For a class of time-delay discrete-time linear systems with external disturbance and measurement noise, the interval estimation problems of state and measurement noise are investigated in this paper. First, the system...For a class of time-delay discrete-time linear systems with external disturbance and measurement noise, the interval estimation problems of state and measurement noise are investigated in this paper. First, the system state together with the time-delay term and measurement noise is augmented as a new state, and a singular system is then constructed. Subsequently, a kind of decoupling technique is employed to eliminate the effect of external disturbance, and an observer is designed to simultaneously estimate the system state and measurement noise. Based on the estimated state and measurement noise, the interval estimations of system state and measurement noise are obtained by reachability analysis technique. Finally, the effectiveness of the proposed method is verified by a four-tank liquid level system.展开更多
This paper studies the issue of observer-based feedback stabilisation for a class of linear sampleddatasystems with model uncertainty and external disturbance. First, for a sampled-data systemwith external disturbance...This paper studies the issue of observer-based feedback stabilisation for a class of linear sampleddatasystems with model uncertainty and external disturbance. First, for a sampled-data systemwith external disturbance, a sampled-data observer is designed to estimate the system state.Subsequently, a robust H∞ controller based on the observer is developed. For a continuous samplinginterval, the gain matrices of both observer and controller change exponentially. Second,using the state coordinate transformations with an exponential rate, a unified dynamics is constructedby augmenting the state estimation error and the closed-loop system state as a newstate. Next, the sufficient conditions ensuring the asymptotical stability of the closed-loop systemare given by the Lyapunov–Krasovskii method and linear matrix inequality (LMI) technique.Finally, the effectiveness of the proposed method is verified by a helicopter model.展开更多
This paper presents development of a control system for ecological driving of a hybrid vehicle. Prediction using traffic signal and road slope information is considered to improve the fuel economy. It is assumed that ...This paper presents development of a control system for ecological driving of a hybrid vehicle. Prediction using traffic signal and road slope information is considered to improve the fuel economy. It is assumed that the automobile receives traffic signal information from intelligent transportation systems （ITS）. Model predictive control is used to calculate optimal vehicle control inputs using traffic signal and road slope information. The performance of the proposed method was analyzed through computer simulation results. Both the fuel economy and the driving profile are optimized using the proposed approach. It was observed that fuel economy was improved compared with driving of a typical human driving model.展开更多
Prevention and control of grape diseases is the key measure to ensure grape yield.In order to improve the precision of grape leaf disease detection,in this study,Squeeze-and-Excitation Networks(SE),Efficient Channel A...Prevention and control of grape diseases is the key measure to ensure grape yield.In order to improve the precision of grape leaf disease detection,in this study,Squeeze-and-Excitation Networks(SE),Efficient Channel Attention(ECA),and Convolutional Block Attention Module(CBAM)attention mechanisms were introduced into Faster Region-based Convolutional Neural Networks(R-CNN),YOLOx,and single shot multibox detector(SSD),to enhance important features and weaken unrelated features and ensure the real-time performance of the model in improving its detection precision.The study showed that Faster R-CNN,YOLOx,and SSD models based on different attention mechanisms effectively enhanced the detection precision and operation speed of the models by slightly enhancing parameters.Optimal models among the three types of models were selected for comparison,and results showed that Faster R-CNN+SE had lower detection precision,YOLOx+ECA required the least parameters with the highest detection precision,and SSD+SE showed optimal real-time performance with relatively high detection precision.This study solved the problem of difficulty in grape leaf disease detection and provided a reference for the analysis of grape diseases and symptoms in automated agricultural production.展开更多
基金supported in part by the National Nature Science Foundation of China(No.61973105)the Natural Science Foundation of Henan Province(No.232300420147)the Fundamental Research Funds for the Universities of Henan Province(No.NSFRF180335).
