instant. The sufficient conditions for the existence of the state observer are given using the average dwell time method. The optimal observer gain matrix is derived by solving convex optimization. On this basis, the zonotope and the state estimation interval containing states are given by the zonotopic method. Finally, the validity and feasibility of the proposed algorithm are demonstrated by a numerical example with the comparison result.
{"title":"Set‐membership state estimation for delayed switched systems with fading measurement and interval uncertainty","authors":"Dongyan Chen, Zhizhen Zhou, Jun Hu, Junting Liu","doi":"10.1002/oca.3118","DOIUrl":"https://doi.org/10.1002/oca.3118","url":null,"abstract":"Considering the influence of interval uncertainty, a set‐membership state estimation algorithm based on zonotope is proposed for discrete‐time delayed switched systems subject to unknown but bounded noises. To economize on communication resources, an event‐triggered mechanism is introduced to ensure the efficient transmission of the required observation information. For the channel fading phenomenon during data transmission, an interval channel fading model applicable to the zonotope set‐emmbership state estimation is proposed. The asynchronous state observer is constructed by considering the inconsistency between event‐triggered instant and subsystem switched instant. The sufficient conditions for the existence of the state observer are given using the average dwell time method. The optimal observer gain matrix is derived by solving convex optimization. On this basis, the zonotope and the state estimation interval containing states are given by the zonotopic method. Finally, the validity and feasibility of the proposed algorithm are demonstrated by a numerical example with the comparison result.","PeriodicalId":501055,"journal":{"name":"Optimal Control Applications and Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140204905","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}
The challenge of controlling frequency deviation becomes more difficult as the complexity of a power network increases. The robustness of the controller has a major impact on the stability of a Modern Power system (MPS). Due to the hybridization of MPS basic AGC controllers (PID, FOPID, and TID) are insufficient to give optimal performance of a plant. This requires a robust controller. So, a novel MPC + PIDN controller has been proposed and evaluated by comparing it with several existing controllers, which gives optimal performance in terms of overshoot, undershoot, and settling time. A new modified Opposition‐based Sea‐horse Optimization (OSHO) method has been suggested to optimize the various controller settings. To demonstrate the OSHO's superiority, it is compared with a few popular, existing meta‐heuristic optimizations. The higher penetration levels of RESs reduced system inertia which further deteriorate frequency response in MPS. To overcome these challenges virtual inertia (VI) is implemented with MPC. VI is applied to improve the performance of the AGC of the interconnected MPS along with emphasizing the nature of intermittent renewable energy sources (RESs) of PV and wind energy. To determine the reliability and flexibility of the proposed controller, analysis has been done under a different situation, including step, random disturbances, and modified IEEE‐39 bus. Finally, the stability analysis is performed on a bode plot and the proposed results are compared with previously published literature. The extensive study demonstrates strong evidence that the suggested control approach is efficient and effective.
{"title":"Enhancing frequency regulation in multi‐area interconnected MPS with virtual inertia using MPC + PIDN controller","authors":"Prabhat Kumar Vidyarthi, Ashiwani Kumar","doi":"10.1002/oca.3121","DOIUrl":"https://doi.org/10.1002/oca.3121","url":null,"abstract":"The challenge of controlling frequency deviation becomes more difficult as the complexity of a power network increases. The robustness of the controller has a major impact on the stability of a Modern Power system (MPS). Due to the hybridization of MPS basic AGC controllers (PID, FOPID, and TID) are insufficient to give optimal performance of a plant. This requires a robust controller. So, a novel MPC + PIDN controller has been proposed and evaluated by comparing it with several existing controllers, which gives optimal performance in terms of overshoot, undershoot, and settling time. A new modified Opposition‐based Sea‐horse Optimization (OSHO) method has been suggested to optimize the various controller settings. To demonstrate the OSHO's superiority, it is compared with a few popular, existing meta‐heuristic optimizations. The higher penetration levels of RESs reduced system inertia which further deteriorate frequency response in MPS. To overcome these challenges virtual inertia (VI) is implemented with MPC. VI is applied to improve the performance of the AGC of the interconnected MPS along with emphasizing the nature of intermittent renewable energy sources (RESs) of PV and wind energy. To determine the reliability and flexibility of the proposed controller, analysis has been done under a different situation, including step, random disturbances, and modified IEEE‐39 bus. Finally, the stability analysis is performed on a bode plot and the proposed results are compared with previously published literature. The extensive study demonstrates strong evidence that the suggested control approach is efficient and effective.","PeriodicalId":501055,"journal":{"name":"Optimal Control Applications and Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140165845","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 studies the problem of optimal control with state constraints for mean-field type stochastic systems, which is governed by a fully coupled forward-backward stochastic differential equation with Teugels martingales. In this system, the coefficients contain not only the state processes but also its expectation value, and the cost function is of mean-field type as well. We use an equivalent backward formulation to deal with the terminal state constraint, and then we obtain a stochastic maximum principle by Ekeland's variational principle. In addition, we discuss a stochastic linear-quadratic control problem with state constraints.
