A hybrid technique is proposed to enhance the power quality (PQ) on the distribution sides of the utility grid (UG) by controlling a unified power quality conditioner (UPQC) connected to hybrid sources (photovoltaic [PV] and wind turbine [WT]). The proposed hybrid method integrates the implementation of the pelican optimization algorithm and the Aquila optimizer; hence, it is called the improved aquila optimizer (IAO) technique. The objective of the proposed method is to lessen the total harmonic distortion (THD), voltage instability, and PQ issues during load fluctuation situations. The improved Aquila Optimizer technique optimizes the control parameters of the UPQC to achieve optimal PQ development. The series controller is attached to the grid‐side to enhance grid PQ, while the shunt hybrid active power filter (SAPF) and shunt active power filter (SHAPF) generate the best control pulses based on load and source conditions. The proposed solution addresses power loss, THD, and voltage instability problems during load fluctuation conditions. The series controller reduces voltage sag by 14% and voltage swell by 15%. The THD for the proposed technique is 0.8%. The PQ of the proposed technique is improved, and various characteristics are reduced. The efficiency of the proposed technique is examined by using MATLAB and is compared to existing approaches. The PQ characteristics are significantly improved, and the proposed technique is better than the existing techniques.
{"title":"Optimal power quality improvement in distribution system with UPQC using an improved strategy","authors":"Tamilarasu Palanisamy, Guna Sekar Thangamuthu","doi":"10.1002/oca.3105","DOIUrl":"https://doi.org/10.1002/oca.3105","url":null,"abstract":"A hybrid technique is proposed to enhance the power quality (PQ) on the distribution sides of the utility grid (UG) by controlling a unified power quality conditioner (UPQC) connected to hybrid sources (photovoltaic [PV] and wind turbine [WT]). The proposed hybrid method integrates the implementation of the pelican optimization algorithm and the Aquila optimizer; hence, it is called the improved aquila optimizer (IAO) technique. The objective of the proposed method is to lessen the total harmonic distortion (THD), voltage instability, and PQ issues during load fluctuation situations. The improved Aquila Optimizer technique optimizes the control parameters of the UPQC to achieve optimal PQ development. The series controller is attached to the grid‐side to enhance grid PQ, while the shunt hybrid active power filter (SAPF) and shunt active power filter (SHAPF) generate the best control pulses based on load and source conditions. The proposed solution addresses power loss, THD, and voltage instability problems during load fluctuation conditions. The series controller reduces voltage sag by 14% and voltage swell by 15%. The THD for the proposed technique is 0.8%. The PQ of the proposed technique is improved, and various characteristics are reduced. The efficiency of the proposed technique is examined by using MATLAB and is compared to existing approaches. The PQ characteristics are significantly improved, and the proposed technique is better than the existing techniques.","PeriodicalId":501055,"journal":{"name":"Optimal Control Applications and Methods","volume":"135 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140126242","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 ‐gain analysis and fault‐tolerant control of a class of uncertain nonlinear discrete‐time switched systems with time‐varying delay and actuator saturation are studied by using the multiple Lyapunov functions method. The fault‐tolerant state feedback controllers and the switching law are designed such that the closed‐loop system with actuator failures satisfies the disturbance attenuation performance indicator. The problem of estimating the capacity of admissible disturbance is transformed into a constrained optimization problem to ensure that the state trajectory of the closed‐loop system is bounded under the action of external disturbances. The upper bound of restricted ‐gain is estimated by solving constrained optimization problems. Then, when the fault‐tolerant controller can be regard as the design variable, the optimization problems above are adjusted for solving control synthesis problems. Finally, the numerical example is given to verify the effectiveness of the design method.