文摘For a class of time-delay discrete-time linear systems with external disturbance and measurement noise, the interval estimation problems of state and measurement noise are investigated in this paper. First, the system state together with the time-delay term and measurement noise is augmented as a new state, and a singular system is then constructed. Subsequently, a kind of decoupling technique is employed to eliminate the effect of external disturbance, and an observer is designed to simultaneously estimate the system state and measurement noise. Based on the estimated state and measurement noise, the interval estimations of system state and measurement noise are obtained by reachability analysis technique. Finally, the effectiveness of the proposed method is verified by a four-tank liquid level system.
基金the National Nature Science Foundation of China[grant number 61973105]in part by the Fundamental Research Funds for the Universities of Henan Province[grant number NSFRF180335]+3 种基金in part by the Innovative Scientists and Technicians Team of Henan Provincial High Education[grant number 20IRTSTHN019]in part by the Innovative Scientists and Technicians Team of Henan Polytechnic University[grant number T2019-2]in part by the Innovation Scientists and Technicians Troop Construction Projects of Henan Province[grant number CXTD2016054]in part by the Zhongyuan high level talents special support plan[grant number ZYQR201912031].
文摘This paper studies the issue of observer-based feedback stabilisation for a class of linear sampleddatasystems with model uncertainty and external disturbance. First, for a sampled-data systemwith external disturbance, a sampled-data observer is designed to estimate the system state.Subsequently, a robust H∞ controller based on the observer is developed. For a continuous samplinginterval, the gain matrices of both observer and controller change exponentially. Second,using the state coordinate transformations with an exponential rate, a unified dynamics is constructedby augmenting the state estimation error and the closed-loop system state as a newstate. Next, the sufficient conditions ensuring the asymptotical stability of the closed-loop systemare given by the Lyapunov–Krasovskii method and linear matrix inequality (LMI) technique.Finally, the effectiveness of the proposed method is verified by a helicopter model.
基金supported by National Natural Science Foundation of China(Nos.51405137,61403129)the Key Scientific Research Program of the Higher Education Institutions of Henan Province(No.15A470014)+1 种基金the Program for Innovative Research Team of Henan Polytechnic Universitythe Doctoral Program Foundation of Henan Polytechnic University
文摘This paper presents development of a control system for ecological driving of a hybrid vehicle. Prediction using traffic signal and road slope information is considered to improve the fuel economy. It is assumed that the automobile receives traffic signal information from intelligent transportation systems （ITS）. Model predictive control is used to calculate optimal vehicle control inputs using traffic signal and road slope information. The performance of the proposed method was analyzed through computer simulation results. Both the fuel economy and the driving profile are optimized using the proposed approach. It was observed that fuel economy was improved compared with driving of a typical human driving model.
基金financially supported by the National Natural Science Foundation of China(Grant No.31971792)the Industrialization Support Project from the Education Department of Gansu Province(Grant No.2021CYZC-57)+2 种基金Youth Science and Technology Foundation of Gansu Province(Grant No.21JR7RA572)School Level Youth Project of Gansu University of Political Science and Law(Grant No.GZF2019XQNLW08)Gansu Education Department Innovation Fund project(Grant No.2022B-144).
文摘Prevention and control of grape diseases is the key measure to ensure grape yield.In order to improve the precision of grape leaf disease detection,in this study,Squeeze-and-Excitation Networks(SE),Efficient Channel Attention(ECA),and Convolutional Block Attention Module(CBAM)attention mechanisms were introduced into Faster Region-based Convolutional Neural Networks(R-CNN),YOLOx,and single shot multibox detector(SSD),to enhance important features and weaken unrelated features and ensure the real-time performance of the model in improving its detection precision.The study showed that Faster R-CNN,YOLOx,and SSD models based on different attention mechanisms effectively enhanced the detection precision and operation speed of the models by slightly enhancing parameters.Optimal models among the three types of models were selected for comparison,and results showed that Faster R-CNN+SE had lower detection precision,YOLOx+ECA required the least parameters with the highest detection precision,and SSD+SE showed optimal real-time performance with relatively high detection precision.This study solved the problem of difficulty in grape leaf disease detection and provided a reference for the analysis of grape diseases and symptoms in automated agricultural production.
