• Journal of Institute of Control, Robotics and Systems
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• v.7 no.7
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• pp.605-613
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• 2001

In this paper, we design a robust adaptive controller for a nonlinear system with uncertainties to be rejected via disturbance adaptation law. The nonlinear system considered has unknown nonlinear functions being influenced by external disturbance. The upper bound of unknown nonlinear functions at each time is estimated by using a disturbance adaptation law. The estimated nonlinear functions are used to design a stabilizing function a control input. Tuning function is used to estimates unknown system parameter without overparametrization. A set-point regulation error converges to a residual set close to zero asymptotically. The effectiveness of the proposed controller is investigated by computer simulation.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2611-2613
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• 2000

This paper deals with a novel approach to unknown inputs observer(UIO) design for linear time-invariant dynamical systems using a fast Walsh transform and Walsh function's differential operation. Generally, UIO has a derivation of system outputs which is not available from the measurement directly. And it is an obstacle to estimate the unknown inputs properly when unexpected measurement noises are presented. Therefore, this paper propose an algebraic approach to eliminate such problems by using a Walsh function's differential operation.

• Journal of Korea Society of Industrial Information Systems
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• v.4 no.1
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• pp.58-62
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• 1999

This paper shows that is possible to identify the system's input-output dynamics exactly in the presence of unknown periodic disturbances for the Active Engine Mount Control Application .The disturbance frequencies and waveforms can be completely unknown and arbitrary. Only measurements of a control excitation signal and the disturbance-contaminated response are used for identification. Examples are given to illustrate the method, including the identification and vibration control of active engine mount for automobile.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.159-162
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• 1997

This paper describes robust stabilization of nonlinear single-input uncertain systems without matching conditions. We consider nonlinear systems with a vector of unknown constant parameters perturbed about a known value. The approach utilizes the generalized controller canonical form to lump the unmatched uncertainties recursively into the matched ones. This can be achieved via nonlinear coordinate transformations which depend not only on the states of the nonlinear system but also on the control input. Then the dynamic robust control law is derived and the stability result is also presented.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1169-1172
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• 1996

A time-optimal control law for quick, strongly nonlinear systems has been developed and demonstrated. This procedure involves the utilization of neural networks as state feedback controllers that learn the time-optimal control actions by means of an iterative minimization of both the final time and the final state error for the known and unknown systems with constrained inputs and/or states. The nature of neural networks as a parallel processor would circumvent the problem of "curse of dimensionality". The control law has been demonstrated for a velocity input type motor identified by a genetic algorithm called GENOCOP.

• Journal of Advanced Marine Engineering and Technology
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• v.11 no.2
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• pp.61-70
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• 1987

미지의 조작량이나 매우 큰 외란이 입력으로 작용하고 있는 동력시스템의 정도 높은 상태를 추정하려면 상태추정에 앞서 시스템의 입력추정이 요구된다. 본 논문에서는 간략형 칼만필터 (SKF:Simplified Kalman Filter)를 이용하여 운동하고 있는 목표물의 상태추정을 행함과 동시에 기동탐지자 (Maneuvering Detector)와 입력추정자 (Input Estimator)에 의해 시스템의 입력을 추정하고 이것에 의하여 SKF의 추정치를 보정해줌으로써 입력이 미지인 동적 시스템의 상태추정에 있어서 추정정도를 개선하는 방법을 제안하며 디지탈계산기를 이용한 시뮤레이션을 통하여 본 방법의 유효성을 밝힌다.

• Proceedings of the KSME Conference
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• 2003.11a
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• pp.256-260
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• 2003

A computer program to calculate thermodynamic properties of oxygen is developed. Procedures for the calculation is briefly discussed. The program calculates unknown thermodynamic properties fixing the state with two independent input properties. If input value by user is inappropriate, it displays an error message. In addition user can change units with easy. The program developed in this work can be utilized to calculate parameters required for the simulation and design of an equipment using oxygen.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.10B
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• pp.1769-1776
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• 2000

In this paper a new method is proposed to identify a modulation method in the case of unknown digitally modulated input signals. The proposed identification method is implemented with an artificial neural network which is based on characteristic feature extracted from the instantaneous amplitude the instantaneous phase and the instantaneous frequency of the input signals. The proposed method was simulated with 9 type signals (ASK2, FSK2, FSK4, PSK2, PSK4, PSK8, QAM8, QAM16) in a noisy communication environment. The results show that the artificial neural network can accurately recognize all kinds of patterns.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.43 no.2 s.344
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• pp.39-43
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• 2006

This paper deals with the AR Identification of unknown system using cumulant, which is the 3rd order statistics of output signal in the presence of the noise signal. The algorithms for identification of unknown system we applies to the AR identification method using the cumulant which is possible to the guarantees of global convergence and the representation of amplitude and phase information of system among with the method of parametric modeling. In the process of identification, we considered unknown system to the one of AR system. After the generation of input signal, it was being passed through the system then We use the its output signal that the noise is added. As a result of identification of AR system by changing the signal to noise ratio, we get the fairly good results compared to original system output values and confirmed that the pole was located in the unit circle of z transform.

• Proceedings of the KAIS Fall Conference
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• 2006.05a
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• pp.307-310
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• 2006

We consider the problem of constructing observers for nonlinear systems with unknown inputs. It is shown that under some conditions, there exists an observer estimating the states of nonlinear systems with unknown inputs. Nonlinear observer design method using observer error linearization and the design technique of unknown input observer(UIO) for linear systems are used to derive conditions. Some illustrative examples are included. In this paper, a direct controller for nonlinear plants using a neural network is presented. The controller is composed of an approximate controller and a neural network auxiliary controller.The approximate controller gives the rough control and the neural network controller gives the complementary signal to further reduce the output tracking error. This method does not put too much restriction on the type of nonlinear plant to be controlled. In this method, a RBF neural network is trained and the system has a stable performance for the inputs it has been trained for. Simulation results show that it is very effective and can realize a satisfactory control of the nonlinear system