Joint parameter identification is a key problem in the modeling of complex structures. The behavior of joint may be random due to the random properties of preload and joint geometries, contact surface and its finish, ...Joint parameter identification is a key problem in the modeling of complex structures. The behavior of joint may be random due to the random properties of preload and joint geometries, contact surface and its finish, etc. A method is presented to simulate the joint parameters as probabilistic variables. In this method the response surface based model updating method and probabilistic approaches are employed to identify the parameters. The study implies that joint parameters of some structures have normal or nearly normal distributions, and a linear FE model with probabilistic variables could illustrate dynamic characteristics of joints.展开更多
This paper discusses closed-loop identification of unstable systems.In particular,wefirst apply the joint input–output identification method and then convert the identification problem of unstable systems into that of st...This paper discusses closed-loop identification of unstable systems.In particular,wefirst apply the joint input–output identification method and then convert the identification problem of unstable systems into that of stable systems,which can be tackled by using kernel-based regularization methods.We propose to identify two transfer functions by kernel regularization,the one from the reference signal to the input,and the one from the reference signal to the output.Since these transfer functions are stable,kernel regularization methods can construct their accurate models.Then the model of unstable system is constructed by ratio of these functions.The effectiveness of the proposed method is demonstrated by a numerical example and a practical experiment with a DC motor.展开更多
文摘Joint parameter identification is a key problem in the modeling of complex structures. The behavior of joint may be random due to the random properties of preload and joint geometries, contact surface and its finish, etc. A method is presented to simulate the joint parameters as probabilistic variables. In this method the response surface based model updating method and probabilistic approaches are employed to identify the parameters. The study implies that joint parameters of some structures have normal or nearly normal distributions, and a linear FE model with probabilistic variables could illustrate dynamic characteristics of joints.
文摘This paper discusses closed-loop identification of unstable systems.In particular,wefirst apply the joint input–output identification method and then convert the identification problem of unstable systems into that of stable systems,which can be tackled by using kernel-based regularization methods.We propose to identify two transfer functions by kernel regularization,the one from the reference signal to the input,and the one from the reference signal to the output.Since these transfer functions are stable,kernel regularization methods can construct their accurate models.Then the model of unstable system is constructed by ratio of these functions.The effectiveness of the proposed method is demonstrated by a numerical example and a practical experiment with a DC motor.
