• International Journal of Precision Engineering and Manufacturing
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• v.7 no.3
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• pp.51-55
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• 2006

An adaptive tracking controller is presented for the vibration reduction of flexible manipulator employed in hazardous area by combining input shaping technique with sliding-mode control. The combined approach appears to be robust in the presence of severe disturbance and unknown parameter which will be estimated by least-square method in real time. In a maneuver strategy, it is found that a hybrid trajectory with a combination of low frequency mode and rigid-body mode results in better performance and is more efficient than the traditional rigid body trajectory alone which many researchers have employed. The feasibility of the adaptive tracking control approach is demonstrated by applying it to the simplified model of robot system. For the applications of the proposed technique to realistic systems, several requirements are discussed such as control stability and large system order resulted from finite element modeling.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10b
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• pp.1300-1305
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• 1990

A VSS observer-based sliding mode control is described for continuous-time systems with uncertain nonlinear elements, in which the Euclidean norm of unknown element is bounded by a known value. For a case of complete state information, we first derive a sliding mode controller consisting of three parts: a linear state feedback control, an equivalent input and a min-niax control. It is then shown that the present attractiveness condition is simpler than that for a case without using the concept of equivalent input. We next design a VSS observer as a completely dual form to the sliding mode controller. Finally, we discuss a cas of incomplete state information by applying the VSS observer.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.485-485
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• 2000

A design method of model following control system using neural networks is proposed. An unknown nonlinear single-input single-output plant is identified using a multilayer neural networks. A linear controller is designed fer the linear approximation model obtained by linearinzing the identification model. The identification model is also used as a plant emulator to obtain the prediction error. Deficient servo performance due to controlling nonlinear plant with only linear controller is mended by adjusting the linear controller output using the prediction output and the parameters of the identification model. An optimal preview controller is adopted as the linear controller by reason of having good servo performance lowering the peak of control input. Validity of proposed method is illustrated through a numerical simulation.

• Proceedings of the KIEE Conference
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• 1997.07b
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• pp.418-421
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• 1997

A control synthesis scheme is presented for nonlinear single-input-single-output (SISO) systems with parametric uncertainty which have completely unstable zero dynamics. The approach involves the derivation of an input-output linearizing control law which achieves internal stability for a nonlinear minimum phase approximation to the original system using Fliess normal form. A vector of unknown constant parameters is also considered. A Lyapunov-based additional control law is shown to stabilize the full system.

• Journal of the Korean Institute of Telematics and Electronics T
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• v.35T no.3
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• pp.25-30
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• 1998

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 features extracted from the instantaneous amplitude, the instantaneous phase and the instantaneous frequency of the input signals. The proposed method was simulated with 8 type signals in a noisy communication environment. The results show that the artificial neural network can accurately recognize all kinds of patterns.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.7
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• pp.107-115
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• 1996

The average distortio of the vector quantizer is calcualted using a probability function F of the input source for a given codebook. But, since the input source is unknown in geneal, using the sample vectors that is realized from a random vector having probability function F, a time-average opeation is employed so as to obtain an approximation of the average distortion. In this case the size of the smple set should be large so that the sample vectors represent true F reliably. The theoretical inspection about the approximation, however, is not perfomed rigorously. Thus one might use the time-average distortion without any verification of the approximation. In this paper, the convergence characteristics of the time-average distortions are theoretically investigated when the size of sample vectors or the size of codebook gets large. It has been revealed that if codebook size is large enough, then small sample set is enough to obtain the average distortion by approximatio of the calculated tiem-averaged distortion. Experimental results on synthetic data, which are supporting the analysis, are also provided and discussed.

• Journal of Industrial Technology
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• v.20 no.B
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• pp.195-200
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• 2000

This paper describes the path planning method in an unknown environment for an autonomous mobile robot equipped with CCD(Charge-Coupled Device) camera. The mobile robot moves along the guideline. The CCD camera is used for the detection of the existence of a guideline. The wavelet transform is used to find the edge of guideline. It is possible for us to do image processing more easily and rapidly by using wavelet transform. We make a fuzzy control rule using image data as an input then determined the position and the navigation of the mobile robot. The center value of guideline is the input of fuzzy logic controller and the steering angle of the mobile robot is the fuzzy controller output. Some actual experiments show that the mobile robot effectively moves to target position by means of the applied fuzzy control.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.5
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• pp.751-754
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• 2015

This paper deals with a robust speed control problem of a vertical one-link manipulator in the presence of parameter uncertainties and unknown input disturbance. Uncertain load weight causes an additional sinusoidal disturbance in the rotation of the link. In order to improve the robustness against parameter uncertainties and external input disturbances, this paper employs an internal model-based disturbance observer approach. Comparative computer simulations are performed to test the performance of the proposed controller. The simulation results show the enhanced performance of the proposed method.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.88.5-88
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• 2001

We propose a neural network solver for an inverse problem. The problem is that input data with complete teaching include defects and predict the defect value. The solver is constructed of a three layer neural network whose learning method is combined from BP and reconstruction learning. The input data for the defects are unknown; therefore, the circulation of an arithmetic progression replaces them; rightly, the learning procedure is not converged for the circulation data vut for the normal data. The learning is quitted after such a learning status id kept. Then, we search a minimum of the differences between teaching data and output of the circulation. Then, we search a minimum of the ...

• Journal of the Korean Institute of Telematics and Electronics
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• v.23 no.5
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• pp.625-630
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• 1986

In this paper, an adaptive observer based upon the exponentially weighted least-square method is implemented in the design of a model reference adaptive controller for an unknown time-invariant discrete single-input single-output linear plant. The adaptive observer estimates the padrameter vectors and initial state vector. The control input is determined so that the output of the plant converges to the output of the stable model reference.