The determination of the dynamic load is one of the indispensable technologies for structure design and health monitoring for aerospace vehicles.However,it is a significant challenge to measure the external excitation...The determination of the dynamic load is one of the indispensable technologies for structure design and health monitoring for aerospace vehicles.However,it is a significant challenge to measure the external excitation directly.By contrast,the technique of dynamic load identification based on the dynamic model and the response information is a feasible access to obtain the dynamic load indirectly.Furthermore,there are multi-source uncertainties which cannot be neglected for complex systems in the load identification process,especially for aerospace vehicles.In this paper,recent developments in the dynamic load identification field for aerospace vehicles considering multi-source uncertainties are reviewed,including the deterministic dynamic load identification and uncertain dynamic load identification.The inversion methods with different principles of concentrated and distributed loads,and the quantification and propagation analysis for multi-source uncertainties are discussed.Eventually,several possibilities remaining to be explored are illustrated in brief.展开更多
Based on the platform of Matlab and the theory of digital signal processing, we propose a method in the cepstrum domain for dynamic load spectra identification of machinery. We demonstrate that the dynamic load spectr...Based on the platform of Matlab and the theory of digital signal processing, we propose a method in the cepstrum domain for dynamic load spectra identification of machinery. We demonstrate that the dynamic load spectra can be identified from the response signal of the system, based on cepstra. An ARMA model is built based on the harmonic retrieval by high-order spectra. The coefficients of a Green function are determined and the window width can be estimated. Finally the effectiveness of the method is validated by simulation results.展开更多
The dynamic parameter identification of the robot is the basis for the design of the controller based on the dynamic model.Currently,the primary method for solving angular velocity and angular acceleration is to filte...The dynamic parameter identification of the robot is the basis for the design of the controller based on the dynamic model.Currently,the primary method for solving angular velocity and angular acceleration is to filter and smooth the position sequence and then form a differential signal.However,if the noise and the original signal overlap in the frequency domain,filtering the noise will also filter out the valuable information in the frequency band.This paper proposes an excitation trajectory based on Logistic function,which fully uses the information in the original signal and can accurately solve the angular velocity and angular acceleration without filtering and smoothing the position sequence.The joint angle of the excitation trajectory is mapped to the joint angular velocity and angular acceleration one by one so that the joint angular velocity and joint angular acceleration can be obtained directly according to the position.The genetic algorithm is used to optimize the excitation trajectory parameters to minimize the observation matrix’s condition number and further improve the identification accuracy.By using the strategy of iterative identification,the dynamic parameters identified in each iteration are substituted into the robot controller according to the previous position sequence until the tracking trajectory approaches the desired trajectory,and the actual joint angular velocity and angular acceleration converge to the expected value.The simulation results show that using the step-by-step strategy,the joint angular velocity and joint angular acceleration of the tracking trajectory quickly converge to the expected value,and the identification error of inertia parameters is less than 0.01 in three iterations.With the increase of the number of iterations,the identification error of inertial parameters can be further reduced.展开更多
基金supported by the National Nature Science Foundation of China(No.12072007)the Ningbo Nature Science Foundation(No.202003N4018)+1 种基金the Aeronautical Science Foundation of China (No. 20182951014)the Defense Industrial Technology Development Program(No.JCKY2019209C004)
文摘The determination of the dynamic load is one of the indispensable technologies for structure design and health monitoring for aerospace vehicles.However,it is a significant challenge to measure the external excitation directly.By contrast,the technique of dynamic load identification based on the dynamic model and the response information is a feasible access to obtain the dynamic load indirectly.Furthermore,there are multi-source uncertainties which cannot be neglected for complex systems in the load identification process,especially for aerospace vehicles.In this paper,recent developments in the dynamic load identification field for aerospace vehicles considering multi-source uncertainties are reviewed,including the deterministic dynamic load identification and uncertain dynamic load identification.The inversion methods with different principles of concentrated and distributed loads,and the quantification and propagation analysis for multi-source uncertainties are discussed.Eventually,several possibilities remaining to be explored are illustrated in brief.
基金Project 59775004 supported by National Natural Science Foundation of China
文摘Based on the platform of Matlab and the theory of digital signal processing, we propose a method in the cepstrum domain for dynamic load spectra identification of machinery. We demonstrate that the dynamic load spectra can be identified from the response signal of the system, based on cepstra. An ARMA model is built based on the harmonic retrieval by high-order spectra. The coefficients of a Green function are determined and the window width can be estimated. Finally the effectiveness of the method is validated by simulation results.
基金supported by Aeronautical Science Foundation of China(No.201916052001)China National Key R&D Program(No.2018YFB1309203)Foundation of the Graduate Innovation Center,Nanjing University of Aeronautics and Astronautics(No.xcxjh20210501)。
文摘The dynamic parameter identification of the robot is the basis for the design of the controller based on the dynamic model.Currently,the primary method for solving angular velocity and angular acceleration is to filter and smooth the position sequence and then form a differential signal.However,if the noise and the original signal overlap in the frequency domain,filtering the noise will also filter out the valuable information in the frequency band.This paper proposes an excitation trajectory based on Logistic function,which fully uses the information in the original signal and can accurately solve the angular velocity and angular acceleration without filtering and smoothing the position sequence.The joint angle of the excitation trajectory is mapped to the joint angular velocity and angular acceleration one by one so that the joint angular velocity and joint angular acceleration can be obtained directly according to the position.The genetic algorithm is used to optimize the excitation trajectory parameters to minimize the observation matrix’s condition number and further improve the identification accuracy.By using the strategy of iterative identification,the dynamic parameters identified in each iteration are substituted into the robot controller according to the previous position sequence until the tracking trajectory approaches the desired trajectory,and the actual joint angular velocity and angular acceleration converge to the expected value.The simulation results show that using the step-by-step strategy,the joint angular velocity and joint angular acceleration of the tracking trajectory quickly converge to the expected value,and the identification error of inertia parameters is less than 0.01 in three iterations.With the increase of the number of iterations,the identification error of inertial parameters can be further reduced.
