• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.451-454
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• 1998

This paper proposes a new method of Model Predictive Control (MPC) of nonlinear process by us-ing the measured Volterra kernels as the nonlinear model. A nonlinear dynamical process is usually de-scribed as Volterra kernel representation, In the authors' method, a pseudo-random M-sequence is ar plied to the nonlinear process, and its output is measured. Taking the crosscorrelation between the input and output, we obtain the Volterra kernels up to 3rd order which represent the nonlinear characteristics of the process. By using the measured Volterra kernels, we can construct the nonlinear model for MPC. In applying Model Predictive Control to a nonlinear process, the most important thing is, in general, what kind of nonlinear model should be used. The authors used the measured Volterra kernels of up to 3rd order as the process model. The authors have carried out computer simulations and compared the simulation results for the linear model, the nonlinear model up to 2nd Volterra kernel, and the nonlinear model up to 3rd order Vol-terra kernel. The results of computer simulation show that the use of Valterra kernels of up to 3rd order is most effective for Model Predictive Control of nonlinear dynamical processes.

• Journal of KSNVE
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• v.10 no.3
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• pp.451-456
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• 2000

In this paper a discrete time model for the identification of nonlinear vibration system with stick-slip friction is proposed. The proposed model can handle the highly nonlinear behavior of the friction such as stick-slip phenomenon and Stribeck effect. The basic idea of the proposed model is as follows : If the nonlinearity of the system can be predicted as a simple function then this nonlinear function term cab be directly used in the discrete time model. By doing this the number of nonlinear terms in the model can be much less than those of NARMAX model which is widely used nonlinear discrete model. The simulation result shows that the proposed model can estimate the response of the nonlinear vibration system with stick-slip friction very well with less computational effort.

• Proceedings of the Korea Concrete Institute Conference
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• 1997.04a
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• pp.392-397
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• 1997

In this paper, design and analysis of anchorage zone in prestressed concrete structure using nonlinear strut and tie model is presented. Nonlinear strut and tie model is an analysis and design model which constructs strut and tie model based on nonlinear analysis considering the nonlinear behavior of concrete. Based on the nonlinear strut and tie model, the analysis and design are performed for the anchorage zone having singular concentric tendons, singular eccentric tendons and multiple tendons, respectively. For verification of the model, comparisons are made with experimental results as well as results by linear strut and tie models. from the comparisons, it is shown that the design of the anchorage zone by the nonlinear model is still economical without loosing the degree of safety and the prediction of the ultimate load by the nonlinear model gives better accuracy than by the linear one.

• Journal of KSNVE
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• v.10 no.1
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• pp.144-152
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• 2000

The identification of a nonlinear vibration system based on the time domain parametric model has been widely studied in recent years. In most of the studies, the NARMAX model has been used for the identification of a nonlinear system. However, the computational load for the identification with this model is quite heavy. In this paper, a new modeling procedure for nonlinear system identification in discrete time domain is proposed. The proposed model has less initial nonlinear terms than NARMAX model, and the terms in the proposed model are derived from physically meaningful way. The performance of the proposed method is evaluated through the simulation, and the result shows that the proposed model can identify the nonlinear characteristics of the vibration system very will less computational effort.

• Journal of the Korean Statistical Society
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• v.36 no.2
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• pp.183-200
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• 2007

We consider a nonlinear autoregressive moving average model with nonlinear GARCH errors, and find sufficient conditions for the existence of a strictly stationary solution of three related time series equations. We also consider a geometric ergodicity and functional central limit theorem for a nonlinear autoregressive model with nonlinear ARCH errors. The given model includes broad classes of nonlinear models. New results are obtained, and known results are shown to emerge as special cases.

• Smart Structures and Systems
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• v.29 no.5
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• pp.729-746
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• 2022

In this paper, a nonlinear structural model updating methodology based on the Deep Belief Network (DBN) is proposed. Firstly, the instantaneous parameters of the vibration responses are obtained by the discrete analytical mode decomposition (DAMD) method and the Hilbert transform (HT). The instantaneous parameters are regarded as the independent variables, and the nonlinear model parameters are considered as the dependent variables. Then the DBN is utilized for approximating the nonlinear mapping relationship between them. At last, the instantaneous parameters of the measured vibration responses are fed into the well-trained DBN. Owing to the strong learning and generalization abilities of the DBN, the updated nonlinear model parameters can be directly estimated. Two nonlinear shear-type structure models under two types of excitation and various noise levels are adopted as numerical simulations to validate the effectiveness of the proposed approach. The nonlinear properties of the structure model are simulated via the hysteretic parameters of a Bouc-Wen model and a Giuffré-Menegotto-Pinto model, respectively. Besides, the proposed approach is verified by a three-story shear-type frame with a piezoelectric friction damper (PFD). Simulated and experimental results suggest that the nonlinear model updating approach has high computational efficiency and precision.

• Journal of Mechanical Science and Technology
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• v.18 no.11
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• pp.1969-1977
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• 2004

To model and understand the physics of partial rub, a nonlinear rotor model is sought by applying a nonlinear parameter identification technique to the experimental data. The results show that the nonlinear terms of damping and stiffness should be included to model partial rotor rub. Especially, the impact and friction during the contact between rotor and stator are tried to explain with a nonlinear model on the basis of experimental data. The estimated nonlinear model shows good agreements between the numerical and the experimental results in its orbit. Also, the estimated nonlinear model could explain the backward whirling orbit and jump phenomenon, which are the typical phenomena of partial rub.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.490-495
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• 1992

A neural model predictive control strategy combining a neural network for plant identification and a nonlinear programming algorithm for solving nonlinear control problems is proposed. A constrained nonlinear optimization approach using successive quadratic programming cooperates with neural identification network is used to generate the optimum control law for the complicate continuous/batch chemical reactor systems that have inherent nonlinear dynamics. Based on our approach, we developed a neural model predictive controller(NMPC) which shows excellent performances on nonlinear, model-plant mismatch cases of chemical reactor systems.

• Smart Structures and Systems
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• v.20 no.4
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• pp.415-426
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• 2017

Traditional experimental verification for nonlinear system identification often faces the problem of experiment model repeatability. In our research, a steel frame experimental model is developed to imitate the behavior of a single story steel frame under horizontal excitation. Two adjustable rotational dampers are used to simulate the plastic hinge effect of the damaged beam-column joint. This model is suggested as a benchmark model for nonlinear dynamics study. Since the nonlinear form provided by the damper is unknown, a Morlet wavelet based method is introduced to identify the mathematical model of this structure under different damping cases. After the model identification, earthquake excitation tests are carried out to verify the generality of the identified model. The results show the extensive applicability and effectiveness of the identification method.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.5
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• pp.157-165
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• 1995

In this paper the comparison between the neural networks and traditional approaches as nonlinear system identification methods are considered. Two model structures of neural networks are the state space model and the input output model neural networks. The traditional methods are the AutoRegressive eXogeneous Input model and the Nonlinear AutoRegressive eXogeneous Input model. Computer simulation for an analytic dynamic model of a single input single output nonlinear system has been done for all the chosen models. Model validation for the obtained models also has been done with testing inputs of the sinusoidal, ramp and the noise ramp.