The topic of this paper is the design of two fractional order schemes, based on a state feedback for linear integer order system. In the first one of the state feedback is associated with a fractional order integral () controller. In the second structure the state feedback is associated with a fractional order proportional integral () controller. With such controllers, the closed loop system with state feedback described by the state equations splits in n‐subsystems with different fractional orders derivatives of the state variable. In order to find the optimal parameters value of both controllers () and (), a multi‐objective particle swarm optimization algorithm is used, with the integral of absolute error, the overshoot , the Buslowicz stability criterion are considered as objective functions. The multi‐objective integral fractional order controller and the multi‐objective proportional integral fractional order controller are applied to stabilize the inverted pendulum‐cart system (IP‐C), and their performance is compared to the fractional order controller. The simulation results of these innovative controllers are also compared with those obtained by conventional proportional–integral–derivative and fractional order proportional–integral–derivative controllers. The robustness of the proposed controllers against disturbances is investigated through simulation runs, considering the non‐linear model of the IP‐C system. The obtained results demonstrate that our approach not only leads to high effectiveness but also showcases remarkable robustness, supported by both simulation and experimental results.
本文的主题是基于线性整数阶系统的状态反馈，设计两种分数阶方案。在第一种方案中，状态反馈与分数阶积分（）控制器相关联。在第二种结构中，状态反馈与分数阶比例积分（）控制器相关联。有了这些控制器，由状态方程描述的带有状态反馈的闭环系统就会分裂成 n 个具有不同分数阶状态变量导数的子系统。为了找到控制器（）和（）的最优参数值，使用了多目标粒子群优化算法，并将绝对误差积分、过冲和 Buslowicz 稳定性准则作为目标函数。将多目标积分分数阶控制器和多目标比例积分分数阶控制器用于稳定倒立摆-小车系统（IP-C），并将它们的性能与分数阶控制器进行了比较。这些创新控制器的仿真结果还与传统的比例积分派生控制器和分数阶比例积分派生控制器的仿真结果进行了比较。考虑到 IP-C 系统的非线性模型，通过模拟运行研究了所提出的控制器对干扰的鲁棒性。仿真和实验结果表明，我们的方法不仅高效，而且具有显著的鲁棒性。
{"title":"Design of fractional MOIF and MOPIF controller using PSO algorithm for the stabilization of an inverted pendulum‐cart system","authors":"Fatima Cheballah, R. Mellah, Abdelhakim Saim","doi":"10.1049/cth2.12648","DOIUrl":"https://doi.org/10.1049/cth2.12648","url":null,"abstract":"The topic of this paper is the design of two fractional order schemes, based on a state feedback for linear integer order system. In the first one of the state feedback is associated with a fractional order integral () controller. In the second structure the state feedback is associated with a fractional order proportional integral () controller. With such controllers, the closed loop system with state feedback described by the state equations splits in n‐subsystems with different fractional orders derivatives of the state variable. In order to find the optimal parameters value of both controllers () and (), a multi‐objective particle swarm optimization algorithm is used, with the integral of absolute error, the overshoot , the Buslowicz stability criterion are considered as objective functions. The multi‐objective integral fractional order controller and the multi‐objective proportional integral fractional order controller are applied to stabilize the inverted pendulum‐cart system (IP‐C), and their performance is compared to the fractional order controller. The simulation results of these innovative controllers are also compared with those obtained by conventional proportional–integral–derivative and fractional order proportional–integral–derivative controllers. The robustness of the proposed controllers against disturbances is investigated through simulation runs, considering the non‐linear model of the IP‐C system. The obtained results demonstrate that our approach not only leads to high effectiveness but also showcases remarkable robustness, supported by both simulation and experimental results.","PeriodicalId":502998,"journal":{"name":"IET Control Theory & Applications","volume":"73 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140741544","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}
Given the unpredictable and intermittent nature of wind energy, precise forecasting of wind power is crucial for ensuring the safe and stable operation of power systems. To reduce the influence of noise data on the robustness of wind power prediction, a wind power prediction method is proposed that leverages an enhanced multi‐objective sand cat swarm algorithm (MO‐SCSO) and a self‐paced long short‐term memory network (spLSTM). First, the actual wind power data is processed into time series as input and output. Then, the progressive advantage of self‐paced learning is used to effectively solve the instability caused by noisy data during long short‐term memory network (LSTM) training. Following this, the improved MO‐SCSO is employed to iteratively optimize the hyperparameters of spLSTM. Ultimately, a combined MO‐SCSO‐spLSTM model is constructed for wind power prediction. This model is validated with the data of onshore wind farms in Austria and offshore wind farms in Denmark. The experimental results show that compared with the traditional LSTM prediction method, the proposed method has better prediction accuracy and robustness. Specifically, in the onshore and offshore wind power prediction experiments, the proposed method reduces the minimum MAE by 5.44% and 4.96%, respectively, and reduces the MAE range by 4.45% and 17.21%, respectively, which could be conducive to the safe and stable operation of power system.
