To address the issue of instability or even imbalance in the orientation and attitude control of quadrotor unmanned aerial vehicles(QUAVs)under random disturbances,this paper proposes a distributed antidisturbance dat...To address the issue of instability or even imbalance in the orientation and attitude control of quadrotor unmanned aerial vehicles(QUAVs)under random disturbances,this paper proposes a distributed antidisturbance data-driven event-triggered fusion control method,which achieves efficient fault diagnosis while suppressing random disturbances and mitigating communication conflicts within the QUAV swarm.First,the impact of random disturbances on the UAV swarm is analyzed,and a model for orientation and attitude control of QUAVs under stochastic perturbations is established,with the disturbance gain threshold determined.Second,a fault diagnosis system based on a high-gain observer is designed,constructing a fault gain criterion by integrating orientation and attitude information from QUAVs.Subsequently,a model-free dynamic linearization-based data modeling(MFDLDM)framework is developed using model-free adaptive control,which efficiently fits the nonlinear control model of the QUAV swarm while reducing temporal constraints on control data.On this basis,this paper constructs a distributed data-driven event-triggered controller based on the staggered communication mechanism,which consists of an equivalent QUAV controller and an event-triggered controller,and is able to reduce the communication conflicts while suppressing the influence of random interference.Finally,by incorporating random disturbances into the controller,comparative experiments and physical validations are conducted on the QUAV platforms,fully demonstrating the strong adaptability and robustness of the proposed distributed event-triggered fault-tolerant control system.展开更多
The rapid increase of the scale and the complexity of the controlled plants bring new challenges such as computing power and storage for conventional control systems.Cloud computing is concerned as a powerful solution...The rapid increase of the scale and the complexity of the controlled plants bring new challenges such as computing power and storage for conventional control systems.Cloud computing is concerned as a powerful solution to handle complex large-scale control missions by using sufficient computing resources.However,the computing ability enables more complex devices and more data to be involved and most of the data have not been fully utilized.Meanwhile,it is even impossible to obtain an accurate model of each device in the complex control systems for the model-based control algorithms.Therefore,motivated by the above reasons,we propose a data-driven predictive cloud control system.To achieve the proposed system,a practical data-driven predictive cloud control testbed is established and together a cloud-edge communication scheme is developed.Finally,the simulations and experiments demonstrate the effectiveness of the proposed system.展开更多
This study employs a data-driven methodology that embeds the principle of dimensional invariance into an artificial neural network to automatically identify dominant dimensionless quantities in the penetration of rod ...This study employs a data-driven methodology that embeds the principle of dimensional invariance into an artificial neural network to automatically identify dominant dimensionless quantities in the penetration of rod projectiles into semi-infinite metal targets from experimental measurements.The derived mathematical expressions of dimensionless quantities are simplified by the examination of the exponent matrix and coupling relationships between feature variables.As a physics-based dimension reduction methodology,this way reduces high-dimensional parameter spaces to descriptions involving only a few physically interpretable dimensionless quantities in penetrating cases.Then the relative importance of various dimensionless feature variables on the penetration efficiencies for four impacting conditions is evaluated through feature selection engineering.The results indicate that the selected critical dimensionless feature variables by this synergistic method,without referring to the complex theoretical equations and aiding in the detailed knowledge of penetration mechanics,are in accordance with those reported in the reference.Lastly,the determined dimensionless quantities can be efficiently applied to conduct semi-empirical analysis for the specific penetrating case,and the reliability of regression functions is validated.展开更多
