• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.994-998
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• 2005

A new control method, called a simple model matching, has been recently developed by the author. This is very simple and be applied for linear and nonlinear discrete time systems with/without time lag. Based on this formulation, identification is examined in this paper using an interconnected neural network with the EBP-EWLS learning algorithm. With this result, a control method is also presented for a nonlinear discrete time system.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.899-902
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• 1993

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 combined with neural identification network is used to generate the optimum control law for complex continuous chemical reactor systems that have inherent nonlinear dynamics. The neural model predictive controller (MNPC) shows good performances and robustness. To whom all correspondence should be addressed.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.1
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• pp.118-124
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• 2004

In this paper, we present a indirect adaptive control method using a wavelet neural network (WNN) for the control of chaotic nonlinear systems without precise mathematical models. The proposed indirect adaptive control method includes the off-line identification and on-line control procedure for chaotic nonlinear systems. In the off-line identification procedure, the WNN based identification model identifies the chaotic nonlinear system by using the serial-parallel identification structure and is trained by the gradient-descent method. And, in the on-line control procedure, a WNN controller is designed by using the off-line identification model and is trained by the error back-propagation algorithm. Finally, the effectiveness and feasibility of the proposed control method is demonstrated with applications to the chaotic nonlinear systems.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.45 no.4
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• pp.574-581
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• 1996

The neural network approach has been shown to be a general scheme for nonlinear dynamical system identification. Unfortunately the error surface of a Multilayer Neural Networks(MNN) that widely used is often highly complex. This is a disadvantage and potential traps may exist in the identification procedure. The objective of this paper is to identify a nonlinear dynamical systems based on Self-Organized Distributed Networks (SODN). The learning with the SODN is fast and precise. Such properties are caused from the local learning mechanism. Each local network learns only data in a subregion. This paper also discusses neural network as identifier of nonlinear dynamical systems. The structure of nonlinear system identification employs series-parallel model. The identification procedure is based on a discrete-time formulation. Through extensive simulation, SODN is shown to be effective for identification of nonlinear dynamical systems. (author). 13 refs., 7 figs., 2 tabs.

• 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.

• 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.

• 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 the Korean Society for Nondestructive Testing
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• v.23 no.6
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• pp.559-565
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• 2003

A spectral element model-based structural damage identification method (SDIM) was derived in the previous study by using the damage-induced changes in frequency response functions. However the previous SDIM often provides poor damage identification results because the nonlinear effect of damage magnitude was not taken into account. Thus, this paper improves the previous SDIM by taking into account the nonlinear effect of damage magnitude. Accordingly an iterative solution method is used in this study to solve the nonlinear matrix equation for local damages distribution. The present SDIM is evaluated through the numerically simulated damage identification tests.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.204-210
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• 1998

This paper discusses an on-line fuzzy dynamic model(FDM) identification of nonlinear processes for the design of fuzzy model based fault detection and isolation(FDI). The dynamic behavior of a nonlinear process is represented by a fuzzy aggregation of a set of local linear models. The identification is divided into two procedures. The first is the off-line identification of membership function. The second is the on-line identification of the local linear models. Then, we propose a residual generation scheme based on the parameters of local linear models and show that the scheme can be used for the design of FDI

• Structural Engineering and Mechanics
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• v.85 no.1
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• pp.119-133
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• 2023

Impact event is the key factor influencing the operational state of the mechanical equipment. Additionally, nonlinear factors existing in the complex mechanical equipment which are currently attracting more and more attention. Therefore, this paper proposes a novel hybrid-separate identification strategy to solve the force identification problem of the nonlinear structure under impact excitation. The 'hybrid' means that the identification strategy contains both l1-norm (sparse) and l2-norm regularization methods. The 'separate' means that the nonlinear response part only generated by nonlinear force needs to be separated from measured response. First, the state-of-the-art two-step iterative shrinkage/thresholding (TwIST) algorithm and sparse representation with the cubic B-spline function are developed to solve established normalized sparse regularization model to identify the accurate impact force and accurate peak value of the nonlinear force. Then, the identified impact force is substituted into the nonlinear response separation equation to obtain the nonlinear response part. Finally, a reduced transfer equation is established and solved by the classical Tikhonove regularization method to obtain the wave profile (variation trend) of the nonlinear force. Numerical and experimental identification results demonstrate that the novel hybrid-separate strategy can accurately and efficiently obtain the nonlinear force and impact force for the nonlinear structure.