Robotic flexible needles have gained significant attention in minimally invasive medical procedures due to their ability to navigate complex anatomical structures and reach targets with high precision. Addressing the complex control problem in a 3‐dimensional environment, a Koopman‐based data‐driven control strategy is proposed in this paper. First, a 3‐dimensional (3D) path tracking problem is modeled using the simplified high‐dimensional bicycle model with the puncture kinematic. Then, with the Koopman operator theory, a finite linear approximation is designed and trained to simplify the nonlinear system of flexible needles. Finally, based on the linear approximation, a Koopman‐based model predictive control (MPC) scheme is proposed to realize 3‐dimensional path tracking for flexible needles. Based on simulations, the linear approximation and data‐driven control strategy are validated.
{"title":"Koopman‐based 3‐dimensional path following control for robotic flexible needles","authors":"Nianhua Zhang, Zhi Qi, Jicheng Chen, Hui Zhang, Hamid Reza Karimi","doi":"10.1002/oca.3170","DOIUrl":"https://doi.org/10.1002/oca.3170","url":null,"abstract":"Robotic flexible needles have gained significant attention in minimally invasive medical procedures due to their ability to navigate complex anatomical structures and reach targets with high precision. Addressing the complex control problem in a 3‐dimensional environment, a Koopman‐based data‐driven control strategy is proposed in this paper. First, a 3‐dimensional (3D) path tracking problem is modeled using the simplified high‐dimensional bicycle model with the puncture kinematic. Then, with the Koopman operator theory, a finite linear approximation is designed and trained to simplify the nonlinear system of flexible needles. Finally, based on the linear approximation, a Koopman‐based model predictive control (MPC) scheme is proposed to realize 3‐dimensional path tracking for flexible needles. Based on simulations, the linear approximation and data‐driven control strategy are validated.","PeriodicalId":501055,"journal":{"name":"Optimal Control Applications and Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141737996","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}
SummaryThis paper investigates an observer‐based optimal fault‐tolerant tracking control problem for interconnected nonlinear systems with input constraints and mismatched disturbances via adaptive dynamic programming (ADP). Firstly, an augmented system consisting of tracking error and the reference trajectory is constructed, and then the original optimal tracking control problem is transformed into the optimal regulation control problem of the augmented system. An integral sliding‐mode‐based optimal fault‐tolerant control scheme is developed to eliminate the effect of actuator faults and guarantee the optimal control performance. Furthermore, a neural network‐based observer is designed to identify the completely unknown system dynamics based on the input‐output data, thereby relaxing the restriction on system dynamics. Subsequently, the modified Hamilton‐Jacobi‐Bellman equations are solved by using the ADP algorithm under a critic network framework. According to the Lyapunov approach, all signals in the closed‐loop augmented system are uniformly ultimately bounded. Finally, the effectiveness of the developed control scheme is demonstrated via simulation results.
{"title":"Observer‐based optimal fault‐tolerant tracking control for input‐constrained interconnected nonlinear systems with mismatched disturbances","authors":"Shihui Liu, Ning Xu, Ning Zhao, Liang Zhang","doi":"10.1002/oca.3173","DOIUrl":"https://doi.org/10.1002/oca.3173","url":null,"abstract":"SummaryThis paper investigates an observer‐based optimal fault‐tolerant tracking control problem for interconnected nonlinear systems with input constraints and mismatched disturbances via adaptive dynamic programming (ADP). Firstly, an augmented system consisting of tracking error and the reference trajectory is constructed, and then the original optimal tracking control problem is transformed into the optimal regulation control problem of the augmented system. An integral sliding‐mode‐based optimal fault‐tolerant control scheme is developed to eliminate the effect of actuator faults and guarantee the optimal control performance. Furthermore, a neural network‐based observer is designed to identify the completely unknown system dynamics based on the input‐output data, thereby relaxing the restriction on system dynamics. Subsequently, the modified Hamilton‐Jacobi‐Bellman equations are solved by using the ADP algorithm under a critic network framework. According to the Lyapunov approach, all signals in the closed‐loop augmented system are uniformly ultimately bounded. Finally, the effectiveness of the developed control scheme is demonstrated via simulation results.","PeriodicalId":501055,"journal":{"name":"Optimal Control Applications and Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141737995","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}
By fully taking the effects of data injection attacks, nonlinear disturbances and limited communication resources, the stability of Lurie industrial cyber physical systems with the time‐varying delays is investigated. The two channels event‐triggered dynamic robust control (ERHIC) strategy is proposed for achieving the stability of systems. On one hand, a dynamic output feedback controller is designed to ensure the stable operation of the system under attacks. On other hand, the measurement channel and the control channel (two channels) event‐triggered mechanisms are designed to improve the utilization rate of system resources. The simulation results with the size of capacitor voltage and inductor current under data injection attacks as the object verity the correctness and the effectiveness of the proposed two channels event‐triggered robust H‐infinity control method and the feasibility of the designed dynamic output feedback controller are verified by the simulation results.
