• The Transactions of the Korean Institute of Electrical Engineers
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• v.32 no.11
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• pp.394-403
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• 1983

This paper discusses a model reference adaptive control for a multi-input multi-output continuos system in matrix fraction description. The controller is of Monopoli-Narendra type with a time-varying gain matrix in the parameter adaptation law. The transfer matrix of the given plant with an adjustable controller is made to approach to that of the reference model asymptotically. It is shown that, under some plausible assumptions such as on the knowlidge of an interactor matrix, the algorithm for a single-input single-output system can be appropriately extended to a multi-input multi-output system. The convergence of an adaptation law is estavlished with some stability theory and stability of the overall system is asserted by an analytical investigation.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.6
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• pp.747-752
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• 2006

In this paper, the adaptive fuzzy sliding mode controller with two systems is designed. The existing sliding mode controller used to $approximation{\^{u}}(t)$ with discrete sgn function and sat function for keeping the state trajectories on the sliding surface[1]. The proposed controller decrease the disturbance for uncertain control gain and This paper is concerned with an Adaptive Fuzzy Sliding Mode Control(AFSMC) that the fuzzy systems ate used to approximate the unknown functions of nonlinear system. In the adaptive fuzzy system, we adopt the adaptive law to approximate the dynamics of the nonlinear plant and to adjust the parameters of AFSMC. The stability of the suggested control system is proved via Lyapunov stability theorem, and convergence and robustness properties ate demonstrated. Futhermore, fuzzy tuning improve tracking abilities by changing some sliding conditions. In the traditional sliding mode control, ${\eta}$ is a positive constant. The increase of ${\eta}$ has led to a significant decrease in the rise time. However, this has resulted in higher overshoot. Therefore the proposed ${\eta}$ tuning AFSMC improve the performances, so that the controller can track the trajectories faster and more exactly than ordinary controller. The simulation results demonstrate that the performance is improved and the system also exhibits stability.

• Journal of the Institute of Convergence Signal Processing
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• v.14 no.2
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• pp.95-98
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• 2013

The LMS(Least Mean Square) algorithm, one of the most famous, is generally used because of tenacity and high mating spots and simplicity of realization, But it has trade-off between nonuniform collection and EMSE(Excess mean square error). To overcome this weakness, a variable step size is used widely, but it needs a lot of calculation loads. In this paper, we suggest changed algorithm in case of environment changes of cars and reduce amount of calculation as it uses original signal and noise signal of IGC(Instantaneous Gain Control) algorithm. In this paper, logarithmic function is removed because of real-time processing IGC. The performance of proposed algorithm is tested to adaptive noise canceller in automobile.

• Journal of Welding and Joining
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• v.8 no.1
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• pp.23-30
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• 1990

The robotic welding has been adapted positively in many welding shops forthe purpose of improving the welding efficiency and liberating operators from the severe working atmosphere. But for a large-size structure with thick plates like ship-building and every kind of plants manufacturing, the application of the arc welding robots is not established yet. The reason is assumed that the conventional arc welding robots are not adaptive for multi-pass welding of thick plates whose grooves are not so accurate. As one solution to this problem, a guidance system which uses the welding arc itself as a sensor is largely used. In this study the velocity controller which changes the tip to workpiece distance for regulating the weld proposed. The proportional and integral gain of velocity controller were determined by using the computer simulation of the control system, and the simulation results compared with the experimental ones. It was revealed that the developed control system using the arc sensor principle has a good capability of tracking the weld joint, although some more studies will be needed to refine the model of arc current.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.21 no.1
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• pp.53-60
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• 2010

This paper presents a novel gain control system composed of a feedback circuit, Two stage Low Noise Amplifier (LNA) using 0.18 um CMOS technology for 5.2 GHz. The feedback circuit consists of the seven function blocks: peak detector, comparator, ADC, IVE(Initial Voltage Elimination) circuit, switch, storage, and current controller. We focus on detecting signal and designing storage circuit that store the previous state. The power consumption of the feedback circuit in the system can be reduced without sacrificing the gain by inserting the storage circuit. The adaptive front-end system with the feedback circuit exhibits 11.39~22.74 dB gain, and has excellent noise performance at high gain mode. Variable gain LNA consumes 5.68~6.75 mW from a 1.8 V supply voltage.

