• Proceedings of the KIEE Conference
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• 2002.11c
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• pp.79-82
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• 2002

In this paper, we propose an adaptive repetitive control algorithm for the system the task of which is repetitive. The feedforward controller in the repetitive control system is modified by using the system parameter identifier in order to improve the convergence characteristics. The proposed algorithm is applied to the tracking control of a linear BLDC motor to which a periodic reference input is applied. It is illustrated by simulation results that the proposed adaptive repetitive control method yields better control performance than existing repetitive control even when modeling errors exist.

• Smart Structures and Systems
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• v.2 no.4
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• pp.313-320
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• 2006

The reliability analysis of structures subjected to stochastic loading involves evaluation of time and probability of the system's residence in a reference domain. In this paper, we derive an asymptotic estimate of exit time for multi-degrees-of-freedom structural systems. The system's dynamics is governed by the Lagrangian equations with linear dissipation and fast additive noise. The logarithmic asymptotic of exit time is found explicitly as a sum of two terms dependent on kinetic and potential energy of the system, respectively. As an example, we estimate exit time and an associated structural performance for a rocking structure.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.05a
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• pp.144-147
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• 2001

This paper presents adaptive fuzzy controller which is uncertainty or unknown variation in different parameters with nonlinear system of helicopter. The proposed adaptive fuzzy controller applied TSK(Takagi-Sugeno-Kang) fuzzy system which is not only low number of fuzzy rule, and a linear input-output equation with a constant term, but also can represent a large class of nonlinear system with good accuracy. The adaptive law was designed by using Lyapunov stability theory. The adaptive fuzzy controller is a model reference adaptive controller which can adjust the parameter $\theta$ so that the plant output tracks the reference model output. First of all, system of helicopter was considered as stopping state, and design of controller was simulated from dynamics equation with stopping state. Results show that it is controlled more successfully with a model reference adaptive controller than with a non-adaptive fuzzy controller when there is a modelling error between system and model or a continuous added noise in such unstable system.

• Proceedings of the KIEE Conference
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• 1990.07a
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• pp.51-54
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• 1990

A parametrization for a linear system is presented to design a direct model reference adaptive pole placement controler. This parametrized model is one of the structured nonminimal models. The exponentially weighted least-squres algorithm is employed to estimate the control parameters. The direct adaptive controller has the exponential weighting properties by the proposed method of selecting the characteristic polynomials of the sensitivity function filters in connection with the reference models.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.4
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• pp.519-525
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• 2014

In this paper, an alternative variable structure controller is designed for the point-to-point regulation control of uncertain general linear plants so that the output of plants can be controlled from an arbitrarily given initial point to an arbitrarily given reference point in the state space. By using the error between the steady state value of the output and an arbitrarily given reference point and those integral, a transformed integral sliding surface is defined, in advance, as the surface from an initial state to an arbitrarily given reference point without the reaching phase problems. A corresponding control input is suggested to satisfy the existence condition of the sliding mode on the preselected transformed integral sliding surface against matched uncertainties and disturbances. Therefore, the output controlled by the proposed controller is completely robust and identical to that of the preselected transformed integral sliding surface. Through an example, the effectiveness of the suggested controller is verified.

• Journal of Power Electronics
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• v.15 no.5
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• pp.1274-1285
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• 2015

This paper deals with model reference adaptive system (MRAS) speed estimators based on a secondary flux for linear induction motors (LIMs). The operation of these estimators significantly depends on an adaptation mechanism. Fixed-gain PI controller is the most common adaptation mechanism that may fail to estimate the speed correctly in different conditions, such as variation in machine parameters and noisy environment. Two adaptation mechanisms are proposed to improve LIM drive system performance, particularly at very low speed. The first adaptation mechanism is based on fuzzy theory, and the second is obtained from an LIM mechanical model. Compared with a conventional PI controller, the proposed adaptation mechanisms have low sensitivity to both variations of machine parameters and noise. The optimum parameters of adaptation mechanisms are tuned using an offline method through chaotic optimization algorithm (COA) because no design criterion is given to provide these values. The efficiency of MRAS speed estimator is validated by both numerical simulation and real-time hardware-in-the-loop (HIL) implementations. Results indicate that the proposed adaptation mechanisms improve performance of MRAS speed estimator.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.758-762
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• 1994

Complicated dynamical behavior can occur in model reference adaptive control systems when two external sinusoidal signals are introduced although the plant and reference model are stable linear first older systems. The phase portrait plot and the power spectral analysis indicate chaotic behavior. In the system treated, a positive Lyapuniov exponent and non-integer dimension clearly manifest chaotic nature of the system.

• Proceedings of the KIEE Conference
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• 1988.07a
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• pp.895-897
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• 1988

This paper is concerned with linear system containing time delay in model reference adaptive control. An algorithm is developed using Lyapunov second method and properties of strictly positive real function.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.135-139
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• 1992

Control of tool-environment interaction force to comply the robot system to an environment is of vital in many automated process. This paper presents the implementation of an adaptive force control with commercial robot system in two dimensional contour following task. A model reference adaptive control system, combined with the linear compensators, is implemented. That is, a use of adaptive control is to provide an auxiliary control system so that the contour following performance can be improved from that of using linear control system only. Hyperstability is used to derive the adaptive control law. Experimental verification of the proposed control system is obtained using PUMA 560 robot system. Data obtained experimentally shows that the use of additional adaptive control system improves the contour following performance about 30% in RMS contact force errors upon that of the system controlled by the linear compensators only.

• Journal of Electrical Engineering and Technology
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• v.12 no.6
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• pp.2399-2410
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• 2017

In this paper, a linear matrix inequality-based sampled-data fuzzy observer design method is proposed based on the exact discretization approach. In the proposed design technique, a numerically relaxed observer design condition is obtained by using the discrete-time fuzzy Lyapunov function. Unlike the existing studies, the designed observer is robust to the uncertain premise variable because the fuzzy observer is designed under the imperfect premise matching condition, in which the membership functions of the system and observer are mismatched. In addition, we apply the proposed method to the state estimation problem of the attitude and heading reference system (AHRS). To do this, we derive a Takagi-Sugeno fuzzy model for the AHRS system, and validate the proposed method through the hardware experiment.