• Journal of Institute of Control, Robotics and Systems
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• v.7 no.8
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• pp.647-655
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• 2001

In adaptive neuro-control, neural networks are used to approximate unknown plant nonlinearities. Until now, most of the studies in the field of controller design for nonlinear system using neural network considers the affine system with fixed number of neurons. This paper considers nonaffine nonlinear systems and on-line variation of the number of neurons. A control law and adaptive laws for neural network weights are established so that the whole system is stable in the sense of Lyapunov. In addition, at the expense of th input, tracking error converges to the arbitrary small neighborhood of the origin. The efficiency of the proposed scheme is shown through simulations ofa simple nonaffine nonlinear system.

• Journal of the Earthquake Engineering Society of Korea
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• v.6 no.3
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• pp.53-62
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• 2002

Site soil condition affects significantly on the seismic response of a structure and is a critical factor for the performance based seismic design of a structure. In this paper, the effects of nonlinear soil properties on the elastic response spectra of a structure including the nonlinearity of a soil due to the earthquake excitation is investigated using one step finite element approach for the entire soil structure system and approximate linear iterative procedure to simulate the nonlinear soil behavior with the Ramberg-Osgood soil model. Studies were carried out for a linear SDOF system of a variable period with and without a pile group for the 1940 CI Centro earthquake recorded on ground rather than rock. The study results showed clearly that the effect of the nonlinear behavior of soft soil is very important on the elastic seismic response of a structure suggesting the necessity of the performance based seismic design.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2002.05a
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• pp.140-146
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• 2002

The fluid induced vibration (FIV) phenomena of a 2-D.O.F airfoil system have been investigated in low Reynolds number incompressible flow region. Unsteady flows with viscosity are computed using two-dimensional incompressible Navier-stokes code. To validate developed Navier-Stokes code, steady and unsteady flow fields around airfoil are analyzed. The present fluid/structure interaction analysis is based on the most accurate computational approach with computational fluid dynamics (CSD) and computational structural dynamics (CSD) techniques. The highly nonlinear fluid/structure interaction phenomena due to severe flow separations have been analyzed fur the low Reynolds region (R$_{N}$ =500~5000) that has a dominancy of flow viscosity. The effect of R$_{N}$ on the fluid/structure coupled vibration instability of 2-DOF airfoil system is presented and the effect of initial angle of attack on the dynamic instability are also shown.own.

• Structural Engineering and Mechanics
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• v.89 no.4
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• pp.421-436
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• 2024

This research addresses experimentally the relationship between the excitation frequency and both hoop and axial wall stresses in a water storage tank. A low-density polyethylene tank with six different aspect ratios (water level to tank radius) was tested using a shake table. A laminar box with sand represents a soil site to simulate Soil-Structure Interaction (SSI). Sine excitations with eight frequencies that cover the first free vibration frequency of the tank-water system were applied. Additionally, Ricker wavelet excitations of two different dominant frequencies were considered. The maximum stresses are compared with those using a nonlinear elastic spring-mass model. The results reveal that the coincidence between the excitation frequency and the free-vibration frequency of the soil-tank-water system increases the sloshing intensity and the rigid-like body motion of the system, amplifying the stress development considerably. The relationship between the excitation frequency and wall stresses is nonlinear and depends simultaneously on both sloshing and uplift. In most cases, the maximum stresses using the nonlinear elastic spring-mass model agree with those from the experiments.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2005.03a
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• pp.405-412
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• 2005

In this study, a numerical method for soil-pile-structure interaction problems in multi-layered half-plane is developed. The total soil-pile-structure interaction system is divided into two parts namely, nonlinear structure part and linear soil-pile interaction parts. In the structure field, the general finite element method is introduced to solve the dynamic equation of motion for the structure. In the soil-pile structure interaction part, physical model consisting of lumped parameter, which is frequency dependent coefficient and determined by rigorous analysis method is introduced. Using proposed analysis procedure, the nonlinear behavior of structure considering soil-structure interaction can be efficiently determined in time domain and the analysis cost is dramatically reduced.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.2
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• pp.701-704
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• 2005

PID controllers, which have been widely used in industry, have a simple structure and robustness to modeling error. But They are difficult to have uniformly good control performance in system parameters variation or different velocity command. In this paper, we propose a nonlinear adaptive PID controller based on a cell-mediated immune response and a gradient descent learning. This algorithm has a simple structure and robustness to system parameters variation. To verify performances of the proposed nonlinear adaptive PID controller, the speed control of nonlinear DC motor is performed. The simulation results show that the proposed control systems are effective in tracking a command velocity under system parameters variation.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.40 no.4
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• pp.264-274
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• 2003

This paper presents a direct generalized minimum-variance self tuning controller with a PID structure using neural network which adapts to the changing parameters of the nonlinear system with nonminimum phase behavior, noises and time delays. The self-tuning controller with a PID structure is a combination of the simple structure of a PID controller and the characteristics of a self-tuning controller that can adapt to changes in the environment. The self-tuning control effect is achieved through the RLS (recursive least square) algorithm at the parameter estimation stage as well as through the Robbins-Monro algorithm at the stage of optimizing the design parameter of the controller. The neural network control effect which compensates for nonlinear factor is obtained from the learning algorithm which the learning error between the filtered reference and the auxiliary output of plant becomes zero. Computer simulation has shown that the proposed method works effectively on the nonlinear nonminimum phase system with time delays and changed system parameter.

• Proceedings of the KIEE Conference
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• 1999.11c
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• pp.827-829
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• 1999

The identification of nonlinear system using Extended GMDH(EGMDH) is studied in this paper. The proposed EGMDH algorithm is based on GMDH(Group Method of Data handling) method and its structure is similar to Neural Networks. The each node of EGMDH structure utilizes several types of high-order polynomial such as linear, quadratic and cubic, and is connected as various kinds of multi-variable inputs. As the operating condition changes, the parameters of EGMDH will also change, so the proposed scheme by means of the EGMDH method is capable of adapting rapidly to the changing environment. The simulation result shows that the simple nonlinear process can be modeled reasonably well by the proposed method which are simple but efficient.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.383-386
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• 1997

In this paper, We proposed a parallel structure of the Neural Network model to nonlinear complex system. Neural Network was used as basic model which has learning ability and high tolerence level. This paper, we used Neural Network which has BP(Error Back Propagation Algorithm) model. But it sometimes has difficulty to append characteristic of input data to nonlinear system. So that, I used HCM(hard c-Means) method of clustering technique to append property of input data. Clustering Algorithms are used extensively not only to organized categorize data, but are also useful for data compression and model construction. Gas furance, a sewage treatment process are used to evaluate the performance of the proposed model and then obtained higher accuracy than other previous medels.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.86-86
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• 2000

This paper describes a neural network based fuzzy type PID control scheme. The PID controller is being widely used in industrial applications. however, it is difficult to determine the appropriate PID gains for (he nonlinear system control. In this paper, we re-analyzed the fuzzy controller as conventional PID controller structure, and proposed a neural network based fuzzy type PID controller whose scaling factors were adjusted automatically. The value of initial scaling factors of the proposed controller were determined on the basis of the conventional PID controller parameters tuning methods and then they were adjusted by using neural network control techniques. Proposed controller was simple in structure and computational burden was small so that on-line adaptation was easy to apply to. The result of practical experiment on the magnetic levitation system, which is known to be hard nonlinear, showed the proposed controller's excellent performance.