• International Journal of Control, Automation, and Systems
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• v.4 no.5
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• pp.545-558
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• 2006

In this paper, PD-like visual servoing is modified in two ways: a sliding-mode observer is applied to estimate the joint velocities, and a RBF neural network is used to compensate the unknown gravity and friction. Based on Lyapunov method and input--to-state stability theory, we prove that PD-like visual servoing with the sliding mode observer and the neuro compensator is robust stable when the gain of the PD controller is bigger than the upper bounds of the uncertainties. Several simulations are presented to support the theory results.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.10
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• pp.53-58
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• 1992

This paper presents a decentralized model reference adaptive control scheme for an interconnected linear system composed of a number of single-input single-output subsystems in which outgoing interactions pass through the measurement channel and are subjected to bounded external disturbances. The scheme can treat the unknown strength of interactions as well as uncertainties in subsystem dynamics, and allows for the case when the relative degree of each decoulped subsystem does not exceed two.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.39 no.5
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• pp.519-526
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• 1990

In this paper we propose the resolved motion controller using a neural network for a robot manipulator. Neural identifier designed by a neural network is trained by using a feedback force as an error signal. The identifier approximates the output of a unknown nonlinear system by monitoring both the input and the output of this system. If the neural network is sufficiently trained well, it does not require either strict modelling of the manipulator or precise parameter estimation. The effectiveness of the proposed controller is demonstrated by computer simulation using a two-link planar robot.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2200-2202
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• 2003

In this paper, differentiation operational matrix for Haar function is derived. Proposed method only using a matrix calculation of Haar discrete matrix and block-pulse function's integration operational matrix. It would be possible to use to design an a1gebraic estimator for fault detection or unknown input observer effectively.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.382-382
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• 2000

The external force disturbance is the one of the main causes that deteriorate the performance of the magnetic suspension. Thus, this paper develops a fuzzy estimator for gain scheduling control of magnetic suspension systems suffering from the unknown disturbance. The proposed fuzzy estimator computes the disturbance injected to the plant and the gain scheduled controller generates the corresponding stabilizing control input associated with the estimated disturbance. In the simulation results we confirm the novelty of the proposed control scheme comparing with the other method using a feedback linearization.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.11
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• pp.127-139
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• 1996

In this paper we propose a new controller design method using hybrid fuzzy-neural netowrk and neural network identification in order ot control systems which are more and more getting nonlinearity. Proposed method performs, for a nonlinear plant with unknown functions, hybird identification using a fuzzy-neural network and a neural network, and then a stable nonlinear controller is designed with those identified informations. To identify a nonlinear function, which is directly related to input signals, we can use a neural network which is satisfied with the proposed stable condition. To identify a nonlinear function, which is not directly related to input signals, we can use a fuzzy-neural network which has excellent identification characteristics. In order to verify excellent control performances of the proposed method, we compare the porposed control method with a conventional neural network control method through simulations and experiments with one link manipulator.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.12
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• pp.1520-1526
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• 1999

This paper presents a current feedback learning controller for dynamic control of DC motors. The proposed controller uses the full third-order dynamics model of DC motor system to drive stable learning rules for virtual current learning input, voltage learning input, and the coefficient of electromotive force. It is shown that the proposed learning controller drives the state of uncertain DC motor system with unknown system parameters and external load torque to the desired one globally asymptotically. Computer simulation and experimental results are given to demonstrate the effectiveness of the proposed adaptive learning controller.

• Korean Management Science Review
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• v.15 no.2
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• pp.125-142
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• 1998

The aggregation on linear large-scale dynamic systems is examined in this paper and a "two-step" approach is proposed. In this procedure, the aggregated system consists of two subsystems. The first subsystem represents aggregation through the retainment of dominant eigenvalues of the original system, leading to a first approximation of the desired output of the original system. The purpose of augmenting it with a second subsystem is to provide an estimation of the error on the first approximation, thus permitting a second correction to the output approximation and resulting in an output approximation of greater accuracy. Optimization techniques are discussed for the determination of unknown parameters in the aggregated system. These techniques use minimization principles of certain suitable performance indices and are developed for both single input-single output and multiple input-multiple output system. Numerical examples illustrating these procedures are given and the results are compared with those obtained using existing methods. Finally, a pharmacokinetics problem is studied from the aggregation point of view.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.37 no.2
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• pp.1-7
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• 2000

In this paper, an adaptive control law for nonlinear systems represented by input-output models are proposed under the assumption that unknown system parameters are in a known compact and convex set. Contrary to the previous results, the compact and convex set is not restricted to a ball whose center is at the origin or convex hypercube. It is proven that the proposed parameter update rule produces a sequence of parameters which reside in the set and guarantees that the position, velocity, and acceleration error converges to zero as time goes to infinity. This theoretical result was justified through simulations.

• ETRI Journal
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• v.17 no.2
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• pp.11-19
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• 1995

The plasma densities generated from a semiconductor bridge (SCB) device employing a capacitor discharge firing set have been measured by a novel diagnostic technique employing a microwave resonator probe. The spatial resolution of the probe is comparable to the separation between the two wires of the transmission lines (${\approx}$3 mm). This method is superior to Langmuir probes in this application because Langmuir probe measurements are affected by sheath effects, small bridge area, and unknown fraction of multiple ions. Measured electron densities are related to the land material and input energy. Although electron densities in the plasma generated by aluminum or tungsten-land SCB devices show a general tendency to increase steadily with power, at the higher energies, the electron densities generated from tungsten-land SCB devices are found to remain constant.