{"title":"Two channels event‐triggered robust H‐infinity control of Luire industrial cyber physical systems under data injection attacks","authors":"Yanfei Qin, Ziwen Sun","doi":"10.1002/oca.3187","DOIUrl":"https://doi.org/10.1002/oca.3187","url":null,"abstract":"By fully taking the effects of data injection attacks, nonlinear disturbances and limited communication resources, the stability of Lurie industrial cyber physical systems with the time‐varying delays is investigated. The two channels event‐triggered dynamic robust control (ERHIC) strategy is proposed for achieving the stability of systems. On one hand, a dynamic output feedback controller is designed to ensure the stable operation of the system under attacks. On other hand, the measurement channel and the control channel (two channels) event‐triggered mechanisms are designed to improve the utilization rate of system resources. The simulation results with the size of capacitor voltage and inductor current under data injection attacks as the object verity the correctness and the effectiveness of the proposed two channels event‐triggered robust H‐infinity control method and the feasibility of the designed dynamic output feedback controller are verified by the simulation results.","PeriodicalId":501055,"journal":{"name":"Optimal Control Applications and Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141642727","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}
Sanjay Joseph Chacko, Rohit Kumar, Rajesh Joseph Abraham
The inverted pendulum‐cart (IPC) control problem has long been a benchmark in the field of control systems due to its inherent instability and nonlinear dynamics. The linear quadratic regulator (LQR) control technique has been proven to be effective in stabilizing the inverted pendulum; however, the challenge lies in finding the optimal control gains that provide the best performance. This article presents a method to address the LQR control design problem for the IPC system which is compared against a particle swarm optimization based LQR and a neural network optimized LQR. The method provides a deterministic approach to finding the weighing matrices Q and R in accordance with the time domain characteristics chosen by the designer, such as settling time and maximum peak overshoot. Results from MATLAB simulations indicate that the suggested strategy has good performance.
{"title":"LQR controller performance via particle swarm optimization and neural networks","authors":"Sanjay Joseph Chacko, Rohit Kumar, Rajesh Joseph Abraham","doi":"10.1002/oca.3183","DOIUrl":"https://doi.org/10.1002/oca.3183","url":null,"abstract":"The inverted pendulum‐cart (IPC) control problem has long been a benchmark in the field of control systems due to its inherent instability and nonlinear dynamics. The linear quadratic regulator (LQR) control technique has been proven to be effective in stabilizing the inverted pendulum; however, the challenge lies in finding the optimal control gains that provide the best performance. This article presents a method to address the LQR control design problem for the IPC system which is compared against a particle swarm optimization based LQR and a neural network optimized LQR. The method provides a deterministic approach to finding the weighing matrices Q and R in accordance with the time domain characteristics chosen by the designer, such as settling time and maximum peak overshoot. Results from MATLAB simulations indicate that the suggested strategy has good performance.","PeriodicalId":501055,"journal":{"name":"Optimal Control Applications and Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141649187","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}
Mrinal Ranjan, Ravi Shankar, U. Raj, Sarvesh Kumar, Jitendra Kumar
The latest advancements in Vehicle‐to‐Grid (V2G) technology enable Electric Vehicles (EVs) to contribute to Frequency Regulation (FR), mitigating the impact of uncertain power fluctuations in renewable resources. However, diverse cyber‐attacks pose threats to the Load Frequency Control (LFC) system and smart grid infrastructure, compromising their accuracy and reliability. This research paper focuses on the effects of cyber‐attacks on frequency regulation in a smart grid system that relies on a communication network. The study proposes a two‐area system and a modified IEEE‐39 bus three‐area system, incorporating intermittent solar photovoltaic and wind turbine sources, traditional conventional sources, and electric vehicles. To ensure frequency stabilization and tie‐line power, the researchers introduce a cascade FOPIDN‐(1+TD) controller. Its