In order to accurately predict the dynamic instabilities of a helicopterrotor/fuselage coupled system, nonlinear differential equations are derived and integrated in thetime domain to yield responses of rotor blade fl...In order to accurately predict the dynamic instabilities of a helicopterrotor/fuselage coupled system, nonlinear differential equations are derived and integrated in thetime domain to yield responses of rotor blade flapping, lead-lag and fuselage motions to simulatethe behavior of the system numerically. To obtain quantitative instabilities, Fast Fourier Transform(FFT) is conducted to estimate the modal frequencies, and Fourier series based moving-blockanalysis is employed in the predictions of the modal damping in terms of the response time history.Study on the helicopter ground resonance exhibits excellent correlation among the time-domain (TD)analytical results, eigenvalues and wind tunnel test data, thus validating the methodology of thepaper. With a large collective pitch set, the predictions of regressive lag modal damping from TDanalysis correlate with the experimental data better than from eigen analysis. TD analysis can beapplied in the dynamic stability analysis of helicopter rotor/fuselage coupled systems incorporatedwith nonlinear blade lag dampers.展开更多
Next-generation vehicle control and future autonomous driving require further advances in vehicle dynamic state estimation. This article provides a concise review, along with the perspectives, of the recent developmen...Next-generation vehicle control and future autonomous driving require further advances in vehicle dynamic state estimation. This article provides a concise review, along with the perspectives, of the recent developments in the estimation of vehicle dynamic states. The definitions used in vehicle dynamic state estimation are first introduced, and alternative estimation structures are presented. Then, the sensor configuration schemes used to estimate vehicle velocity, sideslip angle, yaw rate and roll angle are presented. The vehicle models used for vehicle dynamic state estimation are further summarized, and representative estimation approaches are discussed. Future concerns and perspectives for vehicle dynamic state estimation are also discussed.展开更多
文摘In order to accurately predict the dynamic instabilities of a helicopterrotor/fuselage coupled system, nonlinear differential equations are derived and integrated in thetime domain to yield responses of rotor blade flapping, lead-lag and fuselage motions to simulatethe behavior of the system numerically. To obtain quantitative instabilities, Fast Fourier Transform(FFT) is conducted to estimate the modal frequencies, and Fourier series based moving-blockanalysis is employed in the predictions of the modal damping in terms of the response time history.Study on the helicopter ground resonance exhibits excellent correlation among the time-domain (TD)analytical results, eigenvalues and wind tunnel test data, thus validating the methodology of thepaper. With a large collective pitch set, the predictions of regressive lag modal damping from TDanalysis correlate with the experimental data better than from eigen analysis. TD analysis can beapplied in the dynamic stability analysis of helicopter rotor/fuselage coupled systems incorporatedwith nonlinear blade lag dampers.
基金supported by the National Natural Science Foundation of China(61403158,61520106008)the Project of the Education Department of Jilin Province(2016-429)
文摘Next-generation vehicle control and future autonomous driving require further advances in vehicle dynamic state estimation. This article provides a concise review, along with the perspectives, of the recent developments in the estimation of vehicle dynamic states. The definitions used in vehicle dynamic state estimation are first introduced, and alternative estimation structures are presented. Then, the sensor configuration schemes used to estimate vehicle velocity, sideslip angle, yaw rate and roll angle are presented. The vehicle models used for vehicle dynamic state estimation are further summarized, and representative estimation approaches are discussed. Future concerns and perspectives for vehicle dynamic state estimation are also discussed.
