• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.24-29
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• 1990

In this paper ,ie propose a hybrid position/force controller of a robot manipulator using double-layer neural network. Each layer is constructed from inverse dynamics and Jacobian transpose matrix, respectively. The weighting value of each neuron is trained by using a feedback force as an error signal. If the neural networks are sufficiently trained it does not require the feedback-loop with error signals. The effectiveness of the proposed hybrid position/force controller is demonstrated by computer simulation using a PUMA 560 manipulator.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.334-334
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• 2000

In this paper, sliding mode observer design principles based on the equivalent control approach are discussed for the systems which may not satisfy the matching conditions. We propose a new approach for designing a sliding observer and the proof of the stability of the state reconstruction error system for time-invariant systems using the Lyapunov method. The reaching time to the sliding surface, the sliding dynamics of the system, the stability of the reconstruction error system via Lyapunov method, sufficient conditions for the existence of the sliding mode are studied.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1994.10a
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• pp.466-471
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• 1994

To make a dynamic system a given desired motion trajectory, a new feedback error learning scheme is proposed which is based on the repeatability of dynamic system motion. This method is composed of feedforward and feedback control laws. A benefit of this control scheme is that the input pattern that generates the desired motion can be formed without estimating the physical parameters of system dynamics. The numerical simulations show the good performance of the proposed scheme

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.744-746
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• 1998

In this paper, a new sliding mode observer is proposed by introducing a new sliding surface. The new sliding surface is defined based on the augmented error system with virtual error state. The new sliding mode observer have more degree of freedom than the existing VSS observer. It can have dynamics on the sliding surface.

• Proceedings of the KSME Conference
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• 2003.11a
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• pp.1458-1463
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• 2003

This paper presents an improved identification algorithm of active magnetic bearing rotor systems considering sensor and actuator dynamics. An AMB rotor system has both real and complex poles so that it is very hard to identify them together. In previous research, a linear transformation through a fictitious proportional feedback was used in order to shift the real poles close to the imaginary axis. However, the identification result highly depends on the fictitious feedback gain, and it is not easy to identify the additional dynamics including sensor and actuators at the same time. First, this paper discusses the necessity and a selection criterion of the fictitious feedback gain. An appropriate feedback gain minimizes dominant SVD(Singular Value Decomposition) error through maximizing rank deficiency. Second, more improvement in the identification is achieved through separating the common additional dynamics in all elements of frequency response matrix. The feasibility of the proposed identification algorithm is proved with two theoretical AMB rotor models. Finally, the proposed scheme is compared with previous identification methods using experimental data, and a great improvement in model quality and large amount of time saving can be achieved with the proposed method.

• Korean System Dynamics Review
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• v.3 no.2
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• pp.49-68
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• 2002

The intent of this study is to develop system dynamics model for assessment of organizational and human factors in nuclear power plant which can contribute to secure the nuclear safety. Previous studies are classified into two major approaches. One is engineering approach such as ergonomics and probability safety assessment(PSA). The other is social science approach such like sociology, organization theory and psychology. Both have contributed to find organization and human factors and to present guideline to lessen human error in NPP. But, since these methodologies assume that relationship among factors is independent they don't explain the interactions among factors or variables in NPP. To overcome these limits, we have developed system dynamics model which can show cause and effect among factors and quantify organizational and human factors. The model we developed is composed of 16 functions of job process in nuclear power, and shows interactions among various factors which affects employees' productivity and job quality. Handling variables such like degree of leadership, adjustment of number of employee, and workload in each department, users can simulate various situations in nuclear power plant in the organization side. Through simulation, user can get insight to improve safety in plants and to find managerial tools in the organization and human side. Analyzing pattern of variables, users can get knowledge of their organization structure, and understand stands of other departments or employees. Ultimately they can build learning organization to secure optimal safety in nuclear power plant.

• Nuclear Engineering and Technology
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• v.53 no.8
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• pp.2556-2563
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• 2021

Metaheuristic algorithms can work well in solving or optimizing problems, especially those that require approximation or do not have a good analytical solution. Particle swarm optimization (PSO) is one of these algorithms. The response quality of these algorithms depends on the objective function and its regulated parameters. The nonlinear nature of the pressurized light-water nuclear reactor (PWR) dynamics is a significant target for PSO. The two-point kinetics model of this type of reactor is used because of fission products properties. The proportional-integral-derivative (PID) controller is intended to control the power level of the PWR at a short-time transient. The absolute error (IAE), integral of square error (ISE), integral of time-absolute error (ITAE), and integral of time-square error (ITSE) objective functions have been used as performance indexes to tune the PID gains with PSO. The optimization results with each of them are evaluated with the number of function evaluations (NFE). All performance indexes achieve good results with differences in the rate of over/under-shoot or convergence rate of the cost function, in the desired time domain.

• Transactions of the Korean Society of Automotive Engineers
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• v.15 no.5
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• pp.48-55
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• 2007

A bushing component of a vehicle suspension system is tested to capture the nonlinear behavior of rubber bushing element using the MTS 3-axes rubber test machine. The results of the tests are used to model the artificial neural network bushing model. The performances from the neural network model usually are dependent on the structure of the neural network. In this paper, maximum error, peak error, root mean square error, and error-to-signal ratio are employed to evaluate the performances of the neural network bushing model. A simple simulation is carried out to show the usefulness of the developed procedure.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1292-1295
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• 1996

This paper proposes an adaptive control technique for the autopilot design of STT missile. Dynamics of the missile is highly nonlinear and the equilibrium point is vulnerable to change due to fast maneuvering. Therefore nonlinear control techniques are desirable for the autopilot design of the missile. The nonlinear controller requires the exact model to obtain satisfactory performance. Generally a look-up table is used for the dynamic coefficients of a missile, so there must be coefficients error during actual flight, and the performance of the nonlinear controller using these data can be degraded. The proposed adaptive control technique compensates the nonlinear controller with modeling error resulting from the error of aerodynamic data and disturbance. To investigate the usefulness, the proposed method is applied to autopilot design of STT missile through simulations.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.26 no.9
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• pp.1849-1858
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• 2002

An h-adaptive scheme of first-order least-squares meshfree method is presented. A posteriori error estimates, which can be readily computed from the residual, are also presented. For elliptic problem the error indicators are further improved by applying the Aubin-Nitsche method. In the proposed refinement scheme, Voronoi cells are utilized to insert nodes at appropriate positions. Through numerical examples, it is demonstrated that the error indicators reveal good correlations with the actual errors and the adaptive first-order least-squares meshfree method is effectively applied to the localized problems such as the shock formation in fluid dynamics.