parameters are optimized using a novel Quasi Opposition Arithmetic Optimization Algorithm (QOAOA). The proposed approach's dynamic response is compared with other commonly used controllers, demonstrating its effectiveness in maintaining stability. The article focuses on the LFC system and presents a novel approach involving a cyber‐physical model. It introduces a method for detecting and defending against cyber‐attacks using deep learning, specifically utilizing the Long‐Short‐Term‐Memory (LSTM) network. The effectiveness of the proposed defense strategy is demonstrated through experiments conducted on both a two‐area interconnected power system and the IEEE‐39 bus system. The outcomes indicate that the suggested defense mechanism effectively maintains acceptable levels of power system frequency and tie‐line power during cyber‐attacks. Additionally, the validity of the controller's performance is verified through real‐time analysis using Hardware‐In‐The‐Loop (HIL) simulation with the OPAL‐RT platform.
{"title":"Cyber‐attack and defense method for load frequency control in smart grid systems with electric vehicles","authors":"Mrinal Ranjan, Ravi Shankar, U. Raj, Sarvesh Kumar, Jitendra Kumar","doi":"10.1002/oca.3184","DOIUrl":"https://doi.org/10.1002/oca.3184","url":null,"abstract":"The latest advancements in Vehicle‐to‐Grid (V2G) technology enable Electric Vehicles (EVs) to contribute to Frequency Regulation (FR), mitigating the impact of uncertain power fluctuations in renewable resources. However, diverse cyber‐attacks pose threats to the Load Frequency Control (LFC) system and smart grid infrastructure, compromising their accuracy and reliability. This research paper focuses on the effects of cyber‐attacks on frequency regulation in a smart grid system that relies on a communication network. The study proposes a two‐area system and a modified IEEE‐39 bus three‐area system, incorporating intermittent solar photovoltaic and wind turbine sources, traditional conventional sources, and electric vehicles. To ensure frequency stabilization and tie‐line power, the researchers introduce a cascade FOPIDN‐(1+TD) controller. Its parameters are optimized using a novel Quasi Opposition Arithmetic Optimization Algorithm (QOAOA). The proposed approach's dynamic response is compared with other commonly used controllers, demonstrating its effectiveness in maintaining stability. The article focuses on the LFC system and presents a novel approach involving a cyber‐physical model. It introduces a method for detecting and defending against cyber‐attacks using deep learning, specifically utilizing the Long‐Short‐Term‐Memory (LSTM) network. The effectiveness of the proposed defense strategy is demonstrated through experiments conducted on both a two‐area interconnected power system and the IEEE‐39 bus system. The outcomes indicate that the suggested defense mechanism effectively maintains acceptable levels of power system frequency and tie‐line power during cyber‐attacks. Additionally, the validity of the controller's performance is verified through real‐time analysis using Hardware‐In‐The‐Loop (HIL) simulation with the OPAL‐RT platform.","PeriodicalId":501055,"journal":{"name":"Optimal Control Applications and Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141646646","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}
Ali Bahmani, M. J. Kiani, S. Nejatian, Mahmoud Zadehbagheri
The paper presents the planning (sizing) of a hybrid islanded system containing only renewable sources including wind turbines, photovoltaics, and bio‐waste energy units for the simultaneous supply of electrical and thermal energy. The mentioned renewable sources are used to supply electrical energy. The bio‐waste unit (BEU) is equipped with combined electrical and thermal technology. It is used along with heat pumps to supply thermal energy. Electrical and thermal storage are employed to make the renewable power output as close as possible to the demand level. Electric storage can be either stationary (e.g., battery) or mobile (e.g., electric vehicles), but thermal storage is as stationary type of storage. In the following, the proposed scheme minimizes construction and maintenance costs imposed by power sources, storage devices, and power electronic converters, and expected storage degradation cost. This plan is bound to the model of operation of sources, storage devices, and power electronic converters. In this model, renewables are the main source of power to supply consumers, where storage is adopted to increase the generation level to make it as close as possible to the demand