• 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.

• 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.

• Transactions of the Korean Society of Automotive Engineers
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• v.10 no.5
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• pp.29-34
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• 2002

This study presents the process of the continuous-time system identification for unknown nonlinear systems. The Radial Basis Function(RBF) error filtering identification model is introduced at first. This identification scheme includes RBF network to approximate unknown function of nonlinear system which is structured by affine form. The neural network is trained by the adaptive law based on Lyapunov synthesis method. The identification scheme is applied to engine and the performance of RBF error filtering Identification model is verified by the simulation with a three-state engine model. The simulation results have revealed that the values of the estimated function show favorable agreement with the real values of the engine model. The introduced identification scheme can be effectively applied to model-based nonlinear control.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.29 no.7 s.238
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• pp.938-946
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• 2005

Friction is a dominant nonlinear factor in servomechanisms, which seriously deteriorates system accuracy. A friction compensator is indispensable to fabricate high-performance servomechanisms. In order to compensate for the friction in the servomechanism, identification of the friction elements is required. To estimate the friction of the servomechanism, an accurate linear element model of the system is required first. Tn this paper, a nonlinear friction model, in which static, coulomb and viscous frictions as well as Stribeck effect are included, is identified through the describing function approximation of the nonlinear element. A nonlinear element composed of two relays is intentionally devised to induce various limit cycle conditions in the velocity control loop of the servomechanism. The friction coefficients are estimated from the intersection points of the linear and nonlinear elements in the complex plane. A Butterworth filter is added to the velocity control loop not only to meet the assumption of the harmonic balance method but also to improve the accuracy of the friction identification process. Validity of the proposed method is confirmed through numerical simulations and experiments. In addition, a model-based friction compensator is applied as a feedforward controller to compensate fur the nonlinear characteristics of the servomechanism and to verify the effectiveness of the proposed identification method.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.11
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• pp.127-139
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• 1996

In this paper we propose a new controller design method using hybrid fuzzy-neural netowrk and neural network identification in order ot control systems which are more and more getting nonlinearity. Proposed method performs, for a nonlinear plant with unknown functions, hybird identification using a fuzzy-neural network and a neural network, and then a stable nonlinear controller is designed with those identified informations. To identify a nonlinear function, which is directly related to input signals, we can use a neural network which is satisfied with the proposed stable condition. To identify a nonlinear function, which is not directly related to input signals, we can use a fuzzy-neural network which has excellent identification characteristics. In order to verify excellent control performances of the proposed method, we compare the porposed control method with a conventional neural network control method through simulations and experiments with one link manipulator.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2004.05a
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• pp.663-668
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• 2004

To model and understand the physics of partial rub, a nonlinear rotor model is investigated by applying 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 the nonlinear model on the basis of experimental data. The estimated nonlinear model shows good agreements between numerical and experimental results in its orbit.

• 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.15 no.2
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• pp.409-424
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• 2015

Structural nonlinearity is a common phenomenon encountered in engineering structures under severe dynamic loading. It is necessary to localize and identify structural nonlinearities using structural dynamic measurements for damage detection and performance evaluation of structures. However, identification of nonlinear structural systems is a difficult task, especially when proper mathematical models for structural nonlinear behaviors are not available. In prior studies on nonparametric identification of nonlinear structures, the locations of structural nonlinearities are usually assumed known and all structural responses are measured. In this paper, an identification algorithm is proposed for locating and identifying model-free structural nonlinearities and systems using incomplete measurements of structural responses. First, equivalent linear structural systems are established and identified by the extended Kalman filter (EKF). The locations of structural nonlinearities are identified. Then, the model-free structural nonlinear restoring forces are approximated by power series polynomial models. The unscented Kalman filter (UKF) is utilized to identify structural nonlinear restoring forces and structural systems. Both numerical simulation examples and experimental test of a multi-story shear building with a MR damper are used to validate the proposed algorithm.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.12
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• pp.1280-1286
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• 2004

A lot of industrial chemical processes contain certain input nonlinearities even though they are controlled by several linear controllers. Here we investigate a sequential loop closing identification method for MIMO Hammerstein nonlinear processes with diagonal nonlinearities. The proposed method separates the identification of the nonlinear static function from that of the linear subsystem by using a relay feedback test and a triangular type signal test. From 2 n activations for n n MIMO nonlinear processes, we sequentially identify the whole range of the nonlinear static function as well as the transfer function matrix of the linear subsystem.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.124.3-124
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• 2001

The nonlinearity of several chemical processes is usually approximated by a series of the nonlinear static element and the linear subsystem. In the case of the model that the nonlinear static element precedes the linear subsystem, it is called a Hammerstein model. It is a Wiener model when the order is reserved. Here we investigate a relay feedback identification method for Hammerstein type nonlinear processes. The proposed method separates the identification of the nonlinear static function from that of the linear subsystem by using a relay feedback method. From two times activation of nonlinear processes, we identify he whole range of the nonlinear static function as well as the ultimate information of the linear subsystem.