• Proceedings of the KIEE Conference
• /
• 1984.07a
• /
• pp.84-85
• /
• 1984

The accurate description of many physical processes leads to high number of different equations which are very difficult to handle for simulation or control purposes. The reduction of high-order, linear, time-invariant systems to lower-order ones has been investigated by many researchers. In this paper, a model technique among these methods is used. This technique has been developed here as if it were extensions of Davison's original method (1), its modification having been made to provide, among other things, steady state agreement between the original large-scale and reduced-order model. The advantage of the modal analysis approach is that only matrix operations have to be executed. Here, it is very simple to obtain a reduced model. An example of illustration is shown using the model method.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.52 no.11
• /
• pp.619-625
• /
• 2003

We proposed an optimum PID controller design method of the Smith Predictor It can be applied to various processes. The real process is approximated via the second order plus time delay model (SOPTD) whose parameters are specified through a model reduction algorithm. We already proposed a new model reduction method that considered four point in the Nyquist curve to reduced the steady state error between the real process model and the reduced model using the gradient decent method and the genetic algorithms. In addition, the Smith predictor is used to compensate time delay of the real process model. In this paper, the new optimum parameter tuning algorithm for PID controller of the Smith Predictor is proposed through ITAE as performance index. The Simulation results show the validity and improvement of performance for various processes.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2007.05a
• /
• pp.988-992
• /
• 2007

Recently, dynamic system has been enlarged and is exposed to various types of disturbance. Thus designing controller for those dynamic system under random disturbance is not practically easy. As a result, the exact analysis for the system which is exposed to various irregular disturbance is quite important. In order to perform analysis, conventional BMR(Balanced Model Reduction) method is applied to moment equation in stochastic domain and reliable reduced order system model has been obtained.

• Transactions of the Korean Society for Noise and Vibration Engineering
• /
• v.17 no.10
• /
• pp.912-917
• /
• 2007

Recently, dynamic system has been enlarged and is normally exposed to various types of disturbance. Thus designing controller for these dynamic systems under random disturbance is not practically easy. As a result, the exact analysis for the system which is exposed to various irregular disturbance is quite important. In order to perform analysis, conventional BMR(balance model reduction) method is adopted and applied to moment equation in stochastic domain. Reliable reduced order system model has been obtained.

• Journal of Institute of Control, Robotics and Systems
• /
• v.4 no.2
• /
• pp.170-177
• /
• 1998

In this paper, a unified solution of Hankel norm approximation problem is proposed by $\delta$-operator. To derive the main result, all-pass property is derived from the inner and co-inner property in the $\delta$-domain. The solution of all-pass becomes an optimal Hankel norm approximation problem in .delta.-domain through LLFT(Low Linear Fractional Transformation) inserting feedback term $\phi(\gamma)$, which is a free design parameter, to hold the error bound desired against the variance between the original model and the solution of Hankel norm approximation problem. The proposed solution does not only cover continuous and discrete ones depending on sampling interval but also plays a key role in robust control and model reduction problem. The verification of the proposed solution is exemplified via simulation for the zero-order Hankel norm approximation problem and the model reduction problem applied to a 16th order MIMO system.

• 제어로봇시스템학회:학술대회논문집
• /
• 1990.10a
• /
• pp.556-558
• /
• 1990

A method of model reduction for reducing a higher order Z-transfer function to its lower order model is developed based on the Block - pulse function. The approach is following : I. Block - pulse function can be applied for Z-transfer function of linear digital system described by high order. II. To determined both the coefficients of the denominator and numerator of reduced model. The proposed method is simple for computation, can preserve the dynamic characteristic of the original model satisfactorily.

• Journal of the Korean Institute of Telematics and Electronics S
• /
• v.35S no.5
• /
• pp.27-34
• /
• 1998

This paper is on the discrete model reduction method over disc-type analytic domains. We define hankel singular value over the disc that is mapped by standard bilinear mapping. And the generalized singular perturbation approximation and the direct truncation are generalized to GSPA and DT over a disc. Furthermore, it is shown that the reduced order model over a smaller domaing has a smaller .inf.-norm error bound. And the poposed reduction method is used to obtain the regional pole placement property.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.38 no.9
• /
• pp.933-941
• /
• 2014

This paper provides a comparison between the Krylov subspace method (KSM) and modal truncation method (MTM), which are typical projection-based model order reduction methods. The frequency responses are compared to determine the numerical accuracies and efficiencies. In order to compare the numerical accuracies of the KSM and MTM, the frequency responses and relative errors according to the order of the reduced model and frequency of interest are studied. Subsequently, a numerical examination shows whether a reduced order can be determined automatically with the help of an error convergence indicator. As for the numerical efficiency, the computation time needed to generate the projection matrix and the solution time to perform a frequency response analysis are compared according to the reduced order. A finite element model for a car suspension is considered as an application example of the numerical comparison.

• Structural Monitoring and Maintenance
• /
• v.5 no.3
• /
• pp.417-428
• /
• 2018

In this paper, various model reduction methods were assessed using a shear frame, plane and space truss structures. Each of the structures is one-dimensional, two-dimensional and three-dimensional, respectively. Three scenarios of poor, better, and the best were considered for each of the structures in which 25%, 40%, and 60% of the total degrees of freedom (DOFs) were measured in each of them, respectively. Natural frequencies of the full and reduced order structures were compared in each of the numerical examples to assess the performance of model reduction methods. Generally, it was found that system equivalent reduction expansion process (SEREP) provides full accuracy in the model reduction in all of the numerical examples and scenarios. Iterated improved reduced system (IIRS) was the second-best, providing acceptable results and lower error in higher modes in comparison to the improved reduced system (IRS) method. Although the Guyan's method has very low levels of accuracy. Structures were classified with the excitation frequency. High-frequency structures compared to low-frequency structures have been poor performance in the model reduction methods (Guyan, IRS, and IIRS).

• Proceedings of the KIEE Conference
• /
• 1987.07a
• /
• pp.195-197
• /
• 1987

A new method of model reduction, based on the differentiation of polynomials, is Introduced by Gutman(1982). Without the reciprocal transformation, and the differentiation of the numerator and denominator polynomials of the transfer function, used by Gutman, the lower-order system can be obtained by the permutation of the order of numerator and denominator and the number of differentiation.