curve. The uncertain parameters are imposed by the load, renewable phenomena, and mobile storage parameters. To model these uncertainties, stochastic optimization based on unscented transformation is used. Finally, the findings of the paper demonstrate that the suggested scheme succeeds in the economic planning of the system with a simultaneous supply of electrical and thermal energy. The BEU equipped with a combined electricity and heat system along with thermal storage and a heat pump to supply thermal load besides providing electric energy can reduce the planning cost by 2.9% compared with the case with only electrical energy. Also, the mobile storage presence in the hybrid system can reduce the number of stationary storage devices, which alone results in a 7.7% decrease in the planning cost of the hybrid system.
{"title":"Sizing of storage‐based renewable off‐grid system according to supply of electrical and thermal energies considering unscented transformation‐based stochastic optimization: A case study","authors":"Ali Bahmani, M. J. Kiani, S. Nejatian, Mahmoud Zadehbagheri","doi":"10.1002/oca.3176","DOIUrl":"https://doi.org/10.1002/oca.3176","url":null,"abstract":"The paper presents the planning (sizing) of a hybrid islanded system containing only renewable sources including wind turbines, photovoltaics, and bio‐waste energy units for the simultaneous supply of electrical and thermal energy. The mentioned renewable sources are used to supply electrical energy. The bio‐waste unit (BEU) is equipped with combined electrical and thermal technology. It is used along with heat pumps to supply thermal energy. Electrical and thermal storage are employed to make the renewable power output as close as possible to the demand level. Electric storage can be either stationary (e.g., battery) or mobile (e.g., electric vehicles), but thermal storage is as stationary type of storage. In the following, the proposed scheme minimizes construction and maintenance costs imposed by power sources, storage devices, and power electronic converters, and expected storage degradation cost. This plan is bound to the model of operation of sources, storage devices, and power electronic converters. In this model, renewables are the main source of power to supply consumers, where storage is adopted to increase the generation level to make it as close as possible to the demand curve. The uncertain parameters are imposed by the load, renewable phenomena, and mobile storage parameters. To model these uncertainties, stochastic optimization based on unscented transformation is used. Finally, the findings of the paper demonstrate that the suggested scheme succeeds in the economic planning of the system with a simultaneous supply of electrical and thermal energy. The BEU equipped with a combined electricity and heat system along with thermal storage and a heat pump to supply thermal load besides providing electric energy can reduce the planning cost by 2.9% compared with the case with only electrical energy. Also, the mobile storage presence in the hybrid system can reduce the number of stationary storage devices, which alone results in a 7.7% decrease in the planning cost of the hybrid system.","PeriodicalId":501055,"journal":{"name":"Optimal Control Applications and Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141647844","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 article, we focus on the state estimation and formation control problem for a multi‐UAV system, where only parts of UAVs have GPS measurements, that is, the anchors, and the other UAVs measure their relative positions with anchors. With a second‐order discrete‐time model of each UAV in the formation, we analyze the convergence of state estimation (including its expectation and covariance) for commonly used consensus‐based formation control protocols, under the effect of control and measurement noise. More specifically, based on the discrete‐time stability analysis, we obtain the sufficient and necessary conditions for the expectation stability of the formation control. Using the fixed point theorem in positive symmetric matrix subspace and observability analysis, we rigorously prove that the covariance of estimation is bounded if and only if the anchor node set is globally reachable. Based on our analysis, the formation control parameters and system topology can be designed to satisfy sufficient conditions, such that the desired formation pattern can be achieved for the multi‐UAV dynamic system under GPS‐denied environments. Simulation results corroborate the effectiveness of our theoretical results.
