• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.514-518
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• 2005

In this paper an adaptive fuzzy PI controller with feedback linearizing meth od is implemented to controlling flux and torque separately in induction motor. In this paper first decoupling of torque and flux which are outputs to be controlled, is achieved by using feedback linearization methodology. Then for reducing the effect of noise and rejection of disturbance, main part of controller which is adaptive PI fuzzy controller, is designed. Coefficients of PI controller are determined by defined fuzzy rules due to error dynamic. Inputs of fuzzy system are defined sliding surfaces which consist of torque and flux errors. The main contribution of this paper is effect reduction of noise and disturbance on torque and flux which is based on fuzzy logic and nonlinear control. At last the effectiveness of the proposed control scheme in presence of noise and load disturbance is simulated and comprised to applying sliding method. The results verify better effectiveness of the proposed method for effect reduction of noise and disturbance.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2008.04a
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• pp.202-205
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• 2008

This paper presents an optimization of permanent magnet (PM) in a brushless dc (BLDC) motor using the response surface methodology (RSM). Size and angle of the PM are optimized to minimize the cogging torque, while reducing the magnitude of harmonic at a dominant frequency and maintaining the operating torque. A fitted RS model is constructed by verifying the high reliability of the total variation and the variation of estimated error. The optimized design is validated by carrying out the reanalysis and comparing to the initial model using the nonlinear transient finite element analysis.

• Journal of international Conference on Electrical Machines and Systems
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• v.2 no.1
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• pp.69-76
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• 2013

This paper is concerned with speed tracking control problem for permanent-magnet synchronous drives (PMSM) in the presence of an variable load torque and unknown model parameters. The disturbance of speed control caused by inaccuracy of model parameters has been investigated. A load torque observer has been proposed to observe the load torque and estimate the disturbance caused by inaccuracy of model parameters. Both inertia and friction coefficient are identified in gradient descent approach. The stability condition of the observer has also been studied. Furthermore an improved feed-forward control has been introduced to reduce the speed track error. The proposed control strategy has been verified by both simulation and experimental results.

• Transactions of Materials Processing
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• v.8 no.6
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• pp.583-590
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• 1999

New forward slip model has been developed for the precise prediction of rolling speed in the hot strip finishing mill. Besides those influential factors such as neutral point, work roll diameter, friction coefficient, bite angle and the thickness at each side of entry and delivery of the rolls, roll torque was specifically taken into account in this study. To consider the effect of width change on forward slip, calibration factors obtained from rolling torque has been added to new prediction model and refining method has also been developed to reduce the speed unbalance between adjacent stands. The application of the new model showed a good agreement in rolling speeds between the predictions and the actual measurements, and the standard deviation of prediction error has also been significantly reduced.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.8
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• pp.1217-1223
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• 2015

In this paper, we study the problem of torque ripple minimization in Brushless DC Motors (BLDC) and proposes a disturbance observer (DOB) based controller in order to efficiently reduce the torque ripple. In the DOB based control system, an equivalent disturbance (plant disturbance and effect of modelling error) is cancelled by its estimate. When the DOB controller is applied to BLDC motors, the effect of inverter switching is considered as an equivalent disturbance and to be cancelled by the DOB controller. Through computer simulations, it is shown that the performance of the proposed DOB controller is superior to that of the conventional PI controller. In the case where the numerical values of resistance and inductance are not known exactly, it is shown that the proposed DOB controller achieves better performance than the PI controller.

• The Transactions of the Korean Institute of Power Electronics
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• v.21 no.1
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• pp.65-71
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• 2016

This study investigates a high-accuracy alternate QD model to estimate the characteristics of induction motor under light loads. To demonstrate the usefulness of the alternate QD model, a maximum torque per amp (MTPA) control based on the alternate model is shown to outperform MTPA control based on the standard QD model. The experimental study conducted in this work exhibits that the MTPA control based on the alternate QD model tracks torque commands between 20 Nm and 30 Nm with 5% error, whereas the MTPA control based on the standard QD model generates torques lower by over 23% compared with the aforementioned torque commands. This result indicates that the alternate QD model is a highly accurate model for induction motors under light loads.

• Proceedings of the KIEE Conference
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• 2000.07b
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• pp.978-980
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• 2000

Sensorless PMSM is much studied for the industrial applications and home appliances because, a mechanical sensor reduces reliability and increases cost. Two types of instantaneous torque controls are basically used for high performance variable-speed a.c. drive : vector control and direct torque control. This paper investigates speed sensorless control of PMSM using direct torque control. The switching of inverter is determined from SVPWM realizing the command voltage which is obtained by flux error and measured current without d-q transformation. The rotor speed is estimated through adaptive observer with feedback loop. The simulation and experimental results indicate good performances.

• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.530-532
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• 1998

In this paper, We Designed the torque compensatory controller for robot manipulator using modified chaotic neural networks with self feedback loop. The proposed torque compensatory controller compensate torque of the PD controller. In order to estimate the proposed controller, we implemented to the Cartesian space control of three-axis PUMA robot and compared the simulation results with recurrent neural networks(RNNs) controller. Simulation results show that the learning error drastically decrease at on-line learning. The proposed CNNs controller shows much better control performance and shorter processing time compared to the recurrent neural network controller in the robot trajectory control.

• Proceedings of the KSME Conference
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• 2004.04a
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• pp.739-744
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• 2004

This paper presents an adaptive approach to control the amount of slip of the torque converter bypass clutch using its estimated friction coefficient. The proposed approach can be readily implemented using the inexpensive speed sensors currently installed in an automobile. A measurement feedback control law to drive the slip error to zero together with an adaptation law to identify the unknown friction coefficient is developed using the Lyapunov control design method. The robustness of the control and adaptation laws to parametric and/or torque uncertainties as well as the convergence of the friction coefficient are investigated. Simulation results verify the viability of the proposed control algorithm in real-world vehicle control applications.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.16 no.7
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• pp.610-614
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• 1991

This paper proposes a trajectory constrol fo a robot manipulator by using neural network. The inverse dynamic model of manipuator is learned by neural network. The manipulator is controlled by weight values of the learned neural network. The weight valuese is change with a torque of liner vontroller and a acceleration error. Phsically, the totlal torque for a manipualator is a sum of the liner controller torque and the nerural network controller torque. The proposed control effect is estimated by computer simulation.