{"title":"L2$$ {L}_2 $$‐gain analysis and fault‐tolerant control for nonlinear discrete‐time switched systems with time‐varying delay and actuator saturation","authors":"Hu Guo, Huiju Li, Xinquan Zhang","doi":"10.1002/oca.3110","DOIUrl":"https://doi.org/10.1002/oca.3110","url":null,"abstract":"The ‐gain analysis and fault‐tolerant control of a class of uncertain nonlinear discrete‐time switched systems with time‐varying delay and actuator saturation are studied by using the multiple Lyapunov functions method. The fault‐tolerant state feedback controllers and the switching law are designed such that the closed‐loop system with actuator failures satisfies the disturbance attenuation performance indicator. The problem of estimating the capacity of admissible disturbance is transformed into a constrained optimization problem to ensure that the state trajectory of the closed‐loop system is bounded under the action of external disturbances. The upper bound of restricted ‐gain is estimated by solving constrained optimization problems. Then, when the fault‐tolerant controller can be regard as the design variable, the optimization problems above are adjusted for solving control synthesis problems. Finally, the numerical example is given to verify the effectiveness of the design method.","PeriodicalId":501055,"journal":{"name":"Optimal Control Applications and Methods","volume":"108 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140019986","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 paper, we present two distinct linear Model Predictive Control (MPC) methods for controlling mobile robots in the presence of obstacles while considering the wheel slip. Predictability of the controller enables the robot to automatically choose an alternative path to avoid obstacles. However, environmental conditions and disturbances, including slip, may impact the system model. Therefore, to accurately represent the system, slip angle and slip ratio are factored into the modeling process. Then the kinematic model is linearized using the successive method to reduce computational cost. Next, both Stable MPC (SMPC) and Robust MPC have been designed and implemented on the linearized time-variant model to control the robot. The superiority of the robust predictive control method over the stable method has been discussed in terms of safety and optimal performance considering wheel slip. Finally, based on experimental tests, it has been found that the robust predictive controller is more effective than stable control when the surface is slippery and there is an obstacle in front of the robot. However, in a case where the wheel slip is neglectable, SMPC can be a better choice in presence of obstacles due to the lower computational cost.
{"title":"Superiority of model predictive control with robust and stable approach for sliding wheeled mobile systems in the presence of obstacles","authors":"Moharam Habibnejad Korayem, Fateme Namdarpour, Naeim Yousefi Lademakhi","doi":"10.1002/oca.3107","DOIUrl":"https://doi.org/10.1002/oca.3107","url":null,"abstract":"In this paper, we present two distinct linear Model Predictive Control (MPC) methods for controlling mobile robots in the presence of obstacles while considering the wheel slip. Predictability of the controller enables the robot to automatically choose an alternative path to avoid obstacles. However, environmental conditions and disturbances, including slip, may impact the system model. Therefore, to accurately represent the system, slip angle and slip ratio are factored into the modeling process. Then the kinematic model is linearized using the successive method to reduce computational cost. Next, both Stable MPC (SMPC) and Robust MPC have been designed and implemented on the linearized time-variant model to control the robot. The superiority of the robust predictive control method over the stable method has been discussed in terms of safety and optimal performance considering wheel slip. Finally, based on experimental tests, it has been found that the robust predictive controller is more effective than stable control when the surface is slippery and there is an obstacle in front of the robot. However, in a case where the wheel slip is neglectable, SMPC can be a better choice in presence of obstacles due to the lower computational cost.","PeriodicalId":501055,"journal":{"name":"Optimal Control Applications and Methods","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139968783","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 work addresses the optimized tracking control problem by combining both reinforcement learning (RL) and backstepping technique for the canonical nonlinear unknown dynamic system. Since such dynamic system contains multiple state variables with differential relation, the backstepping technique is considered by making a virtual control sequence in accordance with Lyapunov functions. In the last backstepping step, the optimized actual control is derived by performing the RL under identifier-critic-actor structure, where RL is to overcome the difficulty coming from solving Hamilton-Jacobi-Bellman (HJB) equation. Different from the traditional RL optimizing methods that find the RL updating laws from the square of the HJB equation's approximation, this optimized control is to find the RL training laws from the negative gradient of a simple positive definite function, which is equivalent to the HJB equation. The result shows that this optimized control can obviously alleviate the algorithm complexity. Meanwhile, it can remove the requirement of known dynamic as well. Finally, theory and simulation indicate the feasibility of this optimized control.