{"title":"Convergence analysis of state estimation for UAV formation under GPS‐denied environments","authors":"Yu Ding, Zhengye Wang, Yirui Cong, Xiangke Wang","doi":"10.1002/oca.3159","DOIUrl":"https://doi.org/10.1002/oca.3159","url":null,"abstract":"In this article, we focus on the state estimation and formation control problem for a multi‐UAV system, where only parts of UAVs have GPS measurements, that is, the anchors, and the other UAVs measure their relative positions with anchors. With a second‐order discrete‐time model of each UAV in the formation, we analyze the convergence of state estimation (including its expectation and covariance) for commonly used consensus‐based formation control protocols, under the effect of control and measurement noise. More specifically, based on the discrete‐time stability analysis, we obtain the sufficient and necessary conditions for the expectation stability of the formation control. Using the fixed point theorem in positive symmetric matrix subspace and observability analysis, we rigorously prove that the covariance of estimation is bounded if and only if the anchor node set is globally reachable. Based on our analysis, the formation control parameters and system topology can be designed to satisfy sufficient conditions, such that the desired formation pattern can be achieved for the multi‐UAV dynamic system under GPS‐denied environments. Simulation results corroborate the effectiveness of our theoretical results.","PeriodicalId":501055,"journal":{"name":"Optimal Control Applications and Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141650047","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 is to investigate the consensus control and initialization region optimization for leader‐following multi‐agent systems with time‐varying communication delay and nonidentical consecutive packet dropouts. By combining the Bernoulli distribution model of the data loss and the Hadamard product, the control protocols affected by both communication delay and packet dropouts are utilized to formulate the consensus problem into the mean‐square stability problem of the augmented error systems. Resorting to the Lyapunov function methodology and a structure separation method, a sufficient condition to ensure the consensus is built in the form of linear matrix inequality. Relying on the given Lyapunov function and set‐membership analysis, an optimization procedure is proposed to simultaneously ensure the consensus and enlarge the estimated ellipsoid of initial conditions. The validity of the provided method is illustrated through a comparative simulation.
{"title":"Consensus control and initialization region optimization for leader‐following multi‐agent systems under time‐varying communication delay and consecutive packet dropouts","authors":"Tu Zhang, Guobao Zhang, Yongming Huang","doi":"10.1002/oca.3180","DOIUrl":"https://doi.org/10.1002/oca.3180","url":null,"abstract":"This article is to investigate the consensus control and initialization region optimization for leader‐following multi‐agent systems with time‐varying communication delay and nonidentical consecutive packet dropouts. By combining the Bernoulli distribution model of the data loss and the Hadamard product, the control protocols affected by both communication delay and packet dropouts are utilized to formulate the consensus problem into the mean‐square stability problem of the augmented error systems. Resorting to the Lyapunov function methodology and a structure separation method, a sufficient condition to ensure the consensus is built in the form of linear matrix inequality. Relying on the given Lyapunov function and set‐membership analysis, an optimization procedure is proposed to simultaneously ensure the consensus and enlarge the estimated ellipsoid of initial conditions. The validity of the provided method is illustrated through a comparative simulation.","PeriodicalId":501055,"journal":{"name":"Optimal Control Applications and Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141609518","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}
Elshan Sarkarfarshi, Farzad Hashemzadeh, Peyman Bagheri, Mohammad Amin Rezaei
This paper investigates a networked control system with bounded and time‐varying delays. Also, an observer‐based predictive controller is developed for the active compensation of the network communication delay since it may lead to poor performances or even unstable dynamics for the systems. The existence of the state observer can be established by choosing an appropriate Lyapunov function, and to do this, Luenberger observer gain is computed with Linear Matrix Inequality (LMI) based on the stability conditions of the system. The practical aspect of this research, different from previous works, is the accessibility of the states that are not always available for the system. Controller and observer gains and other essential variables are derived through LMI. In the closed‐loop system, which is modeled as a switching system, the switches are based on communication delays. Using the Lyapunov stability method for switching systems, sufficient LMI conditions are derived to guarantee the stability of the closed‐loop system. Finally, the results of the simulation have demonstrated the performance of the methodology presented.