{"title":"Self‐paced learning long short‐term memory based on intelligent optimization for robust wind power prediction","authors":"Shun Yang, Xiaofei Deng, Dongran Song","doi":"10.1049/cth2.12644","DOIUrl":"https://doi.org/10.1049/cth2.12644","url":null,"abstract":"Given the unpredictable and intermittent nature of wind energy, precise forecasting of wind power is crucial for ensuring the safe and stable operation of power systems. To reduce the influence of noise data on the robustness of wind power prediction, a wind power prediction method is proposed that leverages an enhanced multi‐objective sand cat swarm algorithm (MO‐SCSO) and a self‐paced long short‐term memory network (spLSTM). First, the actual wind power data is processed into time series as input and output. Then, the progressive advantage of self‐paced learning is used to effectively solve the instability caused by noisy data during long short‐term memory network (LSTM) training. Following this, the improved MO‐SCSO is employed to iteratively optimize the hyperparameters of spLSTM. Ultimately, a combined MO‐SCSO‐spLSTM model is constructed for wind power prediction. This model is validated with the data of onshore wind farms in Austria and offshore wind farms in Denmark. The experimental results show that compared with the traditional LSTM prediction method, the proposed method has better prediction accuracy and robustness. Specifically, in the onshore and offshore wind power prediction experiments, the proposed method reduces the minimum MAE by 5.44% and 4.96%, respectively, and reduces the MAE range by 4.45% and 17.21%, respectively, which could be conducive to the safe and stable operation of power system.","PeriodicalId":502998,"journal":{"name":"IET Control Theory & Applications","volume":"4 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140748233","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}
Deyi Fu, Shiyao Qin, Ling-Yun Kong, Yang Xue, Lice Gong, Anqing Wang
With the rapid development of wind power, the power performance and mechanical load characteristics of wind turbine are simultaneously considered and focused. Normally, wind turbine senses the incoming flow characteristics through the nacelle mounted anemometer, due to the inability to perceive the characteristics of wind speed in advance, the control strategy makes the wind turbine itself to be at a passive state during the operation process. In this paper, a wind turbine mechanical load optimization control strategy based on an accurate wind speed estimator with time series Broad Learning System Method (BLSM) is designed, simulated and also verified. Firstly, the basic control theory of the BLSM and also a mechanical load optimization controller is designed. Then the OpenFAST is used to conduct a full‐life cycle simulation comparison study on mechanical load characteristics of wind turbine before and after the implementation of the optimization control strategy. Finally, a field empirical mechanical load test is performed on the wind turbine, which is configured with BLSM mechanical load optimization control technology. The findings indicate that the implementation of this control strategy can significantly mitigate the ultimate and fatigue loads of wind turbines, particularly the fatigue loads of tower base tilt and roll bending moments, with a reduction rate of approximately 6.2% and 4.3%, respectively.
