• Journal of Institute of Control, Robotics and Systems
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• v.7 no.2
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• pp.125-130
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• 2001

This paper presents an indirect field-oriented (IFO) induction motor position servo drives which uses the model following adaptive controller with the artificial neural network(ANN)-based rotor resistance estimator. The model reference adaptive system(MRAS)-based 2-layer ANN estimates the rotor resistance on-line and a linear model-following position controller is designed by using the estimated the rotor resistance value. At the end, a fuzzy logic system(FLS) is added to make the position controller robust to the external disturbances and the parameter variations. The simulation results show the effectiveness of the proposed method.

• Journal of Korean Port Research
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• v.14 no.3
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• pp.291-301
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• 2000

T his paper presents the lateral and longitudinal control algorithm for the driving of a 4WS AGV(Automated Guided Vehicle). The control law to the lateral and longitudinal control of the AGV includes adaptive agin tuning ability, that is the controller gain of the gravity compensated PD controller can be changed on a real-time. The gain tuning law is derived from the Lyapunov direct method using the output error of the reference model and the actual model, And to show the performance of the presented lateral and longitudinal control algorithm, we simulate toe nonlinear AGV equations of the motion by deriving the Newton-Euler Method, The read path is from quay yard area to docking position in loading yard area. The quay yard area is where the quay crane loads the container to the AGV and the docking position is where the container is transferred to the gantry crane. The road types are constructed in a straight line and J-turn. When driving the straight line, the driving velocity is 6㎧ and the J-turn is 3㎧.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.460-463
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• 1995

The present paper studies a robot manipulator's contact tasks on the uncertain flexible objects. The flexible object's distributed parameter model is approximated into a lumped "position state-varying" model. By using the well-known nonlinear feedback compensation, the robot's control space is decomposed into the position control subspace and the object's torque control subspace. The optimal state feedback is designed for the position loop, and the robot's contact force is controlled through controlling the resultant torque on the object using model-reference simple adaptive control. Experiments of a PUMA robot interacting with an aluminum plate show the effectiveness of this control approach. approach.

• 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.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.21 no.7
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• pp.1030-1041
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• 1997

The control model in the tandem cold rolling mill consists of many mathematical theories and is used to calculate the reference values such as the roll gap and the rolling speed for good operation of rolling mill. But, the control model used presently has a problem causing inaccurate prediction of the rolling force. By the parameter identification, it was found that the main factor causing inaccurate prediction of the rolling force was incorrect modeling of the friction coefficient and the flow stress. To get rid of the erroneous factor new adaptive schemes are suggested in this work. Those are a long-time adaptation by the iterative least-square method and a short-time adaptation by the recursive weighted least-square method respectively. The new equations for the friction coefficient and the flow stress are derived by applying the suggested adaptive algorithms. Through the on-line test in an actual mill, it is proved that the rolling force predicted by the new equations is more accurate than the one by the existing equations ever used.

• 제어로봇시스템학회:학술대회논문집
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• 1987.10a
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• pp.717-720
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• 1987

This paper presents a discrete-time model reference adaptive control technique for periodically time-varying plants. It is shown that the identification problem for periodic parameters can be reduced to that of constant unknown parameters case. The global stability of the resulting closed-loop system is established using the key technical lemma of Goodwin, Ramadge and Caines.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.177-182
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• 1989

In this paper a new adaptive control algorithm is derived, with the unknown manipulator and payload parameters being estimated online. In practice, we may simplify the algorithm by not explicity estimating all unknown parameters. Further, the controller must be robust to residual time-varying disturbance, such as striction or torque ripple. Also, the reference model is a simple douple integrator and the acceleration input for robot manipulator consists of a proportion and derivative controller for trajectory tracking purposes. The validity of this control is confirmed in simulation where two-link robot manipulator shows the robust performances in spite of the existing nonlinear interaction and unknown parametrictings

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.780_781
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• 2009

This paper proposes efficiency optimization control of IPMSM drive using hybrid intelligent controller(HIC). The design of the speed controller based on fuzzy-neural network that is implemented using fuzzy control and neural network. The design of the current based on adaptive fuzzy control using model reference and the estimation of the speed based on neural network using ANN controller. In order to maximize the efficiency in such applications, this paper proposes the optimal control method of the armature current. The optimal current can be decided according to the operating speed and the load conditions. This paper proposes speed control of IPMSM using ALM-FNN, current control of model reference adaptive fuzzy control(MTC) and estimation of speed using ANN controller. The proposed control algorithm is applied to IPMSM drive system controlled HIC, the operating characteristics controlled by efficiency optimization control are examined in detail.

• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.3
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• pp.65-71
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• 1997

With the emergence of neural network, there is a revived interest in identification of nonlinear systems. So in this paper, to identify unknown nonlinear systems dynamically we propose DPNN(Dynamic Polynomial Neural Network) using GMDH (Group Method of Data Handling) algorithm. The dynamic system identification using GMDH consists of applying a set of inputloutput data to train the network by dynamically computing the necessary coeffici1:nt sets. Then, MRAC(Mode1 Reference Adaptive Control) is designed to control nonlinear systems using DPNN. In the result, we can see that the modelling and control using DPNN work well by computer simulation.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.12
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• pp.535-541
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• 2002

This paper proposes an adaptive fuzzy controller based fuzzy logic control for high performance of permanent magnet synchronous motor(PMSM) drive. In the proposed system, fuzzy control is sued to implement the direct controller as well as the adaptation mechanism. The adaptation mechanism is executed by fuzzy logic based on the error and change of error measured between the motor speed and output of a reference model. The control performance of adaptive fuzzy controller is evaluated by simulation for various operating conditions. The validity of the proposed controller is confirmed by performance results for PMSM drive system.