{"title":"The maximum principle for optimal control of mean-field FBSDE driving by Teugels martingales with terminal state constraints","authors":"Zhen Huang, Ying Wang, Xiangyun Lin","doi":"10.1002/oca.3117","DOIUrl":"https://doi.org/10.1002/oca.3117","url":null,"abstract":"This article studies the problem of optimal control with state constraints for mean-field type stochastic systems, which is governed by a fully coupled forward-backward stochastic differential equation with Teugels martingales. In this system, the coefficients contain not only the state processes but also its expectation value, and the cost function is of mean-field type as well. We use an equivalent backward formulation to deal with the terminal state constraint, and then we obtain a stochastic maximum principle by Ekeland's variational principle. In addition, we discuss a stochastic linear-quadratic control problem with state constraints.","PeriodicalId":501055,"journal":{"name":"Optimal Control Applications and Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140148687","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 investigates the finite horizon linear quadratic gaussian (LQG) control problem for networked control systems with two‐way Markovian packet dropouts and input delay, where packet dropouts occur from the sensor to the estimator and from the controller to the actuator, and delay occurs from the controller to the actuator. The novelty of this work lies in providing a complete solution for the optimal control problem subject to two‐way Markovian packet dropouts and input delay. The contributions of this article are as follows: first, by applying the maximum principle to the discrete‐time linear systems and the quadratic cost function involving input delay and Markovian packet dropouts, a solution to the forward and backward stochastic difference equations (FBSDEs) is derived. Second, a sufficient and necessary condition for the optimal control problem is obtained by decoupling the coupled Riccati equations. Finally, the explicit solution of the optimal controller is presented based on the complete square method. Numerical examples are provided to demonstrate the validity of the theoretical results.
{"title":"Discrete‐time linear quadratic gaussian control with input delay and Markovian packet dropouts","authors":"Xiao Lu, Yuanyu Cai, Xiao Liang, Hongyu Sun","doi":"10.1002/oca.3119","DOIUrl":"https://doi.org/10.1002/oca.3119","url":null,"abstract":"This article investigates the finite horizon linear quadratic gaussian (LQG) control problem for networked control systems with two‐way Markovian packet dropouts and input delay, where packet dropouts occur from the sensor to the estimator and from the controller to the actuator, and delay occurs from the controller to the actuator. The novelty of this work lies in providing a complete solution for the optimal control problem subject to two‐way Markovian packet dropouts and input delay. The contributions of this article are as follows: first, by applying the maximum principle to the discrete‐time linear systems and the quadratic cost function involving input delay and Markovian packet dropouts, a solution to the forward and backward stochastic difference equations (FBSDEs) is derived. Second, a sufficient and necessary condition for the optimal control problem is obtained by decoupling the coupled Riccati equations. Finally, the explicit solution of the optimal controller is presented based on the complete square method. Numerical examples are provided to demonstrate the validity of the theoretical results.","PeriodicalId":501055,"journal":{"name":"Optimal Control Applications and Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140125906","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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