{"title":"Wind turbine load optimization control and verification based on wind speed estimator with time series broad learning system method","authors":"Deyi Fu, Shiyao Qin, Ling-Yun Kong, Yang Xue, Lice Gong, Anqing Wang","doi":"10.1049/cth2.12635","DOIUrl":"https://doi.org/10.1049/cth2.12635","url":null,"abstract":"With the rapid development of wind power, the power performance and mechanical load characteristics of wind turbine are simultaneously considered and focused. Normally, wind turbine senses the incoming flow characteristics through the nacelle mounted anemometer, due to the inability to perceive the characteristics of wind speed in advance, the control strategy makes the wind turbine itself to be at a passive state during the operation process. In this paper, a wind turbine mechanical load optimization control strategy based on an accurate wind speed estimator with time series Broad Learning System Method (BLSM) is designed, simulated and also verified. Firstly, the basic control theory of the BLSM and also a mechanical load optimization controller is designed. Then the OpenFAST is used to conduct a full‐life cycle simulation comparison study on mechanical load characteristics of wind turbine before and after the implementation of the optimization control strategy. Finally, a field empirical mechanical load test is performed on the wind turbine, which is configured with BLSM mechanical load optimization control technology. The findings indicate that the implementation of this control strategy can significantly mitigate the ultimate and fatigue loads of wind turbines, particularly the fatigue loads of tower base tilt and roll bending moments, with a reduction rate of approximately 6.2% and 4.3%, respectively.","PeriodicalId":502998,"journal":{"name":"IET Control Theory & Applications","volume":"13 s3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140230601","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}
Keyang Wang, Shaobo Zhou, Yao Yao, Qi Sun, Yintao Wang
This paper considers the problem of a defender agent to guard the target area being attacked by a hostile agent. There are two different agents‐intruder and defender here. The purpose of the defender is to prevent the intruder from approaching the target and if possible, catch the intruder. As the guardian object of the defender, the target area also obstructs its free movement. Therefore, a new geometric method is proposed to solve the problem. Firstly, the winning zones are constructed for the agents using two different methods. When the defender's initial position is in the defender's winning zone, the defender can capture the intruder. When the initial position of the defender is in the winning zone of the intruder, the intruder can successfully attack the target. Subsequently, a method is proposed for determining the dominance region boundary under the influence of target obstruction. Also, the winner can be ascertained by observing whether the dominance region boundary intersects the target area. The optimal strategy is then given according to the different payoff functions. Finally, some examples show the effectiveness of the method. In addition, the conditions and limitations for applying this method to other convex targets are provided.
{"title":"A target defence‐intrusion game with considering the obstructive effect of target","authors":"Keyang Wang, Shaobo Zhou, Yao Yao, Qi Sun, Yintao Wang","doi":"10.1049/cth2.12630","DOIUrl":"https://doi.org/10.1049/cth2.12630","url":null,"abstract":"This paper considers the problem of a defender agent to guard the target area being attacked by a hostile agent. There are two different agents‐intruder and defender here. The purpose of the defender is to prevent the intruder from approaching the target and if possible, catch the intruder. As the guardian object of the defender, the target area also obstructs its free movement. Therefore, a new geometric method is proposed to solve the problem. Firstly, the winning zones are constructed for the agents using two different methods. When the defender's initial position is in the defender's winning zone, the defender can capture the intruder. When the initial position of the defender is in the winning zone of the intruder, the intruder can successfully attack the target. Subsequently, a method is proposed for determining the dominance region boundary under the influence of target obstruction. Also, the winner can be ascertained by observing whether the dominance region boundary intersects the target area. The optimal strategy is then given according to the different payoff functions. Finally, some examples show the effectiveness of the method. In addition, the conditions and limitations for applying this method to other convex targets are provided.","PeriodicalId":502998,"journal":{"name":"IET Control Theory & Applications","volume":"41 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140431634","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}
Xiao Qi, Lingyao Lei, Changhui Yu, Zekai Ma, Taotao Qu, Ming Du, Miaosong Gu
The penetration of offshore wind farms (OWFs) in city‐close power systems is rapidly increasing. System inertia will be further reduced. Active frequency support of wind power is essential to solve the load frequency control (LFC) problem. Here, the dynamic virtual inertia control (VIC) method is employed to enhance frequency stability within the permitted operating states of OWFs. An adaptive distributed model predictive control (DMPC) method is proposed and applied to an interconnected power system. The dynamic VIC‐based LFC model is derived and used to construct the predictive model of DMPC. To expand the adaptation of the analytical linearized model of OWFs in different operating points, the adaptive law is further designed to dynamically adjust the parameters of DMPC. The simulation results demonstrate the effectiveness of the proposed control method. The frequency fluctuations can be well‐restrained under different disturbances.