{"title":"Optimized tracking control using reinforcement learning and backstepping technique for canonical nonlinear unknown dynamic system","authors":"Yanfen Song, Zijun Li, Guoxing Wen","doi":"10.1002/oca.3115","DOIUrl":"https://doi.org/10.1002/oca.3115","url":null,"abstract":"The work addresses the optimized tracking control problem by combining both reinforcement learning (RL) and backstepping technique for the canonical nonlinear unknown dynamic system. Since such dynamic system contains multiple state variables with differential relation, the backstepping technique is considered by making a virtual control sequence in accordance with Lyapunov functions. In the last backstepping step, the optimized actual control is derived by performing the RL under identifier-critic-actor structure, where RL is to overcome the difficulty coming from solving Hamilton-Jacobi-Bellman (HJB) equation. Different from the traditional RL optimizing methods that find the RL updating laws from the square of the HJB equation's approximation, this optimized control is to find the RL training laws from the negative gradient of a simple positive definite function, which is equivalent to the HJB equation. The result shows that this optimized control can obviously alleviate the algorithm complexity. Meanwhile, it can remove the requirement of known dynamic as well. Finally, theory and simulation indicate the feasibility of this optimized control.","PeriodicalId":501055,"journal":{"name":"Optimal Control Applications and Methods","volume":"2014 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139968688","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}
To improve the convergence of the gradient iterative (GI) algorithm and the Jacobi-gradient iterative (JGI) algorithm [Bayoumi, Appl Math Inf Sci, 2021], a shift-splitting Jacobi-gradient iterative (SSJGI) algorithm for solving the matrix equation
为了提高梯度迭代（GI）算法和雅各比梯度迭代（JGI）算法[Bayoumi，Appl Math Inf Sci，2021]的收敛性，本文提出了一种基于系数矩阵拆分的求解矩阵方程 A𝒱-𝒱‾B=C的移位拆分雅各比梯度迭代（SSJGI）算法。所提出的算法在某些条件下对任何初始值都能收敛到精确解。为了证明 SSJGI 算法的有效性，并将其与 GI 算法和 JGI 算法 [Bayoumi, Appl Math Inf Sci, 2021] 进行比较，本文提供了数值示例。
{"title":"A shift-splitting Jacobi-gradient iterative algorithm for solving the matrix equation A𝒱−𝒱‾B=C","authors":"Ahmed M. E. Bayoumi","doi":"10.1002/oca.3112","DOIUrl":"https://doi.org/10.1002/oca.3112","url":null,"abstract":"To improve the convergence of the gradient iterative (GI) algorithm and the Jacobi-gradient iterative (JGI) algorithm [Bayoumi, <i>Appl Math Inf Sci</i>, 2021], a shift-splitting Jacobi-gradient iterative (SSJGI) algorithm for solving the matrix equation <mjx-container aria-label=\"upper A script upper V minus script upper V overbar upper B equals upper C\" ctxtmenu_counter=\"1\" ctxtmenu_oldtabindex=\"1\" jax=\"CHTML\" role=\"application\" sre-explorer- style=\"font-size: 103%; position: relative;\" tabindex=\"0\"><mjx-math aria-hidden=\"true\"><mjx-mrow data-semantic-children=\"13,8\" data-semantic-content=\"7\" data-semantic- data-semantic-role=\"equality\" data-semantic-speech=\"upper A script upper V minus script upper V overbar upper B equals upper C\" data-semantic-type=\"relseq\"><mjx-mrow data-semantic-children=\"10,12\" data-semantic-content=\"2\" data-semantic- data-semantic-parent=\"14\" data-semantic-role=\"subtraction\" data-semantic-type=\"infixop\"><mjx-mrow