摘要 :
Structural decoupling problem, i.e. predicting dynamic behavior of a particular substructure from the knowledge of the dynamics of the coupled structure and the other substructure, has been well investigated for three decades and ...
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Structural decoupling problem, i.e. predicting dynamic behavior of a particular substructure from the knowledge of the dynamics of the coupled structure and the other substructure, has been well investigated for three decades and led to several decoupling methods. In spite of the inherent nonlinearities in a structural system in various forms such as clearances, friction and nonlinear stiffness, all decoupling studies are for linear systems. In this study, decoupling problem for nonlinear systems is addressed for the first time. A method, named as FRF Decoupling Method for Nonlinear Systems (FDM-NS), is proposed for calculating FRFs of a substructure decoupled from a coupled nonlinear structure where nonlinearity can be modeled as a single nonlinear element. Depending on where nonlinear element is, i.e., either in the known or unknown subsystem, or at the connection point, the formulation differs. The method requires relative displacement information between two end points of the nonlinear element, in addition to point and transfer FRFs at some points of the known subsystem. However, it is not necessary to excite the system from the unknown subsystem even when the nonlinear element is in that subsystem. The validation of FDM-NS is demonstrated with two different case studies using nonlinear lumped parameter systems. Finally, a nonlinear experimental test structure is used in order to show the real-life application and accuracy of FDM-NS.
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摘要 :
In this paper, a new method for the identification of the Wiener nonlinear system is proposed. The system, being a cascade connection of a linear dynamic subsystem and a nonlinear memoryless element, is identified by a two-step se...
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In this paper, a new method for the identification of the Wiener nonlinear system is proposed. The system, being a cascade connection of a linear dynamic subsystem and a nonlinear memoryless element, is identified by a two-step semiparametric approach. The impulse response function of the linear part is identified via the nonlinear least-squares approach with the system nonlinearity estimated by a pilot nonparametric kernel regression estimate. The obtained estimate of the linear part is then used to form a nonparametric kernel estimate of the nonlinear element of the Wiener system. The proposed method permits recovery of a wide class of nonlinearities which need not be invertible. As a result, the proposed algorithm is computationally very efficient since it does not require a numerical procedure to calculate the inverse of the estimate. Furthermore, our approach allows non-Gaussian input signals and the presence of additive measurement noise. However, only linear systems with a finite memory are admissible. The conditions for the convergence of the proposed estimates are given. Computer simulations are included to verify the basic theory
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摘要 :
A continuous-time Hammerstein system, i.e., a system consisting of a nonlinear memoryless subsystem followed by a linear dynamic one, is identified. The system is driven and disturbed by white random signals. The a priori informat...
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A continuous-time Hammerstein system, i.e., a system consisting of a nonlinear memoryless subsystem followed by a linear dynamic one, is identified. The system is driven and disturbed by white random signals. The a priori information about both subsystems is nonparametric, which means that functional forms of both the nonlinear characteristic and the impulse response of the dynamic subsystem are unknown. An algorithm to estimate the nonlinearity is presented and its pointwise convergence to the true characteristic is shown. The impulse response of the dynamic part is recovered with a correlation method. The algorithms are computationally independent. Results of a simulation example are given
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摘要 :
Identification is considered for the Hammerstein system consisting of a static nonlinear block f(middot) followed by an ARX subsystem, when the system output is observed with noise. No assumption is made on the structure of f(midd...
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Identification is considered for the Hammerstein system consisting of a static nonlinear block f(middot) followed by an ARX subsystem, when the system output is observed with noise. No assumption is made on the structure of f(middot). Recursive estimates are given for coefficients of the ARX subsystem and for the value of f(u) at any u. All estimates are proved to converge to the true values with probability one. Numerical examples are provided justifying the theoretical analysis
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摘要 :
Identification is considered for the Hammerstein system consisting of a static nonlinear block f(middot) followed by an ARX subsystem, when the system output is observed with noise. No assumption is made on the structure of f(midd...
