• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.349-349
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• 2000

A Clustering Adaptive Fuzzy Logic Controller(CAFLC) is applied to the torque control of a brushless do motor drive. Objective of this system includes elimination of torque ripple due to cogging at low speeds under loads. The CAFLC implemented has advantages of computational simplicity, and self-tuning characteristics. Simulation results showed that the torque ripple and dynamic response of the system using a CAFLC were superior to the model reference adaptive controlled system.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1994.10a
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• pp.460-465
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• 1994

A high precision position control techinque of Brushless DC(BLDC) motor system with time varying parameters is expressed using the self tuning control method. The time varying parameters is estimated on real time by detecting voltage references from controller and mechanical motor speeds from tacho-generator. The effectiveness of the method is evaluated through the positon control experimental results of a BLDC motor system for reference change and arbitrary disturbance.

• Proceedings of the KIEE Conference
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• 1985.07a
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• pp.55-60
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• 1985

A state-space model of a burn through point control system of an industrial sintering process is derived. The model is then used in designing a self-tuning controller which consists of the receding horizon control law and a least-squares prediction algorithm. By applying this adaptive controller to POSCO sintering process IV, satisfactory expermental results have been obtained. Some of these practical results are presented in this paper.

• Journal of Power Electronics
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• v.13 no.4
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• pp.656-668
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• 2013

This paper presents a wavelet-fuzzy based controller for indirect field oriented control of three-phase induction motor drives. The discrete wavelet transform is used to decompose the error between the actual speed and the command speed of the induction motor drive into different frequency components. The transformed error coefficients along with the scaling gains are used for generating the control component of the motor. Self-tuning fuzzy logic is used for online tuning of the scaling gains of the controller. The proposed controller has the ability to meet the speed tracking requirements in the closed loop system. The complete indirect field oriented control scheme incorporating the proposed wavelet-fuzzy based controller is investigated theoretically and simulated under various dynamic operating conditions. The simulation results are compared with a conventional proportional integral controller and a fuzzy based controller. The speed control scheme incorporating the proposed controller is implemented in real time using a digital processor control board. Simulation and experimental results validate the effectiveness of the proposed controller.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.35 no.5
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• pp.177-184
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• 1986

An effective cartesian coordinate model is presented to control a robot motion along a prescribed timebased hand trajectory in cartesian coordinates and to provide an adaptive feedback design approach utilizing self-tuning control methods without requiring a detailed mathematical description of the system dynamics. Assuming that each of the hybrid variable set of velocities and forces at the cartesian coordinate level is mutually independent, the dynamic model for the cartesian coordinate control is reduced to first-order SISO models for each degree of freedom of robot hand, including a term to represent all unmodeled effects, by which the number of parameters to be identified is minimized. The self-tuners are designde to minimize a chosen performance criterion, and the computed control forces are resolved into applied joint torques by the Jacobian matrix. The robustness of the model and controller is demonstrated by comparing with the other catesian coordinate controllers.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.4
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• pp.434-443
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• 1999

In this paper, neural network predictive PID (NNPPID) control system is proposed to reduce the vibration of building structure. NNPPID control system is made up predictor, controller, and self-tuner to yield the parameters of controller. The neural networks predictor forecasts the future output based on present input and output of building structure. The controller is PID type whose parameters are yielded by neural networks self-tuning algorithm. Computer simulations show displacements of single and multi-story structure applied to NNPPID system about disturbance loads-wind forces and earthquakes.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.2
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• pp.50-59
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• 1998

This paper presents stabilization and adaptive control of flexible single link robot manipulator system by self-recurrent neural networks that is one of the neural networks and is effective in nonlinear control. The architecture of neural networks is a modified model of self-recurrent structure which has a hidden layer. The self-recurrent neural networks can be used to approximate any continuous function to any desired degree of accuracy and the weights are updated by feedback-error learning algorithm. When a flexible manipulator is rotated by a motor through the fixed end, transverse vibration may occur. The motor toroque should be controlled in such a way that the motor rotates by a specified angle, while simultaneously stabilizing vibration of the flexible manipuators so that it is arresed as soon as possible at the end of rotation. Accurate vibration control of lightweight manipulator during the large changes in configuration common to robotic tasks requires dynamic models that describe both the rigid body motions, as well as the flexural vibrations. Therefore, a dynamic models for a flexible single link robot manipulator is derived, and then a comparative analysis was made with linear controller through an simulation and experiment. The results are proesented to illustrate thd advantages and imporved performance of the proposed adaptive control ove the conventional linear controller.

• Proceedings of the KIEE Conference
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• 2005.07b
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• pp.1453-1455
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• 2005

This paper presents self tuning PI controller of IPMSM drive using neural network. Self tuning PI controller is developed to minimize overshoot, rise time and settling time following sudden parameter changes such as speed, load torque and inertia. Also, this paper is proposed speed control of IPMSM using neural network and estimation of speed using artificial neural network(ANN) controller. The results on a speed controller of IPMSM are presented to show the effectiveness of the proposed gain tuner. And this controller is better than the fixed gains one in terms of robustness, even under great variations of operating conditions and load disturbance.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.24 no.4
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• pp.33-42
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• 2010

The induction motor drive applied to field oriented control is widely used in industry applications. However, it is deceased performance and authenticity by saturation, temperature changing, disturbance and parameters changing because modeling of induction motor is nonlinear and complex. In order to control variable speed operation, conventional PI-like controllers are commonly used. These controllers provide limited good performance over a wide range of operation, even under ideal field oriented conditions. This paper proposes self tuning PI controller based on fuzzy-neural network(FNN)-PI controller that is implemented using fuzzy control, neural network, and adaptive fuzzy controller(AFC). Also, this paper proposes estimation of speed using ANN. The proposed control algorithm is applied to induction motor drive system using FNN-PI, AFC and ANN controller. Also, this paper proposes the anlysis results to verify the effectiveness of controller.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.324-329
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• 1998

In this paper, neural networks predictive PID (NNPPID) control system is proposed to reduce the vibration of structure. NNPPID control system is made up predictor, controller, and self-tuner to yield the optimal parameters of controller. The neural networks predictor forecasts the future outputs based on present input and output of structure. The controller is PID type whose parameters are yielded by neural networks self tuning algorithm. Computer simulations show displacements of multi-story structures applied to NNPPID system about environmental load-wind forces and earthquakes.