A Dual-Frequency Approach Based on the Subspace Method for Identifying Nonlinear Vibration Structures

2016 ◽  
Author(s):  
M. W. Zhang ◽  
Y. M. Wei ◽  
S. Wei ◽  
Z. K. Peng ◽  
X. J. Dong ◽  
...  
Keyword(s):  
State Space ◽  
Discrete Time ◽  
Nonlinear Models ◽  
State Space Model ◽  
Accurate Estimation ◽  
Structural System ◽  
Dual Frequency ◽  
Continuous Time Model ◽  
Time Model ◽  
State Space System

Subspace-based state space system identification method is well known for its computational efficiency and robustness. However, when directly applied to nonlinear models, intrinsic drawbacks exist and may lead to less accurate estimation. This paper explains how the error is generated when transforming continuous-time model to discrete-time one and then presents an approach to identify nonlinear systems by choosing two different sample frequencies for different parts of the discrete-time state-space model. The method allows to identify the nonlinear coefficients and the underlying linear system more robustly based on only the input and output of the structural system. Several numerical examples are given to illustrate the correctness of the analysis and the effectiveness of the method.

scholarly journals A Discrete-TimeGeo/G/1 Retrial Queue with Two Different Types of Vacations

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Feng Zhang ◽  
Zhifeng Zhu
Keyword(s):  
Discrete Time ◽  
Queueing System ◽  
Retrial Queue ◽  
System Size ◽  
Stochastic Decomposition ◽  
Continuous Time Model ◽  
Time Model ◽  
Different Types ◽  
Number Of Customers ◽  
The Relationship

We analyze a discrete-timeGeo/G/1 retrial queue with two different types of vacations and general retrial times. Two different types of vacation policies are investigated in this model, one of which is nonexhaustive urgent vacation during serving and the other is normal exhaustive vacation. For this model, we give the steady-state analysis for the considered queueing system. Firstly, we obtain the generating functions of the number of customers in our model. Then, we obtain the closed-form expressions of some performance measures and also give a stochastic decomposition result for the system size. Moreover, the relationship between this discrete-time model and the corresponding continuous-time model is also investigated. Finally, some numerical results are provided to illustrate the effect of nonexhaustive urgent vacation on some performance characteristics of the system.

IDENTIFICATION OF ON-AND OFF-LINE LINEAR STATE SPACE MODELS USING SUBSPACE METHODS

2015 ◽  
Vol 77 (28) ◽  
Author(s):  
Nurul Syahirah Khalid ◽  
Norhaliza Abd. Wahab ◽  
Muhammad Iqbal Zakaria
Keyword(s):  
State Space ◽  
State Space Model ◽  
Model Identification ◽  
State Space Models ◽  
Subspace Methods ◽  
State Space System ◽  
Linear State ◽  
Value Decomposition ◽  
Error State

In this paper, subspace identification methods are proposed to analyze the differences between On-And Off-Line Linear State Space Models Using Subspace Methods. There are several ways that can estimate the order of the system. For this paper, Singular Value Decomposition (SVD) is used to estimate the order of the system. Comparing with the others methods, this method only need a limited number of input and output data for the determination of the system matrices. Two methods of the subspace algorithm are used which is N4SID (Numerical algorithm for Subspace State Space System Identification) and MOESP (Multivariable Output-Error State-Space model identification).

scholarly journals Predicting IBNYR Events and Delays II. Discrete Time

1990 ◽  
Vol 20 (1) ◽  
pp. 93-111 ◽  
Author(s):  
William S. Jewell
Keyword(s):  
Discrete Time ◽  
Continuous Time ◽  
Bayesian Method ◽  
Predictive Accuracy ◽  
Random Delay ◽  
Continuous Time Model ◽  
Practical Situation ◽  
Time Model ◽  
Numerical Example ◽  
Exposure Interval

AbstractAn IBNYR event is one that occurs randomly during some fixed exposure interval and incurs a random delay before it is reported. A previous paper developed a continuous-time model of the IBNYR process in which both the Poisson rate at which events occur and the parameters of the delay distribution are unknown random quantities; a full-distributional Bayesian method was then developed to predict the number of unreported events. Using a numerical example, the success of this approach was shown to depend upon whether or not the occurrence dates were available in addition to the reporting dates. This paper considers the more usual practical situation in which only discretized epoch information is available; this leads to a loss of predictive accuracy, which is investigated by considering various levels of quantization for the same numerical example.

scholarly journals Model Predictive Control with a Relaxed Cost Function for Constrained Linear Systems

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
David Sotelo ◽  
Antonio Favela-Contreras ◽  
Viacheslav V. Kalashnikov ◽  
Carlos Sotelo
Keyword(s):  
Model Predictive Control ◽  
Cost Function ◽  
State Space ◽  
Predictive Control ◽  
Discrete Time ◽  
Control Strategy ◽  
Control Strategies ◽  
State Space Model ◽  
Control Technique ◽  
Space Model

The Model Predictive Control technique is widely used for optimizing the performance of constrained multi-input multi-output processes. However, due to its mathematical complexity and heavy computation effort, it is mainly suitable in processes with slow dynamics. Based on the Exact Penalization Theorem, this paper presents a discrete-time state-space Model Predictive Control strategy with a relaxed performance index, where the constraints are implicitly defined in the weighting matrices, computed at each sampling time. The performance validation for the Model Predictive Control strategy with the proposed relaxed cost function uses the simulation of a tape transport system and a jet transport aircraft during cruise flight. Without affecting the tracking performance, numerical results show that the execution time is notably decreased compared with two well-known discrete-time state-space Model Predictive Control strategies. This makes the proposed Model Predictive Control mainly suitable for constrained multivariable processes with fast dynamics.

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