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Identification is considered for the Hammerstein system consisting of a static nonlinear block f(middot) followed by an ARX subsystem, when the system output is observed with noise. No assumption is made on the structure of f(middot). Recursive estimates are given for coefficients of the ARX subsystem and for the value of f(u) at any u. All estimates are proved to converge to the true values with probability one. Numerical examples are provided justifying the theoretical analysis
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摘要 :
A continuous-time Wiener system is identified. The system consists of a linear dynamic subsystem and a memoryless nonlinear one connected in a cascade. The input signal is a stationary white Gaussian random process. The system is ...
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A continuous-time Wiener system is identified. The system consists of a linear dynamic subsystem and a memoryless nonlinear one connected in a cascade. The input signal is a stationary white Gaussian random process. The system is disturbed by stationary white random Gaussian noise. Both subsystems are identified from input-output observations taken at the input and output of the whole system. The a priori information is very small and, therefore, resulting identification problems are nonparametric. The impulse impulse of the linear part is recovered by a correlation method, while the nonlinear characteristic is estimated with the help of the nonparametric kernel regression method. The authors prove convergence of the proposed identification algorithms and examine their convergence rates
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摘要 :
Discusses Hammerstein model identification in the frequency domain using sampled input-output data. By exploring the fundamental frequency and harmonics generated by the unknown nonlinearity, we propose a frequency domain approach...
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Discusses Hammerstein model identification in the frequency domain using sampled input-output data. By exploring the fundamental frequency and harmonics generated by the unknown nonlinearity, we propose a frequency domain approach and show its convergence for both the linear and nonlinear subsystems in the presence of noise. No a priori knowledge of the structure of the nonlinearity is required and the linear part can be nonparametric.
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摘要 :
Derived from the idea of stochastic approximation, recursive algorithms to identify a Hammerstein system are presented. Two of them recover the characteristic of the nonlinear memoryless subsystem, while the third one estimates th...
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Derived from the idea of stochastic approximation, recursive algorithms to identify a Hammerstein system are presented. Two of them recover the characteristic of the nonlinear memoryless subsystem, while the third one estimates the impulse response of the linear dynamic part. The a priori information about both subsystems is nonparametric. Consistency in quadratic mean is shown, and the convergence rate is examined. Results of numerical simulation are also presented.
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In this study, a fixed point iteration-based subspace identification method is proposed for Hammerstein state-space systems. The original system is decomposed into two subsystems with fewer parameters based on the hierarchical ide...
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In this study, a fixed point iteration-based subspace identification method is proposed for Hammerstein state-space systems. The original system is decomposed into two subsystems with fewer parameters based on the hierarchical identification principle. Each subsystem is related directly to either the linear dynamics or the static non-linearity. A two-stage least-squares-based iterative method is then implemented to separately estimate the coefficients of the non-linear subsystem and the extended Markov parameters of the linear subsystem. The linear subsystem parameters are extracted from the identified extended Markov parameters using a singular value decomposition based method. Convergence analysis of the proposed method is established using fixed point theory, which shows that the proposed method gives consistent estimates under arbitrary non-zero initial conditions. Simulation results are included to show the performance of the proposed method.
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摘要 :
A new methodology is proposed to investigate the large-signal stability of interconnected power electronics systems. The approach consists of decoupling the system into a source subsystem and a load subsystem, and stability of the...
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A new methodology is proposed to investigate the large-signal stability of interconnected power electronics systems. The approach consists of decoupling the system into a source subsystem and a load subsystem, and stability of the entire system can be analyzed based on investigating the feedback loop formed by the interconnected source/load system. The proposed methodology requires two stages: 1) since the source and the load are unknown nonlinear subsystems, system identification, which consists of isolating each subsystem into a series combination of a linear part and a nonlinear part, must be performed; and 2) stability analysis of the interconnected system is conducted thereafter based on a developed stability criterion suitable for the nonlinear interconnected source-load model. Applicability of the methodology is verified through the stability analysis of a typical power electronics system
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