data-semantic-annotation=\"clearspeak:simple;clearspeak:unit\" data-semantic-children=\"0,1\" data-semantic-content=\"9\" data-semantic- data-semantic-parent=\"13\" data-semantic-role=\"implicit\" data-semantic-type=\"infixop\"><mjx-mi data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"italic\" data-semantic- data-semantic-parent=\"10\" data-semantic-role=\"latinletter\" data-semantic-type=\"identifier\"><mjx-c></mjx-c></mjx-mi><mjx-mo data-semantic-added=\"true\" data-semantic- data-semantic-operator=\"infixop,\" data-semantic-parent=\"10\" data-semantic-role=\"multiplication\" data-semantic-type=\"operator\" style=\"margin-left: 0.056em; margin-right: 0.056em;\"><mjx-c></mjx-c></mjx-mo><mjx-mi data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"script\" data-semantic- data-semantic-parent=\"10\" data-semantic-role=\"latinletter\" data-semantic-type=\"identifier\"><mjx-c></mjx-c></mjx-mi></mjx-mrow><mjx-mo data-semantic- data-semantic-operator=\"infixop,−\" data-semantic-parent=\"13\" data-semantic-role=\"subtraction\" data-semantic-type=\"operator\" rspace=\"4\" space=\"4\"><mjx-c></mjx-c></mjx-mo><mjx-mrow data-semantic-annotation=\"clearspeak:unit\" data-semantic-children=\"5,6\" data-semantic-content=\"11\" data-semantic- data-semantic-parent=\"13\" data-semantic-role=\"implicit\" data-semantic-type=\"infixop\"><mjx-mover data-semantic-children=\"3,4\" data-semantic- data-semantic-parent=\"12\" data-semantic-role=\"latinletter\" data-semantic-type=\"overscore\"><mjx-over style=\"padding-bottom: 0.2em; padding-left: 0.171em; margin-bottom: -0.385em;\"><mjx-mo data-semantic- data-semantic-parent=\"5\" data-semantic-role=\"overaccent\" data-semantic-type=\"punctuation\" size=\"s\"><mjx-stretchy-h style=\"width: 1.202em;\"><mjx-ext><mjx-c></mjx-c></mjx-ext></mjx-stretchy-h></mjx-mo></mjx-over><mjx-base><mjx-mi data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"script\" data-semantic- data-semantic-parent=\"5\" data-semantic-role=\"latinletter\" data-semantic-type=\"identifier\"><mjx-c></mjx-c></mjx-mi></mjx-base></mjx-mover><mjx-mo data-semantic-added=\"true","PeriodicalId":501055,"journal":{"name":"Optimal Control Applications and Methods","volume":"120 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139950838","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 investigates the consensus tracking of multi-agent systems (MASs) with general linear dynamics and directed graphs via observer-based event-triggered (ET) control. For each follower, a dynamic event-triggered (DET) unknown input observer (UIO) is constructed to estimate the relative states, which utilizes the discrete relative output information among neighboring agents, and the estimate errors can exponentially converge to zero. Then, an observer-based DET controller, which has the superiority of reducing the communication burden, is presented. Unlike most existing works, a dual ET mechanism of observer and controller is proposed, whose triggering functions are independent of each other. In addition, the time-varying item in the triggering functions is further extended to be a class of positive functions, including some existing exponential functions as its special cases. Under the proposed DET UIO and DET control protocol, it is rigorously demonstrated that consensus tracking can be achieved asymptotically, and Zeno behavior is ruled out. Finally, a numerical example is provided to verify the validity of the results.
