• Journal of Advanced Marine Engineering and Technology
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• v.19 no.3
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• pp.77-83
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• 1995

Recently, in the field of industrial servo-systems, several methods have been proposed for tracking the reference input fastly and finely without overshoot. These methods, however, are established under hypothesis that structure and parameters of the plant are known accurately and they are time invariant. In practice, it is difficult to obtain the values of plant's parameters accurately and usually plants change with time and operation conditions. In this paper a method to construct the nominal model tracking adaptive control system is proposed. The system is composed of the nomial model which produces a ideal response and the model tracking system with the fuzzy adaptive controller. If the actual plant is equal to the controlled object in the nominal model, the output of the plant is the same as that of the nominal model and the fuzzy adaptive controller becomes idle. However, when the plant changes, the fuzzy adaptive controller of the tracking system operates in order for the output of the plant to track the ideal response. Through the computer simulations under various conditions, it is confirmed that the proposed model tracking system is very effective.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.2
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• pp.697-702
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• 2016

To control permanent magnet synchronous motors smoothly, it is important to know the exact parameter values of the stator resistance, various inductances, and the flux linkage of the permanent magnet. In practice, these parameters vary due to a variable operating point, temperature change, or a fault. This paper proposes a MRAS (Model Reference Adaptive System) based parameter estimator and adaptive control scheme. Owing to the non-linearity of the system equation with respect to these parameters, although many schemes proposed previously assumed that some parameters are known, all the parameters were assumed to be unknown. The simulation results revealed the effectiveness of the proposed algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1478-1481
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• 2005

As the model of most practical system cannot be obtained, the practice of typical control method is limited. Accordingly, numerous artificial intelligence control methods have been used widely. Fuzzy control and neural network control have been an important point in the developing process of the field. This paper is proposed adaptive fuzzy-neural network based on the vector controlled interior permanent magnet synchronous motor drive system. The fuzzy-neural network is first utilized for the speed control. A model reference adaptive scheme is then proposed in which the adaptation mechanism is executed using fuzzy-neural network. Also, this paper is proposed estimation of speed of interior permanent magnet synchronous motor using artificial neural network controller. The back-propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The error between the desired state variable and the actual one is back-propagated to adjust the rotor speed, so that the actual state variable will coincide with the desired one. The back-propagation mechanism is easy to derive and the estimated speed tracks precisely the actual motor speed. This paper is proposed the analysis results to verify the effectiveness of the new method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.23 no.7
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• pp.1667-1676
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• 1998

It is known that adaptive minimum mean square error (MMSE) receivers exhibit better performance than coventional single-user code division multiple access (CDMA) receivers in fixed (or static) channels and have the simple structure compared to other multi-user CDMA receivers. In practice, the radio channels are fading channels where the amplitude and phase of the received signal are time-varying and the sufficient signal-to-noise ratio is not always guaranteed. Thus, the performance of adaptive MMSE receivers is severely degraded in fading environments. We propose a new structure and adaptation algorithm for an adaptive MMSE receivers. Based on computer simulation results, it is observed that the proposed receiver demonstrates substantial performance improvement over currently available adaptive MMSE receivers in fading environments.

• International Journal of Control, Automation, and Systems
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• v.5 no.6
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• pp.621-629
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• 2007

In the adaptive fuzzy control field for affine nonlinear systems, there are two basic configurations: direct and indirect. It is well known that the direct configuration needs more restrictions on the control gain than the indirect configuration. In general, these restrictions are difficult to check in practice where mathematical models of plant are not available. In this paper, using a simple extension of the universal approximation theorem, we show that the only required constraint on the control gain is that its sign is known. The Lyapunov synthesis approach is used to guarantee the stability and convergence of the closed loop system. Finally, examples of an inverted pendulum and a magnet levitation system demonstrate the theoretical results.

• Journal of Digital Convergence
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• v.4 no.1
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• pp.43-54
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• 2006

New technologies have turned theories of education engineering into practice and also made education more efficient. One of them is the theory of 'Computer Adaptive Testing(CAT)'. This study is aimed to design and develop a web-based teaming system by making up for the weak points in the existing computer adaptive testing(CAT). The characteristics of the system are as follows: Firstly, tests can be controlled to fit the teachers' purposes by putting in different levels of both questions and samples. Secondly, this system does not test the same number of questions as students with different levels of ability do on a paper test, but a test can be taken according to their level. Thus this system is able to correctly judge learners' ability in a short time.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10b
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• pp.36-41
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• 1993

The adiabatic slirn-type autoclave reactor for free radical polymerization of LDPE is represented by a two-compartment four-cell model, which is proven effective to predict the reactor behavior as well as the polymer properties. Since the temperature distribution along the reactor axis plays the central role for the properties of the polymer product, it is important in practice to regulate the temperature in each compartment. The present study aims for the application of the adaptive control algorithm not only in the period of start-up but also during the steady state operation. It is shown that the temperature control is significantly improved over the conventional PID-control and this also brings about a reduction of variations in the polymer properties. This study demonstrates the potential application of the adaptive controller for the control of the polymerization reactor operated under the adiabatic condition.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.1
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• pp.72-81
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• 2009

This paper presents a robust adaptive control method using model reference control strategy against autonomous robot systems with random friction nature. We approximate a nonlinear robot system model by means of a feedback linearization approach to derive nominal control law. We construct a Least Square (LS) based observer to estimate friction dynamics online and then represent a perturbed system model with respect to approximation error between an actual friction and its estimation. Model reference based control design is achieved to implement an auxiliary control in order for reducing control error in practice due to system perturbation. Additionally, we conduct theoretical study to demonstrate stability of the perturbed system model through Lyapunov theory. Numerical simulation is carried out for evaluating the proposed control methodology and demonstrating its superiority by comparing it to a traditional nominal control method.

• Proceedings of the IEEK Conference
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• 2001.06a
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• pp.69-72
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• 2001

Blind equalization of transmission channel is important in communication areas and signal processing applications because it does not need training sequence, nor does it require a priori channel information. Recently, Tong et al. proposed solutions for this problem exploit the diversity induced by antenna array or time oversampling, leading to the second order statistics techniques, fur example, subspace method, prediction error method, and so on. The linear prediction error method is perhaps the most attractive in practice due to the insensitive to blind equalizer length mismatch as well as for its simple adaptive filter implementation. Unfortunately, the previous one-step prediction error method is known to be limited in arbitrary delay. In this paper, we induce the optimal delay, and propose the adaptive blind equalizer with multi-step linear prediction using RLS-type algorithm. Simulation results are presented to demonstrate the proposed algorithm and to compare it with existing algorithms.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.61 no.4
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• pp.161-167
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• 2012

Energy conversion systems such as power inverters and converters are basically significant in establishing photovoltaic power systems to enhance power effectiveness. This paper proposes a new converter control method by using the Lyapunov redesign approach. We construct the proposed control mechanism linearly composed of nominal control and auxiliary control laws. The former is generally designed through a well-known power electronic technology and the latter is implemented to compensate real-time control error due to uncertain natures of converter systems in practice. For realizing adaptive control capability in the proposed control mechanism, a control parameter vector is estimated by utilizing a steepest descent based optimization method. We carry out numerical simulation with Matlab(c) software to demonstrate reliability of the proposed converter control system and conduct a comparative study to prove its superiority by comparing with a generic converter control methodology.