• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.5
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• pp.46-50
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• 1997

In fuzzy logic control, static fuzzy rules cannot cope with significant changes of parameters of plants or environment. To solve this prohlem, self-organizing fuzzy control. neural-network-hased fuzzy logic control and so on have heen introduced so far. However, dynamically changed fuzzy rules of these schemes may make a fuzzy logic controller Fall into dangerous situations because the changed fuzzy rules may he incomplete or inconsistent. This paper proposes a new adaptive filzzy logic control scheme using a predictivc neural network. Although some parameters of a controlled plant or environment are changed, proposed fuzzy logic controller changes its decision outputs adaptively and robustly using unchanged initial fuzzy rules and the predictive errors generated hy the predictive neural network by on-line learning. Experimental results with a D<' servo-motor position control problem show that propnsed cnntrol scheme is very useful in the viewpoint of adaptability.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.7
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• pp.1360-1365
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• 2011

Electro-hydraulic servo motor is used to a lot of in the field of industrial equipment which requires one of the control functions among pressure, flow, and power output. In this paper, linear discrete reference model of the electro-hydraulic servo motor system are made for 1-step ahead predictive control. The parameters of electro-hydraulic servo motor system are estimated using the recursive least square method. 1-step ahead predictive model output of electro-hydraulic servo motor system corresponded to reference model output in spite of estimated parameters are not meet real parameters. Control performance affections are studied due to the forgetting factors variation.

• Proceedings of the KIEE Conference
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• 1989.11a
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• pp.376-379
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• 1989

In this paper a direct adaptive control scheme which minimizes N-step cost function is proposed. It is derived via GPC control algorithm under the assumption that we know N-step unit step response of the given system. The proposed adaptive scheme is shown to have the same stability properties with the RHTC control, and require very small amount of calculation compared with other adaptive algorithms which minimize N-step cost function. And it is shown through simulations that the proposed adaptive algorithm is robust to the variation of the unit step response which is assumed to be known.

• Journal of Electrical Engineering and Technology
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• v.12 no.2
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• pp.926-936
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• 2017

The nonlinear behavior and the high performance requirement are the main problems that appear in the design of manipulator robots and their controllers. For that reason, the simulation, real-time execution and comparison of the performance of controllers applied to a robot with three degrees of freedom are presented. Five controllers are prepared to test the robot's dynamic model: predictive; hyperbolic sine-cosine; sliding mode; hybrid composed of a predictive + hyperbolic sine-cosine controller; and adaptive controller. A redundant robot, a communication and signal conditioning interface, and a simulator are developed by means of the MatLab/Simulink software, which allows analyzing the dynamic performance of the robot and of the designed controllers. The manipulator robot is made to follow a test trajectory which, thanks to the proposed controllers, it can do. The results of the performance of this manipulator and of its controllers, for each of the three joints, are compared by means of RMS indices, considering joint errors according to the imposed trajectory and to the controller used.

• Proceedings of the KIPE Conference
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• 1996.06a
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• pp.17-20
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• 1996

An adaptive current control scheme of a permanent magnet (PM) synchronous motor with the simultaneous estimation of the magnitude of the flux linkage and stator resistance is proposed. The adaptive parameter estimation is achieved by a model reference adaptive system (MRAS) technique. The adaptive laws are derived by the Popov's hyperstability theory and the positivity concept. The predictive control scheme is employed for the current controller with the estimated parameters. The robustness of the proposed current control scheme is compared with the conventional one through the computer simulations.

• Journal of Institute of Control, Robotics and Systems
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• v.2 no.1
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• pp.60-66
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• 1996

Model Based Predictive Control(MBPC) has been widely used in predictive control since 80's. GPC[1] which is the superset of many MBPC strategies a popular method, but GPC has some weakness, such as insufficient stability analysis, non-applicability to internally unstable systems. However, CRHPC[2] proposed in 1991 overcomes the above limitations. So we chose RHPC based on CRHPC for electric furnace control. An electric furnace which has nonlinear properties and large time delay is difficult to control by linear controller because it needs nearly perfect modelling and optimal gain in case of PID. As a result, those controls are very time-consuming. In this paper, we applied RHPC with equality constraint to electric furnace. The reults of experiments also include the case of RHPC with monotonic weighting improving the transient response and including unmodelled dynamics. So, This paper proved the practical aspect of RHPC for real processes.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.344-349
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• 1990

Under the assumption that process input/output data are sufficiently rich to allow reasonable plant identification, a long-range predictive control method for SISO bilinear plant is derived. In order to ensure offset-free behaviour of the control method, a new bilinear CARIMA model with variable dead-time is introduced. Furthermore, to extend the maximum output prediction horizon, the future predicted outputs in the bilinear term are assumed to be equal to the known future set-points. With a classical recursive adaptation algorithm, the proposed control scheme is capable of stable control of bilinear plants with variable parameters, with variable dead-time, and with a model order which changes instantaneously. Several simulation results demonstrate the characteristics of the proposed bilinear model predictive control method.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1915-1919
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• 2004

In this paper, we propose a new discrete-time predictive current controller for a PMSM(Permanent Magnet Synchronous Motor). The main objectives of the current controllers are to ensure that the measured stator currents tract the command values accurately and to shorten the transient interval as much as possible, in order to obtain high-performance of ac drive system. The conventional predictive current controller is hard to implement in full digital current controller since a finite calculation time causes a delay between the current sensing time and the time that it takes to apply the voltage to motor. A new control strategy in this paper is seen the scheme that gets the fast adaptation of transient current change, the fast transient response tracking and is proposed simplified calculation. Moreover, the validity of the proposed method is demonstrated by numerical simulations and the simulation results will be verified the improvements of predictive controller and accuracy of the current controller.

• Journal of the Korean Crystal Growth and Crystal Technology
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• v.13 no.6
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• pp.284-289
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• 2003

The electric furnace, inside which the desired temperature is kept by the generated heat, is known to be a difficult system to control and model exactly because system parameters and response delayed time are varied as the temperature and positions are changed. In this study, the GPCEW (generalized predictive control with exponential weight), which always guarantees the stability of the closed loop system and can be effectively applied to the internally unstable system, was introduced to the ceramic drying electric furnace and was verified by showing its temperature tracking performance experimentally.

• Proceedings of the KOSOMBE Conference
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• v.1992 no.05
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• pp.79-81
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• 1992

This paper describes the design of an adaptive closed circuit anesthesia controller based on a multiplexed mass spectrometer system. The controller deals with measurement deterioration caused by measurement delay and rise time through a tong catheter as well as long sampling times due to the multiplexed measurements. Measurement data is extrapolated between sampling periods to increase the estimation convergence rate. A multiple-step-ahead predictive control algorithm is used to calculate intermediatc control inputs between sampling intervals. Simulations are used to validate the designed controller.