本文通过基于观测器的事件触发（ET）控制，研究了具有一般线性动力学和有向图的多代理系统（MAS）的共识跟踪。为每个跟随者构建一个动态事件触发（DET）未知输入观测器（UIO）来估计相对状态，该观测器利用相邻代理之间离散的相对输出信息，估计误差可指数收敛为零。然后，提出了一种基于观测器的 DET 控制器，它具有减轻通信负担的优点。与大多数现有研究不同的是，本文提出了一种观察器和控制器的双 ET 机制，其触发函数是相互独立的。此外，触发函数中的时变项被进一步扩展为一类正 L1$$ {L}_1 $$ 函数，包括一些现有的指数函数作为其特例。在所提出的 DET UIO 和 DET 控制协议下，严格证明了可以渐近地实现共识跟踪，并排除了 Zeno 行为。最后，还提供了一个数值示例来验证结果的正确性。
{"title":"Observer-based output feedback event-triggered consensus tracking for linear multi-agent systems","authors":"Xuxi Zhang, Jinbao Song","doi":"10.1002/oca.3111","DOIUrl":"https://doi.org/10.1002/oca.3111","url":null,"abstract":"This paper investigates the consensus tracking of multi-agent systems (MASs) with general linear dynamics and directed graphs via observer-based event-triggered (ET) control. For each follower, a dynamic event-triggered (DET) unknown input observer (UIO) is constructed to estimate the relative states, which utilizes the discrete relative output information among neighboring agents, and the estimate errors can exponentially converge to zero. Then, an observer-based DET controller, which has the superiority of reducing the communication burden, is presented. Unlike most existing works, a dual ET mechanism of observer and controller is proposed, whose triggering functions are independent of each other. In addition, the time-varying item in the triggering functions is further extended to be a class of positive <mjx-container aria-label=\"upper L 1\" 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\"><mjx-semantics><mjx-mrow><mjx-msub data-semantic-children=\"0,1\" data-semantic- data-semantic-role=\"latinletter\" data-semantic-speech=\"upper L 1\" data-semantic-type=\"subscript\"><mjx-mrow><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-mrow><mjx-script style=\"vertical-align: -0.15em;\"><mjx-mrow size=\"s\"><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\"><mjx-c></mjx-c></mjx-mn></mjx-mrow></mjx-script></mjx-msub></mjx-mrow></mjx-semantics></mjx-math><mjx-assistive-mml aria-hidden=\"true\" display=\"inline\" unselectable=\"on\"><math altimg=\"/cms/asset/dcbe03f0-0356-4c09-8e6d-1ffcc43333ba/oca3111-math-0001.png\" xmlns=\"http://www.w3.org/1998/Math/MathML\"><semantics><mrow><msub data-semantic-=\"\" data-semantic-children=\"0,1\" data-semantic-role=\"latinletter\" data-semantic-speech=\"upper L 1\" data-semantic-type=\"subscript\"><mrow><mi data-semantic-=\"\" data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"italic\" data-semantic-parent=\"2\" data-semantic-role=\"latinletter\" data-semantic-type=\"identifier\">L</mi></mrow><mrow><mn data-semantic-=\"\" data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"normal\" data-semantic-parent=\"2\" data-semantic-role=\"integer\" data-semantic-type=\"number\">1</mn></mrow></msub></mrow>$$ {L}_1 $$</annotation></semantics></math></mjx-assistive-mml></mjx-container> functions, including some existing exponential functions as its special cases. Under the proposed DET UIO and DET control protocol, it is rigorously demonstrated that consensus tracking can be achieved asymptotically, and Zeno behavior is ruled out. Finally, a numerical example is provided to verify the validity of the results.","PeriodicalId":501055,"journal":{"name":"Optimal Control Applications and Methods","volume":"143 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139950814","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}
Optimal control has become a tool of choice for in silico optimization of drug infusion protocols, as is the case in cancer therapy. The relatively less developed area of optimal control for epidemiology has received considerable attention recently due to the Covid-19 pandemic.
Though apparently different, these two contexts usually involve models-whether finite-dimensional or infinite dimensional-which come from population dynamics, while considering cost functionals of the same type. Furthermore, they often encounter the same specific difficulties, such as considerable model uncertainty and require ad-hoc techniques to make optimal control strategies implementable (frequency of drug infusions, discrete controls).
Consequently, optimal control techniques used and developed in the Special Issue share strong similarities in how they tackle control problems arising in therapeutics and epidemiology.