This paper proposes the nonlinear direct data-driven control from theoretical analysis and practical engineering,i.e.,unmanned aerial vehicle(UAV)formation flight system.Firstly,from the theoretical point of view,cons...This paper proposes the nonlinear direct data-driven control from theoretical analysis and practical engineering,i.e.,unmanned aerial vehicle(UAV)formation flight system.Firstly,from the theoretical point of view,consider one nonlinear closedloop system with a nonlinear plant and nonlinear feed-forward controller simultaneously.To avoid the complex identification process for that nonlinear plant,a nonlinear direct data-driven control strategy is proposed to design that nonlinear feed-forward controller only through the input-output measured data sequence directly,whose detailed explicit forms are model inverse method and approximated analysis method.Secondly,from the practical point of view,after reviewing the UAV formation flight system,nonlinear direct data-driven control is applied in designing the formation controller,so that the followers can track the leader’s desired trajectory during one small time instant only through solving one data fitting problem.Since most natural phenomena have nonlinear properties,the direct method must be the better one.Corresponding system identification and control algorithms are required to be proposed for those nonlinear systems,and the direct nonlinear controller design is the purpose of this paper.展开更多
The data-driven fault diagnosis methods can improve the reliability of analog circuits by using the data generated from it. The data have some characteristics, such as randomness and incompleteness, which lead to the ...The data-driven fault diagnosis methods can improve the reliability of analog circuits by using the data generated from it. The data have some characteristics, such as randomness and incompleteness, which lead to the diagnostic results being sensitive to the specific values and random noise. This paper presents a data-driven fault diagnosis method for analog circuits based on the robust competitive agglomeration （RCA）, which can alleviate the incompleteness of the data by clustering with the competing process. And the robustness of the diagnostic results is enhanced by using the approach of robust statistics in RCA. A series of experiments are provided to demonstrate that RCA can classify the incomplete data with a high accuracy. The experimental results show that RCA is robust for the data needed to be classified as well as the parameters needed to be adjusted. The effectiveness of RCA in practical use is demonstrated by two analog circuits.展开更多
Solid oxide fuel cells (SOFCs) are considered to be one of the most important clean,distributed resources. However,SOFCs present a challenging control problem owing to their slow dynamics,nonlinearity and tight operat...Solid oxide fuel cells (SOFCs) are considered to be one of the most important clean,distributed resources. However,SOFCs present a challenging control problem owing to their slow dynamics,nonlinearity and tight operating constraints. A novel data-driven nonlinear control strategy was proposed to solve the SOFC control problem by combining a virtual reference feedback tuning (VRFT) method and support vector machine. In order to fulfill the requirement for fuel utilization and control constraints,a dynamic constraints unit and an anti-windup scheme were adopted. In addition,a feedforward loop was designed to deal with the current disturbance. Detailed simulations demonstrate that the fast response of fuel flow for the current demand disturbance and zero steady error of the output voltage are both achieved. Meanwhile,fuel utilization is kept almost within the safe region.展开更多
含水层介质的非均质性使得有机污染场地普遍呈现污染源区复杂、污染物反向扩散及浓度反弹等问题。传统依赖稀疏钻孔取样的低分辨率调查难以精确刻画污染物迁移分布,已成为制约场地精准治理的关键瓶颈。场地精细刻画,也称为高分辨率场地...含水层介质的非均质性使得有机污染场地普遍呈现污染源区复杂、污染物反向扩散及浓度反弹等问题。传统依赖稀疏钻孔取样的低分辨率调查难以精确刻画污染物迁移分布,已成为制约场地精准治理的关键瓶颈。场地精细刻画,也称为高分辨率场地刻画(high-resolution site characterization,HRSC)已逐渐成为有机污染场地调查与修复实践的核心技术。本次系统梳理了HRSC的发展历程与研究进展。从关键非均质尺度与采样体积等角度界定了HRSC的基本原理,重点阐述了实时监测技术—动态采样策略—数据驱动决策的3大核心步骤,总结了HRSC在污染物相态精准识别、源区划定和优势通道辨识中的应用进展。现有研究表明,基于直推探测、原位传感与地球物理成像等实时技术,可在厘米到米级关键尺度上显著提升对残留非水相液体分布、污染羽范围以及低渗区反向扩散等的刻画能力,结合动态采样与多源证据可有效降低概念模型的不确定性并提高靶区修复决策的可靠性。HRSC正由“以数据获取为主”向“多源数据融合与智能化决策支撑”演进,以支撑风险管控导向的高效调查与精准修复决策。展开更多
新型电力系统建设背景下,新能源渗透率不断提高造成电力系统灵活性需求剧增。针对电力系统灵活性供需失衡问题,提出含碳捕集电厂的源荷储资源数据驱动鲁棒优化调度模型。首先,基于碳捕集电厂、抽水蓄能等源荷储灵活性资源运行特性,刻画...新型电力系统建设背景下,新能源渗透率不断提高造成电力系统灵活性需求剧增。针对电力系统灵活性供需失衡问题,提出含碳捕集电厂的源荷储资源数据驱动鲁棒优化调度模型。首先,基于碳捕集电厂、抽水蓄能等源荷储灵活性资源运行特性,刻画电力系统灵活性供给能力;其次,采用数据驱动方法构建椭球不确定性集以刻画风电、光伏波动区间,根据各时刻边界值量化灵活性需求;进而,结合灵活性供需关系,提出源荷储资源数据驱动鲁棒优化模型,并采用改进列与约束生成算法(column and constraint generation,C&CG)进行求解。算例仿真表明,通过协同调度源荷储灵活性资源有助于支撑电力系统功率平衡,提高灵活性裕度,数据驱动鲁棒优化方法能够剔除传统盒式不确定集中的不实际恶劣场景,进而改善传统鲁棒优化模型过于保守的问题,并显著提升计算效率。展开更多
基金supported in part by the National Natural Science Foundation of China,Grant/Award Number:62003267the Key Research and Development Program of Shaanxi Province,Grant/Award Number:2023-GHZD-33Open Project of the State Key Laboratory of Intelligent Game,Grant/Award Number:ZBKF-23-05。