{"title":"Observer‐based networked predictive controller with bounded and unknown time‐varying delay","authors":"Elshan Sarkarfarshi, Farzad Hashemzadeh, Peyman Bagheri, Mohammad Amin Rezaei","doi":"10.1002/oca.3169","DOIUrl":"https://doi.org/10.1002/oca.3169","url":null,"abstract":"This paper investigates a networked control system with bounded and time‐varying delays. Also, an observer‐based predictive controller is developed for the active compensation of the network communication delay since it may lead to poor performances or even unstable dynamics for the systems. The existence of the state observer can be established by choosing an appropriate Lyapunov function, and to do this, Luenberger observer gain is computed with Linear Matrix Inequality (LMI) based on the stability conditions of the system. The practical aspect of this research, different from previous works, is the accessibility of the states that are not always available for the system. Controller and observer gains and other essential variables are derived through LMI. In the closed‐loop system, which is modeled as a switching system, the switches are based on communication delays. Using the Lyapunov stability method for switching systems, sufficient LMI conditions are derived to guarantee the stability of the closed‐loop system. Finally, the results of the simulation have demonstrated the performance of the methodology presented.","PeriodicalId":501055,"journal":{"name":"Optimal Control Applications and Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141614271","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}
Juntao Liang, Peng Yi, Wei Li, Jiaxuan Zuo, Bo Zhu, Yong Wang
Unmanned aerial vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) are widely used in various fields, and autonomous tracking of UGV by UAV can significantly improve the collaborative capability and operational range of unmanned systems. In order to realize autonomous UAV tracking in complex and cluttered scenarios, this paper presents a vision‐based tracking system for UAV tracking UGV, integrating a visual target tracking model based on deep learning with a local path planning methodology. The system includes a deep learning‐based tracker, a state machine, and an obstacle avoidance planning algorithm. In addition, to improve the robustness of UAV onboard tracking, we introduce a lightweight tracking algorithm based on MobileNetV2 network, which ensures the tracking performance while improving the operation speed. Through real‐world experiments, it is demonstrated that our system can realize autonomous target tracking in cluttered environments without prior information. The proposed tracker exhibits a success rate of 61.7% on the UAV123 dataset and achieves a tracking speed of 45 frames per second (fps) on the NVIDIA Jetson TX2, demonstrating significant advancements in real‐time, efficient unmanned tracking technologies.
{"title":"Real‐time visual tracking design for an unmanned aerial vehicle in cluttered environments","authors":"Juntao Liang, Peng Yi, Wei Li, Jiaxuan Zuo, Bo Zhu, Yong Wang","doi":"10.1002/oca.3175","DOIUrl":"https://doi.org/10.1002/oca.3175","url":null,"abstract":"Unmanned aerial vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) are widely used in various fields, and autonomous tracking of UGV by UAV can significantly improve the collaborative capability and operational range of unmanned systems. In order to realize autonomous UAV tracking in complex and cluttered scenarios, this paper presents a vision‐based tracking system for UAV tracking UGV, integrating a visual target tracking model based on deep learning with a local path planning methodology. The system includes a deep learning‐based tracker, a state machine, and an obstacle avoidance planning algorithm. In addition, to improve the robustness of UAV onboard tracking, we introduce a lightweight tracking algorithm based on MobileNetV2 network, which ensures the tracking performance while improving the operation speed. Through real‐world experiments, it is demonstrated that our system can realize autonomous target tracking in cluttered environments without prior information. The proposed tracker exhibits a success rate of 61.7% on the UAV123 dataset and achieves a tracking speed of 45 frames per second (fps) on the NVIDIA Jetson TX2, demonstrating significant advancements in real‐time, efficient unmanned tracking technologies.","PeriodicalId":501055,"journal":{"name":"Optimal Control Applications and Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141609519","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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