{"title":"Optimal control in therapeutics and epidemiology","authors":"Camille Pouchol, Nastassia Pouradier Duteil","doi":"10.1002/oca.3114","DOIUrl":"https://doi.org/10.1002/oca.3114","url":null,"abstract":"<h2> Context</h2>\u0000<p>Optimal control has become a tool of choice for in silico optimization of drug infusion protocols, as is the case in cancer therapy. The relatively less developed area of optimal control for epidemiology has received considerable attention recently due to the Covid-19 pandemic.</p>\u0000<p>Though apparently different, these two contexts usually involve models-whether finite-dimensional or infinite dimensional-which come from population dynamics, while considering cost functionals of the same type. Furthermore, they often encounter the same specific difficulties, such as considerable model uncertainty and require ad-hoc techniques to make optimal control strategies implementable (frequency of drug infusions, discrete controls).</p>\u0000<p>Consequently, optimal control techniques used and developed in the Special Issue share strong similarities in how they tackle control problems arising in therapeutics and epidemiology.</p>","PeriodicalId":501055,"journal":{"name":"Optimal Control Applications and Methods","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139950756","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 today's electric power network, fast changes are the norm, and auxiliary services such as automatic generation control (AGC) play a crucial role in maintaining the quality of the power supply. AGC ensures a balance between power generation, demand, and losses to sustain frequency stability and variation in tie-line power within a set limit, even when the load changes. To accomplish this, it is always beneficial to consider new approaches to controlling the situation. This paper introduces the design of new AGC regulators that consider deviation in DC tie-line power as an extra control variable for the turbine controller. In this study, three different deregulated power systems (containing two control areas), namely three thermal generations, the combination of one thermal and two hydro generations, and three hydro generations, including two power distribution companies (DISCOs) in each control area. These optimal AGC regulators are implemented in the proposed thermal–thermal–thermal system to carry out the various power contracts. The results have shown that the dynamic outcomes meet the AGC standards. To improve further dynamic results and the proposed systems' stability margins by incorporating redox flow batteries (RFB). In actual operating conditions, the system parameters do not remain constant due to the aging effect, assumptions made in simplifying the mathematical model, etc. Thus, ±50% deviations in the nominal value of system parameters to assess how well the optimal AGC regulators perform in the system under investigation. The suggested realistic AGC system incorporates many parameter fluctuations and works well with the optimal AGC regulators developed for the proposed plans. This study expanded to include a two-area, thermal-hydro-hydro (THH) system under a deregulated framework connected via an asynchronous transmission link with or without a thyristor-controlled phase shifter (TCPS) and RFB. The genetic algorithm (GA) can solve many issues and has global search, flexibility to varied problem types, intrinsic parallelism, and the ability to handle massive, complex search spaces. Thus, a two-area deregulated hydro-hydro-hydro system with parallel tie-lines utilizes GA-PID, GA-FOPID, and GA-(1 + PI)-FOPID controllers. Moreover, a random load disturbance (RLD) is employed in each section of the offered approach to show the resilience and elite performance of the proposed control strategy. To produce various dynamic reactions in the plans, MATLAB software version R2013a is employed.
{"title":"Design and analysis of optimal AGC regulator for multi-area power systems with TCPS and energy storage unit in deregulated environment","authors":"Ram Naresh Mishra, Narendra Kumar, Devendra Kumar Chaturvedi","doi":"10.1002/oca.3106","DOIUrl":"https://doi.org/10.1002/oca.3106","url":null,"abstract":"In today's electric power network, fast changes are the norm, and auxiliary services such as automatic generation control (AGC) play a crucial role in maintaining the quality of the power supply. AGC ensures a balance between power generation, demand, and losses to sustain frequency stability and variation in tie-line power within a set limit, even when the load changes. To accomplish this, it is always beneficial to consider new approaches to controlling the situation. This paper introduces the design of new AGC regulators that consider deviation in DC tie-line power as an extra control variable for the turbine controller. In this study, three different deregulated power systems (containing two control areas), namely three thermal generations, the combination of one thermal and two hydro generations, and three hydro generations, including two power distribution companies (DISCOs) in each control area. These optimal AGC regulators are implemented in the proposed thermal–thermal–thermal system to carry out the various power contracts. The results have shown that the dynamic outcomes meet the AGC standards. To improve further dynamic results and the proposed systems' stability margins by incorporating redox flow batteries (RFB). In actual operating conditions, the system parameters do not remain constant due to the aging effect, assumptions made in simplifying the mathematical model, etc. Thus, ±50% deviations in the nominal value of system