{"title":"Adaptive distributed MPC based load frequency control with dynamic virtual inertia of offshore wind farms","authors":"Xiao Qi, Lingyao Lei, Changhui Yu, Zekai Ma, Taotao Qu, Ming Du, Miaosong Gu","doi":"10.1049/cth2.12639","DOIUrl":"https://doi.org/10.1049/cth2.12639","url":null,"abstract":"The penetration of offshore wind farms (OWFs) in city‐close power systems is rapidly increasing. System inertia will be further reduced. Active frequency support of wind power is essential to solve the load frequency control (LFC) problem. Here, the dynamic virtual inertia control (VIC) method is employed to enhance frequency stability within the permitted operating states of OWFs. An adaptive distributed model predictive control (DMPC) method is proposed and applied to an interconnected power system. The dynamic VIC‐based LFC model is derived and used to construct the predictive model of DMPC. To expand the adaptation of the analytical linearized model of OWFs in different operating points, the adaptive law is further designed to dynamically adjust the parameters of DMPC. The simulation results demonstrate the effectiveness of the proposed control method. The frequency fluctuations can be well‐restrained under different disturbances.","PeriodicalId":502998,"journal":{"name":"IET Control Theory & Applications","volume":"12 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140434588","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}
Guangji Zhang, Weisheng Yan, Rongxin Cui, Feiyu Ma
To address the tracking problem of the hybrid‐driven underwater legged robot, a control strategy is proposed that decomposes the whole tracking control system into two subsystems: body‐level and actuator‐level. The body‐level subsystem uses a central pattern generator (CPG)‐based controller to plan suitable gaits to meet the required heading and the forward velocity, crucial for accurate tracking in underwater environments. The actuators‐level subsystem employs a cooperative approach between the C‐shaped legs and thrusters of the robot. To execute the intended gait while adhering to actuation constraints and the no‐slip requirement, the torques of the legs are calculated by a model predictive control and feedback compensation (MPCF)‐based controller. Simultaneously, the calculation of the thrusters concerns four aspects to keep the legs attached to the ground and maintain the stable locomotion of the robot. Simulations on the ROS‐Gazebo platform verify the mobility of the robot and demonstrate the effectiveness of the proposed CPG‐MPCF strategy.
为了解决混合驱动水下腿式机器人的跟踪问题，我们提出了一种控制策略，将整个跟踪控制系统分解为两个子系统：身体级和执行器级。身体级子系统使用基于中央模式发生器（CPG）的控制器来规划合适的步态，以满足所需的航向和前进速度，这对水下环境中的精确跟踪至关重要。执行器级子系统在机器人的 C 形腿和推进器之间采用了一种合作方法。为了在执行预定步态的同时遵守执行约束和无滑动要求，腿部扭矩由基于模型预测控制和反馈补偿（MPCF）的控制器计算。同时，推进器的计算涉及四个方面，以保持腿部贴地，维持机器人的稳定运动。在 ROS-Gazebo 平台上进行的模拟验证了机器人的机动性，并证明了所提出的 CPG-MPCF 策略的有效性。
{"title":"Model predictive control‐based tracking controller for hybrid‐driven underwater legged robot","authors":"Guangji Zhang, Weisheng Yan, Rongxin Cui, Feiyu Ma","doi":"10.1049/cth2.12604","DOIUrl":"https://doi.org/10.1049/cth2.12604","url":null,"abstract":"To address the tracking problem of the hybrid‐driven underwater legged robot, a control strategy is proposed that decomposes the whole tracking control system into two subsystems: body‐level and actuator‐level. The body‐level subsystem uses a central pattern generator (CPG)‐based controller to plan suitable gaits to meet the required heading and the forward velocity, crucial for accurate tracking in underwater environments. The actuators‐level subsystem employs a cooperative approach between the C‐shaped legs and thrusters of the robot. To execute the intended gait while adhering to actuation constraints and the no‐slip requirement, the torques of the legs are calculated by a model predictive control and feedback compensation (MPCF)‐based controller. Simultaneously, the calculation of the thrusters concerns four aspects to keep the legs attached to the ground and maintain the stable locomotion of the robot. Simulations on the ROS‐Gazebo platform verify the mobility of the robot and demonstrate the effectiveness of the proposed CPG‐MPCF strategy.","PeriodicalId":502998,"journal":{"name":"IET Control Theory & Applications","volume":"12 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139962554","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 addresses the problem of achieving finite‐time fault‐tolerant consensus control for a class of non‐linear fractional‐order multi‐agent systems (NFO‐MAS) using finite‐time fault detection and estimation, as well as a finite‐time state observer. To achieve this, a specific lemma is utilized to rewrite the high‐order model of NFO‐MAS as a lower‐order NFO unique system. By employing new identification rules and introducing a fault estimation method, both the state variables and faults of the agents are estimated within a finite time. Subsequently, a finite‐time sliding mode control law is designed based on the estimated fault and the state variables obtained from the proposed finite‐time observer to achieve consensus within a finite time for the fractional‐order non‐linear MAS. The stability of the fault estimation, state observer, and consensus controller is proven using the finite‐time Lyapunov theory. The effectiveness of the proposed approach is demonstrated through numerical simulations.
