• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2000.05a
• /
• pp.426-429
• /
• 2000

In this paper, we propose a system identification and control method using bias-modified neural network. The proposed method performs, for a nonlinear plant with unknown functions, system identification using bias-modified neural network, and then controller is designed with those identified informations. In order to verify the usefulness of the proposed method, we simulated the proposed control method with one link manipulator system and confirmed the excellency.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2001.11a
• /
• pp.409-412
• /
• 2001

Identification of systems operating in closed loop has long been of prime interest in industrial applications. The fundamental problem with closed-loop data is the correlation between the unmeasurable noise and the input. This is the reason why several methods that work in open loop fail when applied to closed-loop data. The prediction error based approaches to the closed-loop system are divided to direct method and indirect method. Both of direct and indirect methods are known to be applied to the closed-loop data without critical modification. But the direct method induces the bias error in the experimental frequency response function and this bias error may deteriorates the parameter estimation performance

• Wind and Structures
• /
• v.9 no.2
• /
• pp.159-178
• /
• 2006

For the slender and flexible cable supported bridges, identification of all the flutter derivatives for the vertical, lateral and torsional motions is essential for its stability investigation. In all, eighteen flutter derivatives may have to be considered, the identification of which using a three degree-of-freedom elastic suspension system has been a challenging task. In this paper, a system identification technique, known as covariance-driven stochastic subspace identification (COV-SSI) technique, has been utilized to extract the flutter derivatives for a typical bridge deck. This method identifies the stochastic state-space model from the covariances of the output-only (stochastic) data. All the eighteen flutter derivatives have been simultaneously extracted from the output response data obtained from wind tunnel test on a 3-DOF elastically suspended bridge deck section-model. Simplicity in model suspension and measurements of only output responses are additional motivating factors for adopting COV-SSI technique. The identified discrete values of flutter derivatives have been approximated by rational functions.

• Journal of Electrical Engineering and Technology
• /
• v.5 no.4
• /
• pp.597-605
• /
• 2010

This paper presents a contribution to parameter identification of a non-linear system using a new strategy to improve the genetic algorithm (GA) method. Since cost function plays an important role in GA-based parameter identification, we propose to improve the simple version of GA, where weights of the cost function are not taken as constant values, but varying along the procedure of parameter identification. This modified version of GA is applied to the induction motor (IM) as an example of nonlinear system. The GA cost function is the weighted sum of stator current and rotor speed errors between the plant and the model of induction motor. Simulation results show that the identification method based on improved GA is feasible and gives high precision.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.67 no.2
• /
• pp.248-254
• /
• 2018

In this paper, a Monte Carlo-based Recursive Least Square(MC-RLS) method is presented to directly identify the inverse model of the dynamical system. Although a RLS method has been used for the identification based on the deterministic data in the closed loop controlled form, it would be better for RLS to identify the model with random data. In addition, the inverse model obtained by inverting the identified forward model may not work properly. Therefore, MC-RLS can be used for the inverse model identification without proceeding a numerical inversion of an identified forward model. The performance of the proposed method is verified through experimental studies on a control moment gyroscope.

• Journal of the Korean Society for Precision Engineering
• /
• v.15 no.11
• /
• pp.172-179
• /
• 1998

This paper proposes an identification method for modes of a thin plate where multiple actuators and sensors are bonded. When a natural frequency of a mode is decoupled from all other natural frequencies, the mode can be identified separatedly with a bandpass filter. Since a thin plate has resonant peaks at natural frequencies, the bandpass filter can be designed to extract the signal of the mode to be identified. Parameters of the second order linear differential equation of the mode can be obtained to apply the Least square method to the extract the modal signal. The proposed identification method is applied to an all-clamped plate with two pairs of piezoelectric actuators and sensors. The outputs of the identified model match with the experimental data well.

• Journal of Biosystems Engineering
• /
• v.15 no.4
• /
• pp.281-289
• /
• 1990

A frequency-domain, system-identification method using a binary multifrequency signal was developed to find the transfer function between smoke intensity and throttle position in a diesel engine. This paper describes the simulation study performed to test the identification method developed. With an assumption of a diesel operation in a limited region about the normal operating state, the linear theory was adopted. Because that air fuel ratio is one of the most important operating variables causing smoke production in diesel combustion, single-input and single-output model was adopted.

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

• Journal of Power System Engineering
• /
• v.4 no.3
• /
• pp.72-80
• /
• 2000

In order to design a stable robust controller, nominal model, and the upper bound about the uncertainty which is the error of the model are needed. The problem to estimate the nominal model of controlled system and the upper bound of uncertainty at the same time is called robust identification. When the nominal model of controlled system and the upper bound of uncertainty in relation to robust identification are given, the evaluation of the validity of the model and the upper bound makes it possible to distinguish whether there is a model which explains observation data including disturbance among the model set. This paper suggests a method to identity the uncertainty which removes disturbance and expounds observation data by giving a probable postulation and plural data set to disturbance. It also examines the suggested method through a numerical computation simulation and validates its effectiveness.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2002.11b
• /
• pp.676-681
• /
• 2002

System Identification is carried out for a full scale five-story builing to design a vibration controller. Dynamic characteristics such as natural frequencies, damping ratios, and modes are obtained from the input/output information by both sine-sweep method and white noise method. The active mass driver installed on the five floor is applied as external loading to move the building and each floor acceleration is measured and processed for the system identification. The identified building will be experimentally investigated again with viscoelastic dampers installed at inter-stories to obtain the response behavior. Corresponding result will be presented soon.