A flexible two degrees of freedom (2-DOF) steering model of multi-axlevehicle (MAV) is presented with considering the effect of frame flexibility based on the classic2-DOF model. A method to calculate the frame flexib...A flexible two degrees of freedom (2-DOF) steering model of multi-axlevehicle (MAV) is presented with considering the effect of frame flexibility based on the classic2-DOF model. A method to calculate the frame flexibility is derived by using three moments equation.The steering stability of MAV is analyzed. The steering performance of MAV is also researched infrequency domain. Simulation results show that the dynamic effects of flexible model are more severethan rigid model and the flexible effect of frame will weaken the steering stability of MAV.Different disposals of steering axles lead to different steering characteristics of MAV. Thein-phase steering mode improves the steering characteristics and stability at high speed. Theanti-phase steering mode increases the steering mobility at low vehicle speed.展开更多
A new method to calculate the motor temperature rising in electric vehicle （EV） is proposed based on the stator resistance identification. The measure theory of the motor temperature rising with the stator resistanc...A new method to calculate the motor temperature rising in electric vehicle （EV） is proposed based on the stator resistance identification. The measure theory of the motor temperature rising with the stator resistance is discussed at first. An enhanced magnetism motor dynamic math model is built which is the research object. Then the resistance identification system model is built on the mutual model reference adaptive,system （MRAS） theory. The simulation diagram of the mutual MRAS model is constructed and the resistance identification performance is studied in different motor states. Simulation results indicate that the stator resistance identification model with the mutual MRAS is effective. At the same time, the identification of motor temperature rising is possible with the identification of the stator resistance.展开更多
The utilization of traffic information received from intelligent vehicle highway systems(IVHS) to plan velocity and split output power for multi-source vehicles is currently a research hotspot. However, it is an open ...The utilization of traffic information received from intelligent vehicle highway systems(IVHS) to plan velocity and split output power for multi-source vehicles is currently a research hotspot. However, it is an open issue to plan vehicle velocity and distribute output power between different supply units simultaneously due to the strongly coupling characteristic of the velocity planning and the power distribution. To address this issue, a flexible predictive power-split control strategy based on IVHS is proposed for electric vehicles(EVs) equipped with battery-supercapacitor system(BSS). Unlike hierarchical strategies to plan vehicle velocity and distribute output power separately, a monolayer model predictive control(MPC) method is employed to optimize them online at the same time. Firstly, a flexible velocity planning strategy is designed based on the signal phase and time(SPAT) information received from IVHS and then the Pontryagin’s minimum principle(PMP) is adopted to formulate the optimal control problem of the BSS. Then, the flexible velocity planning strategy and the optimal control problem of BSS are embedded into an MPC framework, which is online solved using the shooting method in a fashion of receding horizon. Simulation results verify that the proposed strategy achieves a superior performance compared with the hierarchical strategy in terms of transportation efficiency, battery capacity loss, energy consumption and computation time.展开更多
This paper is concerned with the robust control synthesis of autonomous underwater vehicle （AUV） for general path following maneuvers. First, we present maneuvering kinematics and vehicle dynamics in a unified frame...This paper is concerned with the robust control synthesis of autonomous underwater vehicle （AUV） for general path following maneuvers. First, we present maneuvering kinematics and vehicle dynamics in a unified framework. Based on H∞ loop-shaping procedure, the 2-DOF autopilot controller has been presented to enhance stability and path tracking. By use of model reduction, the high-order control system is reduced to one with reasonable order, and further the scaled low-order controller has been analyzed in both the frequency and the time domains. Finally, it is shown that the autopilot control system provides robust performance and stability against prescribed levels of uncertainty.展开更多
Bridge frequency(BF)identification using the vehicle scanning method has attracted considerable attention during the last two decades.However,most previous studies have adopted unrealistic vehicle models,thus finding ...Bridge frequency(BF)identification using the vehicle scanning method has attracted considerable attention during the last two decades.However,most previous studies have adopted unrealistic vehicle models,thus finding limited practical applications.This study proposes a smartphone-based BF identification method that uses the contact-point acceleration response of a four degree-of-freedom vehicle model.The said response can be inferred from the vehicle body response measured by a smartphone.For realizing practical applications,this method is incorporated into a self-developed smartphone app to obtain data smoothly and identify BFs in a timely manner.Numerical and experimental investigations are performed to verify the effectiveness of the proposed method.In particular,the robustness of this method is investigated numerically against various factors,including the vehicle speed,bridge span,road roughness,and bridge type.Furthermore,laboratory calibration tests are performed to investigate the accuracy of the smartphone gyroscope in measuring the angular velocity,where anomalous data are detected and eliminated.Laboratory experiment results for a simply supported bridge indicate that the proposed method can be used to identify the first two BFs with acceptable accuracy.展开更多