本文探讨了如何利用有限时间故障检测和估计以及有限时间状态观测器，为一类非线性分数阶多代理系统（NFO-MAS）实现有限时间容错共识控制的问题。为实现这一目标，利用一个特定的阶式将 NFO-MAS 的高阶模型重写为一个低阶 NFO 唯一系统。通过采用新的识别规则和引入故障估计方法，可以在有限时间内对代理的状态变量和故障进行估计。随后，根据故障估计和有限时间观测器得到的状态变量设计有限时间滑模控制法则，从而在有限时间内实现分数阶非线性 MAS 的共识。利用有限时间 Lyapunov 理论证明了故障估计、状态观测器和共识控制器的稳定性。通过数值模拟证明了所提方法的有效性。
{"title":"Hybrid finite‐time fault‐tolerant consensus control of non‐linear fractional order multi‐agent systems based on fault detection and estimation","authors":"Mahmood Nazifi, M. Pourgholi","doi":"10.1049/cth2.12627","DOIUrl":"https://doi.org/10.1049/cth2.12627","url":null,"abstract":"This paper addresses the problem of achieving finite‐time fault‐tolerant consensus control for a class of non‐linear fractional‐order multi‐agent systems (NFO‐MAS) using finite‐time fault detection and estimation, as well as a finite‐time state observer. To achieve this, a specific lemma is utilized to rewrite the high‐order model of NFO‐MAS as a lower‐order NFO unique system. By employing new identification rules and introducing a fault estimation method, both the state variables and faults of the agents are estimated within a finite time. Subsequently, a finite‐time sliding mode control law is designed based on the estimated fault and the state variables obtained from the proposed finite‐time observer to achieve consensus within a finite time for the fractional‐order non‐linear MAS. The stability of the fault estimation, state observer, and consensus controller is proven using the finite‐time Lyapunov theory. The effectiveness of the proposed approach is demonstrated through numerical simulations.","PeriodicalId":502998,"journal":{"name":"IET Control Theory & Applications","volume":"118 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139836706","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 considers the problem of communication protocols between leaders and its followers for motion planning in an initially partially known environment. In this setting, the leader observes the environment information to satisfy its own local objective and and the follower completes its own local objective by estimating the states of the leader and communicating with the leader to update its knowledge about the environment when it is necessary, where the local objectives can be expressed in temporal logic. A verifier construction is built first to contain all possible communication protocols between the leaders and the followers. Then, a two‐step synthesis procedure is proposed to capture all feasible communication protocol that satisfy the local objectives for the leader and follower, respectively. In the first step, a sub‐verifier is synthesized to satisfy the objective of the follower. In the second step, based on the obtained sub‐verifier, an iterative algorithm is proposed to extract communication protocols such that the objectives of the leader and follower are satisfied, respectively. A running example is provided to illustrate the proposed procedures.
{"title":"A leader‐follower communication protocol for motion planning in partially known environments under temporal logic specifications","authors":"Xiaohong Yan, Yingying Liu, Renwen Chen, Wei Duan","doi":"10.1049/cth2.12636","DOIUrl":"https://doi.org/10.1049/cth2.12636","url":null,"abstract":"This paper considers the problem of communication protocols between leaders and its followers for motion planning in an initially partially known environment. In this setting, the leader observes the environment information to satisfy its own local objective and and the follower completes its own local objective by estimating the states of the leader and communicating with the leader to update its knowledge about the environment when it is necessary, where the local objectives can be expressed in temporal logic. A verifier construction is built first to contain all possible communication protocols between the leaders and the followers. Then, a two‐step synthesis procedure is proposed to capture all feasible communication protocol that satisfy the local objectives for the leader and follower, respectively. In the first step, a sub‐verifier is synthesized to satisfy the objective of the follower. In the second step, based on the obtained sub‐verifier, an iterative algorithm is proposed to extract communication protocols such that the objectives of the leader and follower are satisfied, respectively. A running example is provided to illustrate the proposed procedures.","PeriodicalId":502998,"journal":{"name":"IET Control Theory & Applications","volume":"41 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139779753","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 elucidates the use of optimization algorithms to identify the controller parameters employed in adjusting the current and voltage values of loads powered by solar energy systems and battery groups. Parameters for these controllers were independently derived using a combination of ant colony optimization with Levy flight, hybrid firefly‐particle swarm optimization, hybrid gravitation search algorithm‐particle swarm optimization, alongside the implementation of Jaya and whale optimization algorithms. The results from each method were juxtaposed for thorough analysis. In addition, three distinct Maximum Power Point Tracker (MPPT) algorithms were employed in the system: perturbation and observation, open circuit voltage, and incremental conductance (IC). To assess the system’s adaptability to real‐world conditions, it was tested against varying temperatures and sunlight levels. Moreover, potential changes in the loads were considered by varying the load. The efficacy of the controllers was examined by altering both the environment and load. The effectiveness of the controllers was examined by referring to the integral of time‐weighted absolute error value. The system was simulated using MATLAB/Simulink software. This study demonstrates that the fractional‐order PID controller achieves the most effective results, the Jaya algorithm provides the best controller parameters, and the IC technique exhibits the highest performance in MPPT.