文摘To address the issue of instability or even imbalance in the orientation and attitude control of quadrotor unmanned aerial vehicles(QUAVs)under random disturbances,this paper proposes a distributed antidisturbance data-driven event-triggered fusion control method,which achieves efficient fault diagnosis while suppressing random disturbances and mitigating communication conflicts within the QUAV swarm.First,the impact of random disturbances on the UAV swarm is analyzed,and a model for orientation and attitude control of QUAVs under stochastic perturbations is established,with the disturbance gain threshold determined.Second,a fault diagnosis system based on a high-gain observer is designed,constructing a fault gain criterion by integrating orientation and attitude information from QUAVs.Subsequently,a model-free dynamic linearization-based data modeling(MFDLDM)framework is developed using model-free adaptive control,which efficiently fits the nonlinear control model of the QUAV swarm while reducing temporal constraints on control data.On this basis,this paper constructs a distributed data-driven event-triggered controller based on the staggered communication mechanism,which consists of an equivalent QUAV controller and an event-triggered controller,and is able to reduce the communication conflicts while suppressing the influence of random interference.Finally,by incorporating random disturbances into the controller,comparative experiments and physical validations are conducted on the QUAV platforms,fully demonstrating the strong adaptability and robustness of the proposed distributed event-triggered fault-tolerant control system.
基金supported by the National Natural Science Foundation of China(61836001,62122014,62173036,62102022)。
文摘The rapid increase of the scale and the complexity of the controlled plants bring new challenges such as computing power and storage for conventional control systems.Cloud computing is concerned as a powerful solution to handle complex large-scale control missions by using sufficient computing resources.However,the computing ability enables more complex devices and more data to be involved and most of the data have not been fully utilized.Meanwhile,it is even impossible to obtain an accurate model of each device in the complex control systems for the model-based control algorithms.Therefore,motivated by the above reasons,we propose a data-driven predictive cloud control system.To achieve the proposed system,a practical data-driven predictive cloud control testbed is established and together a cloud-edge communication scheme is developed.Finally,the simulations and experiments demonstrate the effectiveness of the proposed system.
基金supported by the National Natural Science Foundation of China(Grant Nos.12272257,12102292,12032006)the special fund for Science and Technology Innovation Teams of Shanxi Province(Nos.202204051002006).
文摘This study employs a data-driven methodology that embeds the principle of dimensional invariance into an artificial neural network to automatically identify dominant dimensionless quantities in the penetration of rod projectiles into semi-infinite metal targets from experimental measurements.The derived mathematical expressions of dimensionless quantities are simplified by the examination of the exponent matrix and coupling relationships between feature variables.As a physics-based dimension reduction methodology,this way reduces high-dimensional parameter spaces to descriptions involving only a few physically interpretable dimensionless quantities in penetrating cases.Then the relative importance of various dimensionless feature variables on the penetration efficiencies for four impacting conditions is evaluated through feature selection engineering.The results indicate that the selected critical dimensionless feature variables by this synergistic method,without referring to the complex theoretical equations and aiding in the detailed knowledge of penetration mechanics,are in accordance with those reported in the reference.Lastly,the determined dimensionless quantities can be efficiently applied to conduct semi-empirical analysis for the specific penetrating case,and the reliability of regression functions is validated.
基金Natural Science Basic Research Plan in Shaanxi Province of China(2023-JC-QN-0733).
文摘This paper proposes the nonlinear direct data-driven control from theoretical analysis and practical engineering,i.e.,unmanned aerial vehicle(UAV)formation flight system.Firstly,from the theoretical point of view,consider one nonlinear closedloop system with a nonlinear plant and nonlinear feed-forward controller simultaneously.To avoid the complex identification process for that nonlinear plant,a nonlinear direct data-driven control strategy is proposed to design that nonlinear feed-forward controller only through the input-output measured data sequence directly,whose detailed explicit forms are model inverse method and approximated analysis method.Secondly,from the practical point of view,after reviewing the UAV formation flight system,nonlinear direct data-driven control is applied in designing the formation controller,so that the followers can track the leader’s desired trajectory during one small time instant only through solving one data fitting problem.Since most natural phenomena have nonlinear properties,the direct method must be the better one.Corresponding system identification and control algorithms are required to be proposed for those nonlinear systems,and the direct nonlinear controller design is the purpose of this paper.
