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
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
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
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
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 (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 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）