• International Journal of Control, Automation, and Systems
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• v.6 no.3
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• pp.453-459
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• 2008

The neural network is currently being used throughout numerous control system fields. However, it is not easy to obtain an input-output pattern when the neural network is used for the system of a single feedback controller and it is difficult to obtain satisfactory performance with when the load changes rapidly or disturbance is applied. To resolve these problems, this paper proposes a new mode to implement a neural network controller by installing a real object for control and an algorithm for this, which can replace the existing method of implementing a neural network controller by utilizing activation function at the output node. The real plant object for controlling of this mode implements a simple neural network controller replacing the activation function and provides the error back propagation path to calculate the error at the output node. As the controller is designed using a simple structure neural network, the input-output pattern problem is solved naturally and real-time learning becomes possible through the general error back propagation algorithm. The new algorithm applied neural network controller gives excellent performance for initial and tracking response and shows a robust performance for rapid load change and disturbance, in which the permissible error surpasses the range border. The effect of the proposed control algorithm was verified in a test that controlled the speed of a motor equipped with a high speed computing capable DSP on which the proposed algorithm was loaded.

• Journal of the Korean Institute of Telematics and Electronics
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• v.19 no.2
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• pp.31-37
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• 1982

A practical and robust control scheme is suggested for MIMO disciete time processes with real simple poles. This type of control scheme, having the advantages of both the adaptiveness and optimality, maybe successfully applicable to structured dynamic controllers for plants whose paiameters are slowly timevaiying. The identiflcation of the process paiameters is undertaken in ARMA form and the optimization of the feedback gain matrix is performed in the state space representation with respect to a standard quadratic criterion.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.20 no.7
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• pp.65-73
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• 2006

This paper presents speed control of IPMSM drive using neural network(NN) PI controller. In general, PI controller in computer numerically controlled machine process fixed gain. They may perform well under some operating conditions, but not all. To increase the robustness of fixed gain PI controller, NNPI controller proposes a new method based neural network. NNPI controller is developed to minimize overshoot rise time and settling time following sudden parameter changes such as speed, load torque and inertia. Also, this paper is proposed speed control of IPMSM using neural network and estimation of speed using artificial neural network(ANN) controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The results on a speed controller of IPMSM are presented to show the effectiveness of the proposed gain tuner. And this controller is better than the fixed gains one in terms of robustness, even under great variations of operating conditions and load disturbance.

• The Journal of the Acoustical Society of Korea
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• v.16 no.5
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• pp.18-25
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• 1997

The feedforward control algorithm for active noise control exhibits high stability and performance robustness. But it has a slow convergence speed and requires a correlated reference signal. Broadband active control systems typically use feedback control in order to increase the convergence speed and to avoid the problems associated with obtaining and decoupling a suitable reference signal. However, it is well known that conventional feedback control systems have a gain-bandwidth limitation and stability problem. This paper presents the new system based on the combination of both feedforward and feedback system in order to increase the convergence speed. The proposed system uses a proposed control algorithm termed "optional-summed" algorithm in which the "optional summed reference signal" comprised of weighted sum of an original reference signal and a eror signal, is used as an input to an adaptive system. Thus, the proposed system can have faster convergence speed and better performance than either feedforward or feedback system using the Filtered-x LMS algorithm as almost equivalent complexity of computation as it. Several simulation results demonstrating the good properties of the proposed adaptive system as well as verifying the analytical results are also presented in the paper.

• The Journal of the Acoustical Society of Korea
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• v.24 no.5
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• pp.238-247
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• 2005

In this Paper. we Presents subband design techniques of a compensating filter and adaptive feedback canceller for the digital hearing aid. The sensorineural hearing loss has a hearing threshold that shows a nonlinear characteristic in frequency domain. and its compensation suffers from an echo that produced by an undesired time varying feedback path. Therefore. the digital hearing aid requires the compensator that can adjust gains nonlinearly in frequency bands and eliminate the echo rapidly In the Proposed digital hearing aid. the compensating filter is designed by the adaptive system identification method in subband structure, and the adaptive feedback canceller is designed by the subband affine projection algorithm. The designed compensation filter can control the nonlinear gain in each subband respectively, therefore precise compensation is possible. And the feedback canceller using the subband adaptive filter achieves fast convergence rate. The Performances of the Proposed method are verified by computer simulations as comparing with the behaviors of the previous trials.