基金supported by the National Natural Science Foundation of China (61202078 61071139)the National High Technology Research and Development Program of China (863 Program)(SQ2011AA110101)
文摘The data-driven fault diagnosis methods can improve the reliability of analog circuits by using the data generated from it. The data have some characteristics, such as randomness and incompleteness, which lead to the diagnostic results being sensitive to the specific values and random noise. This paper presents a data-driven fault diagnosis method for analog circuits based on the robust competitive agglomeration （RCA）, which can alleviate the incompleteness of the data by clustering with the competing process. And the robustness of the diagnostic results is enhanced by using the approach of robust statistics in RCA. A series of experiments are provided to demonstrate that RCA can classify the incomplete data with a high accuracy. The experimental results show that RCA is robust for the data needed to be classified as well as the parameters needed to be adjusted. The effectiveness of RCA in practical use is demonstrated by two analog circuits.
基金Projects(51076027,51036002) supported by the National Natural Science Foundation of ChinaProject(20090092110051) supported by the Doctoral Fund of Ministry of Education of China
文摘Solid oxide fuel cells (SOFCs) are considered to be one of the most important clean,distributed resources. However,SOFCs present a challenging control problem owing to their slow dynamics,nonlinearity and tight operating constraints. A novel data-driven nonlinear control strategy was proposed to solve the SOFC control problem by combining a virtual reference feedback tuning (VRFT) method and support vector machine. In order to fulfill the requirement for fuel utilization and control constraints,a dynamic constraints unit and an anti-windup scheme were adopted. In addition,a feedforward loop was designed to deal with the current disturbance. Detailed simulations demonstrate that the fast response of fuel flow for the current demand disturbance and zero steady error of the output voltage are both achieved. Meanwhile,fuel utilization is kept almost within the safe region.
基金Supported by State Key Program of National Natural Science Foundation of China （60834001） and National Natural Science Foundation of China （60774022）.Acknowledgement Authors would like to thank NSFC organizers and participants who shared their ideas and works with us during the NSFC workshop on data-based control, decision making, scheduling, and fault diagnosis. In particular, authors would like to thank Chai Tian-You, Sun You-Xian, Wang Hong, Yan Hong-Sheng, and Gao Fu-Rong for discussing the concept on design model shown in Fig. 12, the concept on temporal multi-scale shown in Fig. 8, the concept on fault diagnosis shown in Fig. 14, the concept on dynamic scheduling shown in Fig. 15, and the concept on interval model shown in Fig. 16, respectively.
基金Supported by National Basic Research Program of China（973 Program）（2013CB035500） National Natural Science Foundation of China（61233004,61221003,61074061）+1 种基金 International Cooperation Program of Shanghai Science and Technology Commission （12230709600） the Higher Education Research Fund for the Doctoral Program of China（20120073130006）
文摘含水层介质的非均质性使得有机污染场地普遍呈现污染源区复杂、污染物反向扩散及浓度反弹等问题。传统依赖稀疏钻孔取样的低分辨率调查难以精确刻画污染物迁移分布,已成为制约场地精准治理的关键瓶颈。场地精细刻画,也称为高分辨率场地刻画(high-resolution site characterization,HRSC)已逐渐成为有机污染场地调查与修复实践的核心技术。本次系统梳理了HRSC的发展历程与研究进展。从关键非均质尺度与采样体积等角度界定了HRSC的基本原理,重点阐述了实时监测技术—动态采样策略—数据驱动决策的3大核心步骤,总结了HRSC在污染物相态精准识别、源区划定和优势通道辨识中的应用进展。现有研究表明,基于直推探测、原位传感与地球物理成像等实时技术,可在厘米到米级关键尺度上显著提升对残留非水相液体分布、污染羽范围以及低渗区反向扩散等的刻画能力,结合动态采样与多源证据可有效降低概念模型的不确定性并提高靶区修复决策的可靠性。HRSC正由“以数据获取为主”向“多源数据融合与智能化决策支撑”演进,以支撑风险管控导向的高效调查与精准修复决策。
文摘新型电力系统建设背景下,新能源渗透率不断提高造成电力系统灵活性需求剧增。针对电力系统灵活性供需失衡问题,提出含碳捕集电厂的源荷储资源数据驱动鲁棒优化调度模型。首先,基于碳捕集电厂、抽水蓄能等源荷储灵活性资源运行特性,刻画电力系统灵活性供给能力;其次,采用数据驱动方法构建椭球不确定性集以刻画风电、光伏波动区间,根据各时刻边界值量化灵活性需求;进而,结合灵活性供需关系,提出源荷储资源数据驱动鲁棒优化模型,并采用改进列与约束生成算法(column and constraint generation,C&CG)进行求解。算例仿真表明,通过协同调度源荷储灵活性资源有助于支撑电力系统功率平衡,提高灵活性裕度,数据驱动鲁棒优化方法能够剔除传统盒式不确定集中的不实际恶劣场景,进而改善传统鲁棒优化模型过于保守的问题,并显著提升计算效率。