parameters to assess how well the optimal AGC regulators perform in the system under investigation. The suggested realistic AGC system incorporates many parameter fluctuations and works well with the optimal AGC regulators developed for the proposed plans. This study expanded to include a two-area, thermal-hydro-hydro (THH) system under a deregulated framework connected via an asynchronous transmission link with or without a thyristor-controlled phase shifter (TCPS) and RFB. The genetic algorithm (GA) can solve many issues and has global search, flexibility to varied problem types, intrinsic parallelism, and the ability to handle massive, complex search spaces. Thus, a two-area deregulated hydro-hydro-hydro system with parallel tie-lines utilizes GA-PID, GA-FOPID, and GA-(1 + PI)-FOPID controllers. Moreover, a random load disturbance (RLD) is employed in each section of the offered approach to show the resilience and elite performance of the proposed control strategy. To produce various dynamic reactions in the plans, MATLAB software version R2013a is employed.","PeriodicalId":501055,"journal":{"name":"Optimal Control Applications and Methods","volume":"125 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139764198","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}
Maximilian Pierer von Esch, Andreas Völz, Knut Graichen
This paper presents synchronous as well as asynchronous formulations of the alternating direction method of multipliers (ADMM) for solving continuous-time nonlinear distributed model predictive control (DMPC) problems. It is shown that the optimal control problems of certain system classes can be transformed to fit the consensus-based ADMM variant problem formulation. The arising subproblems are solved locally on the agent level while the consensus step is solved centrally by a coordinator. Furthermore, the convergence of the synchronous and asynchronous ADMM algorithms to their respective first-order optimality conditions is presented in a continuous-time setting. The algorithm is applied to different example systems for which the convergence behavior and influence of the individual algorithmic parameters are investigated. The computation time of the agents remains unaffected by the system size and thus demonstrates the applicability to high-scaled systems. Moreover, results show that the asynchronous algorithm performs better in terms of execution time when compared to its synchronous counterpart.
{"title":"Asynchronous ADMM for nonlinear continuous-time systems","authors":"Maximilian Pierer von Esch, Andreas Völz, Knut Graichen","doi":"10.1002/oca.3104","DOIUrl":"https://doi.org/10.1002/oca.3104","url":null,"abstract":"This paper presents synchronous as well as asynchronous formulations of the alternating direction method of multipliers (ADMM) for solving continuous-time nonlinear distributed model predictive control (DMPC) problems. It is shown that the optimal control problems of certain system classes can be transformed to fit the consensus-based ADMM variant problem formulation. The arising subproblems are solved locally on the agent level while the consensus step is solved centrally by a coordinator. Furthermore, the convergence of the synchronous and asynchronous ADMM algorithms to their respective first-order optimality conditions is presented in a continuous-time setting. The algorithm is applied to different example systems for which the convergence behavior and influence of the individual algorithmic parameters are investigated. The computation time of the agents remains unaffected by the system size and thus demonstrates the applicability to high-scaled systems. Moreover, results show that the asynchronous algorithm performs better in terms of execution time when compared to its synchronous counterpart.","PeriodicalId":501055,"journal":{"name":"Optimal Control Applications and Methods","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139764216","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 paper, we propose a stochastic variance reduction gradient method with adaptive step size, referred to as the SVRG-New BB method, to solve the convex stochastic optimization problem. The method could be roughly viewed as a hybrid of the SVRG algorithm and a new BB step mechanism. Under the condition that the objective function is strongly convex, we provide the linear convergence proof of this algorithm. Numerical experiment results show that the performance of the SVRG-New BB algorithm can surpass other existing algorithms if parameters in the algorithm are properly chosen.
{"title":"A stochastic variance reduced gradient method with adaptive step for stochastic optimization","authors":"Jing Li, Dan Xue, Lei Liu, Rulei Qi","doi":"10.1002/oca.3109","DOIUrl":"https://doi.org/10.1002/oca.3109","url":null,"abstract":"In this paper, we propose a stochastic variance reduction gradient method with adaptive step size, referred to as the SVRG-New BB method, to solve the convex stochastic optimization problem. The method could be roughly viewed as a hybrid of the SVRG algorithm and a new BB step mechanism. Under the condition that the objective function is strongly convex, we provide the linear convergence proof of this algorithm. Numerical experiment results show that the performance of the SVRG-New BB algorithm can surpass other existing algorithms if parameters in the algorithm are properly chosen.","PeriodicalId":501055,"journal":{"name":"Optimal Control Applications and Methods","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139764220","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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