The estimation of fractional vegetation cover(FVC) is important for identifying and monitoring desertification, especially in arid and semiarid regions. By using regression and pixel dichotomy models, we present the c...The estimation of fractional vegetation cover(FVC) is important for identifying and monitoring desertification, especially in arid and semiarid regions. By using regression and pixel dichotomy models, we present the comparison of Sentinel-2A(S2) multispectral instrument(MSI) and Landsat 8(L8) operational land imager(OLI) data regarding the retrieval of FVC in a semi-arid sandy area(Mu Us Sandland, China, in August 2016). A combination of unmanned aerial vehicle(UAV) high-spatial-resolution images and field plots were used to produce verified data. Based on a normalized difference vegetation index(NDVI) regression model, the results showed that, compared with that of L8, the coefficient of determination(R2) of S2 increased by 26.0%, and the root mean square error(RMSE) and the sum of absolute error(SAE) decreased by 3.0% and 11.4%, respectively. For the ratio vegetation index(RVI) regression model, compared with that of L8, the R2 of S2 increased by 26.0%, and the RMSE and SAE decreased by 8.0% and 20.0%, respectively. When the pixel dichotomy model was used, compared with that of L8, the RMSE of S2 decreased by 21.3%, and the SAE decreased by 26.9%. Overall, S2 performed better than L8 in terms of FVC inversion. Additionally, in this paper, we develop a verified scheme based on UAV data in combination with the object-based classification method. This scheme is feasible and sufficiently robust for building relationships between field data and inversion results from satellite data. Further, the synergy of multi-source sensors(especially UAVs and satellites) is a potential effective way to estimate and evaluate regional ecological environmental parameters(FVC).展开更多
文摘A flexible two degrees of freedom (2-DOF) steering model of multi-axlevehicle (MAV) is presented with considering the effect of frame flexibility based on the classic2-DOF model. A method to calculate the frame flexibility is derived by using three moments equation.The steering stability of MAV is analyzed. The steering performance of MAV is also researched infrequency domain. Simulation results show that the dynamic effects of flexible model are more severethan rigid model and the flexible effect of frame will weaken the steering stability of MAV.Different disposals of steering axles lead to different steering characteristics of MAV. Thein-phase steering mode improves the steering characteristics and stability at high speed. Theanti-phase steering mode increases the steering mobility at low vehicle speed.
基金Sponsored by the National"863"Program Project(2005AA501650)
文摘A new method to calculate the motor temperature rising in electric vehicle （EV） is proposed based on the stator resistance identification. The measure theory of the motor temperature rising with the stator resistance is discussed at first. An enhanced magnetism motor dynamic math model is built which is the research object. Then the resistance identification system model is built on the mutual model reference adaptive,system （MRAS） theory. The simulation diagram of the mutual MRAS model is constructed and the resistance identification performance is studied in different motor states. Simulation results indicate that the stator resistance identification model with the mutual MRAS is effective. At the same time, the identification of motor temperature rising is possible with the identification of the stator resistance.
基金supported by the National Natural Science Foundation of China (62173303)the Fundamental Research for the Zhejiang P rovincial Universities (RF-C2020003)。
文摘The utilization of traffic information received from intelligent vehicle highway systems(IVHS) to plan velocity and split output power for multi-source vehicles is currently a research hotspot. However, it is an open issue to plan vehicle velocity and distribute output power between different supply units simultaneously due to the strongly coupling characteristic of the velocity planning and the power distribution. To address this issue, a flexible predictive power-split control strategy based on IVHS is proposed for electric vehicles(EVs) equipped with battery-supercapacitor system(BSS). Unlike hierarchical strategies to plan vehicle velocity and distribute output power separately, a monolayer model predictive control(MPC) method is employed to optimize them online at the same time. Firstly, a flexible velocity planning strategy is designed based on the signal phase and time(SPAT) information received from IVHS and then the Pontryagin’s minimum principle(PMP) is adopted to formulate the optimal control problem of the BSS. Then, the flexible velocity planning strategy and the optimal control problem of BSS are embedded into an MPC framework, which is online solved using the shooting method in a fashion of receding horizon. Simulation results verify that the proposed strategy achieves a superior performance compared with the hierarchical strategy in terms of transportation efficiency, battery capacity loss, energy consumption and computation time.