{"title":"Exploring solar energy systems: A comparative study of optimization algorithms, MPPTs, and controllers","authors":"Aykut Fatih Güven","doi":"10.1049/cth2.12626","DOIUrl":"https://doi.org/10.1049/cth2.12626","url":null,"abstract":"This study elucidates the use of optimization algorithms to identify the controller parameters employed in adjusting the current and voltage values of loads powered by solar energy systems and battery groups. Parameters for these controllers were independently derived using a combination of ant colony optimization with Levy flight, hybrid firefly‐particle swarm optimization, hybrid gravitation search algorithm‐particle swarm optimization, alongside the implementation of Jaya and whale optimization algorithms. The results from each method were juxtaposed for thorough analysis. In addition, three distinct Maximum Power Point Tracker (MPPT) algorithms were employed in the system: perturbation and observation, open circuit voltage, and incremental conductance (IC). To assess the system’s adaptability to real‐world conditions, it was tested against varying temperatures and sunlight levels. Moreover, potential changes in the loads were considered by varying the load. The efficacy of the controllers was examined by altering both the environment and load. The effectiveness of the controllers was examined by referring to the integral of time‐weighted absolute error value. The system was simulated using MATLAB/Simulink software. This study demonstrates that the fractional‐order PID controller achieves the most effective results, the Jaya algorithm provides the best controller parameters, and the IC technique exhibits the highest performance in MPPT.","PeriodicalId":502998,"journal":{"name":"IET Control Theory & Applications","volume":" 1016","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139787135","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}
Hongjia Sha, Ju H. Park, Jun Chen, Mingbo Zhu, Chengjie Nan
This paper is concerned with the stability analysis of discrete‐time systems with a time‐varying delay. The conservatism and computation burden are two important factors to evaluate a stability condition. By taking the relationship of two reciprocally convex parts into consideration, a new combined matrix‐separation‐based inequality is proposed that involves only a few free matrices. Moreover, an improved matrix‐injection‐based transformation lemma with the parameter varying within a closed interval is proposed by introducing only one free matrix. By constructing an appropriate Lyapunov–Krasovskii functional and applying the improved methods, a relaxed stability condition is consequently obtained with a small number of decision variables. Two numerical examples are given to show the merits of the proposed methods.
{"title":"Stability analysis of discrete‐time systems with a time‐varying delay via improved methods","authors":"Hongjia Sha, Ju H. Park, Jun Chen, Mingbo Zhu, Chengjie Nan","doi":"10.1049/cth2.12632","DOIUrl":"https://doi.org/10.1049/cth2.12632","url":null,"abstract":"This paper is concerned with the stability analysis of discrete‐time systems with a time‐varying delay. The conservatism and computation burden are two important factors to evaluate a stability condition. By taking the relationship of two reciprocally convex parts into consideration, a new combined matrix‐separation‐based inequality is proposed that involves only a few free matrices. Moreover, an improved matrix‐injection‐based transformation lemma with the parameter varying within a closed interval is proposed by introducing only one free matrix. By constructing an appropriate Lyapunov–Krasovskii functional and applying the improved methods, a relaxed stability condition is consequently obtained with a small number of decision variables. Two numerical examples are given to show the merits of the proposed methods.","PeriodicalId":502998,"journal":{"name":"IET Control Theory & Applications","volume":"65 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139851913","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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