• International Journal of Control, Automation, and Systems
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• v.1 no.1
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• pp.28-34
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• 2003

In this paper, the problem of designing an adaptive observer for a class of nonlinear systems with linear unknown parameters is studied. The nonlinear system to be considered consists of two blocks, only one of which has measurable states. Assuming the minimum-phase property of the error dynamics obtained after a change of coordinates and imposing some conditions on the functions multiplied by unknown parameters, an adaptive observer is constructed using an existing observer design method.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.05a
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• pp.203-206
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• 2002

This paper describes fuzzy control methodologies of the steam generator which have nonlinear characteristics in the nuclear power plant. Actually, the steam generator part of the power generator has a problem to control water level because it has complex components and nonlinear characteristics. In order to control nonlinear terms of the model, Takagj-Sugeno (75) fuzzy system is used to design a controller. In designing procedure, intelligent digital redesign method is used to control the nonlinear system. This digital controller keeps the performance of the analog controller. Simulation examples are included for ensuring the proposed control method.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.6
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• pp.1229-1238
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• 2011

This paper deals with the lateral dynamic model of an all-wheel steered articulated vehicle to design a guidance controller. Nonlinear dynamic model of articulated vehicle is developed by complementing the model about the BRT system of California PATH in U. S. A. and the Phileas system of the APTS in Netherlands. Linear lateral dynamic model has been derived from the nonlinear dynamic model under some assumptions associated with the driving conditions. To design a guidance controller, we derive a transfer function that is steering angle as input and lateral acceleration as output from the linear lateral dynamic model by applying the parameter of vehicle that is developed by Korea Railroad Research Institute. To validate the dynamic model, nonlinear dynamic model has been compared with a vehicle model that has been programmed in ADAMS, and linear dynamic model has been compared with a nonlinear dynamic model under sime assumptions.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.6
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• pp.1159-1166
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• 2010

To improve prediction quality of a nonlinear prediction system, the system's capability for uncertainty of nonlinear data should be satisfactory. This paper presents a TSK fuzzy prediction system that can consider and deal with the uncertainty of nonlinear data sufficiently. In the design procedures of the proposed system, HCBKA(Hierarchical Correlationship-Based K-means clustering Algorithm) was used to generate the accurate fuzzy rule base that can control output according to input efficiently, and the first-order difference method was applied to reflect various characteristics of the nonlinear data. Also, multiple prediction systems were designed to analyze the prediction tendencies of each difference data generated by the difference method. In addition, to enhance the prediction quality of the proposed system, an error compensation method was proposed and it compensated the prediction error of the systems suitably. Finally, the prediction performance of the proposed system was verified by simulating two typical time series examples.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.5
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• pp.966-972
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• 2007

This paper presents position control of nonlinear three-dimensional crane systems using neural network approach. Such crane system generally includes very complicated characteristic dynamics and mechanical framework such that its mathematical model is expressed by strong nonlinearity. This leads difficulty in control design for the systems. We linearize the nonlinear system model to construct PID control applying well-known linear control theory and then neural network is utilized to compensate system perturbation due to linearization. Thus, control input of the crane system is composed of nominal PID and neural output signals respectively. Our method illustrates simple design procedure, but system perturbation and modelling error are overcome through a neural compensator. As well. adaptive neural control is constructed from online learning. Computer simulation demonstrates our control approach is superior to the classic control systems.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.399-402
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• 1996

The electrodynamic loudspeakers should have a wide dynamic range to reproduce various sound levels. When the input signal is small, the radiated sound from the loudspeaker is not so much distorted. However, for large input signal with low frequency component the radiated sound is significantly distorted due to the nonlinearities of the loudspeaker. The suspension, damping, and magnetic flux of loudspeaker are the main sources of the nonlinearity. Such electromechanical parameters related to harmonic distortion have been represented by a polynomial model for diaphragm displacement, while each of the polynomial coefficient is evaluated by using the principle of harmonic balance experimentally. Based on the polynomial model, we designed a compensator for nonlinear harmonic distortion of direct radiator loudspeaker. Than observer is used to estimate the displacement of the loudspeaker diaphragm, which is rather difficult to measure directly in the conventional setting. The usefulness of the designed compensator is demonstrated by numerical simulations. Simulation results show about 30db decrease at the second and third higher harmonic distortions. We carry out an experiment on speaker to verify designed controller and nonlinear observer.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1516-1520
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• 2005

We proposed the indirect adaptive fuzzy model based sliding mode controller to control a nonaffine nonlinear systems. Takagi-Sugano fuzzy system is used to represent the nonaffine nonlinear system and then inverted to design the controller at each sampling time. Also sliding mode component is employed to eliminate the effects of disturbances, while a fuzzy model component equipped with an adaptation mechanism reduces modeling uncertainties by approximating model uncertainties. The proposed controller and adaptive laws guarantee that the closed-loop system is stable in the sense of Lyapunov and the output tracks a desired trajectory asymptotically.

• Proceedings of the KIEE Conference
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• 1993.11a
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• pp.326-328
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• 1993

Modelling theory, based on differential equations, is not an adequate tool for solving the problems of complex system. Identification of complex system using GMDH(group method of data handling) is more appropriate for this problems. In this paper, GMDH algorithm is used to identify the nonlinear plant and to design model following nonlinear control system. Simulation for the DC motor show the good performance of model following nonlinear control system.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.93.4-93
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• 2001

Based on input and output data pairs of nonlinear systems, this paper proposes a simple and effective Just-In-Time (JIT) method, called Direct JIT Control, for nonlinear control design. It uses an inverse model of controlled plant to compute an initial control action, and then adapts the initial control action by adding a fine-tuning control action, depended on the errors between the real outputs and the expected reference signals. Meanwhile, the proposed JIT method accomplishes the adaptation of the inverse model just simply by means of the refreshment of input and output data pairs. In addition, the JIT modeling technique guarantees this method to obtain an approximate inverse model of the controlled nonlinear plant in the neighborhood of a query. Based on a ...

• Transactions on Control, Automation and Systems Engineering
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• v.2 no.4
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• pp.248-254
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• 2000

This paper presents and output feedback fuzzy H(sup)$\infty$ control problem for a class of nonlinear systems with time-varying delayed state. The Takagi-Sugeno fuzzy model is employed to represent a nonlinear systems with time-varying delayed state. Using a single quadratic Lyapunov function, the globally exponential stability and disturance attenuation of the closed-loop fuzzy control system are discussed. Sufficient conditions for the existence of fuzzy H(sup)$\infty$ controllers are given in terms of matrix inequalities. Constructive algorithm for design of fuzzy H(sup)$\infty$ controller is also developed. A simulation example is given to illustrate the performance of the proposed design method.