基金a part of the project titled "Development of Key Marine Equipments for Enhancement of Ocean Industry-Development of Underwater Manipulator and Thrusting System Driven by Electric Motor" funded by the Ministry of Land, Transport and Maritime Affairs, Korea
文摘This paper is concerned with the robust control synthesis of autonomous underwater vehicle （AUV） for general path following maneuvers. First, we present maneuvering kinematics and vehicle dynamics in a unified framework. Based on H∞ loop-shaping procedure, the 2-DOF autopilot controller has been presented to enhance stability and path tracking. By use of model reduction, the high-order control system is reduced to one with reasonable order, and further the scaled low-order controller has been analyzed in both the frequency and the time domains. Finally, it is shown that the autopilot control system provides robust performance and stability against prescribed levels of uncertainty.
基金National Natural Science Foundation of China under Grant Nos.51978215 and 52378295National Key R&D Program of China under Grant No.2019YFC1511100+1 种基金Guangdong Basic and Applied Basic Research Foundation under Grant No.2022A1515110587Shenzhen S&T Project under Grant Nos.JCYJ20200109112816582 and KQTD20210811090112003。
文摘Bridge frequency(BF)identification using the vehicle scanning method has attracted considerable attention during the last two decades.However,most previous studies have adopted unrealistic vehicle models,thus finding limited practical applications.This study proposes a smartphone-based BF identification method that uses the contact-point acceleration response of a four degree-of-freedom vehicle model.The said response can be inferred from the vehicle body response measured by a smartphone.For realizing practical applications,this method is incorporated into a self-developed smartphone app to obtain data smoothly and identify BFs in a timely manner.Numerical and experimental investigations are performed to verify the effectiveness of the proposed method.In particular,the robustness of this method is investigated numerically against various factors,including the vehicle speed,bridge span,road roughness,and bridge type.Furthermore,laboratory calibration tests are performed to investigate the accuracy of the smartphone gyroscope in measuring the angular velocity,where anomalous data are detected and eliminated.Laboratory experiment results for a simply supported bridge indicate that the proposed method can be used to identify the first two BFs with acceptable accuracy.
基金National Natural Science Foundation of China(No.41301451,41541008)Fundamental Research Funds for the Central Universities(No.2452018144)
文摘The estimation of fractional vegetation cover(FVC) is important for identifying and monitoring desertification, especially in arid and semiarid regions. By using regression and pixel dichotomy models, we present the comparison of Sentinel-2A(S2) multispectral instrument(MSI) and Landsat 8(L8) operational land imager(OLI) data regarding the retrieval of FVC in a semi-arid sandy area(Mu Us Sandland, China, in August 2016). A combination of unmanned aerial vehicle(UAV) high-spatial-resolution images and field plots were used to produce verified data. Based on a normalized difference vegetation index(NDVI) regression model, the results showed that, compared with that of L8, the coefficient of determination(R2) of S2 increased by 26.0%, and the root mean square error(RMSE) and the sum of absolute error(SAE) decreased by 3.0% and 11.4%, respectively. For the ratio vegetation index(RVI) regression model, compared with that of L8, the R2 of S2 increased by 26.0%, and the RMSE and SAE decreased by 8.0% and 20.0%, respectively. When the pixel dichotomy model was used, compared with that of L8, the RMSE of S2 decreased by 21.3%, and the SAE decreased by 26.9%. Overall, S2 performed better than L8 in terms of FVC inversion. Additionally, in this paper, we develop a verified scheme based on UAV data in combination with the object-based classification method. This scheme is feasible and sufficiently robust for building relationships between field data and inversion results from satellite data. Further, the synergy of multi-source sensors(especially UAVs and satellites) is a potential effective way to estimate and evaluate regional ecological environmental parameters(FVC).
