• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.20 no.3
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• pp.53-61
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• 2006

This paper is proposed adaptive fuzzy-neuro controller for high performance of induction motor drive. The design of this algorithm based on fuzzy-neural network controller that is implemented using fuzzy control and neural network. This controller uses fuzzy nile as training patterns of a neural network. Also, this controller uses the back-propagation method to adjust the weights between the neurons of neural network in order to minimize the error between the command output and actual output. A model reference adaptive scheme is proposed in which 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 the adaptive fuzzy-neuro controller is evaluated by analysis for various operating conditions. The results of experiment prove that the proposed control system has strong high performance and robustness to parameter variation, and steady-state accuracy and transient response.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.4
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• pp.201-212
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• 2000

In this paper, an adaptive mechanism based on immune algorithm is designed and it is applied for the autonomous guided vehicle(AGV) driving. When the immune algorithm is applied to the PID controller, there exists the cast that the plant is damaged due to the abrupt change of PID parameters since the parameters are adjusted almost randomly. To solve this problem, a neural network is used to model the plant and the parameter tuning of the model is performed by the immune algorithm. After the PID parameters are determined in this off-line manner, these gains are then applied to the plant for the on-line control using immune adaptive algorithm. Moreover, even though the neural network model may not be accurate enough intially, the weighting parameters are adjusted to be accurate through the on-line fine tuning. The computer simulation for the control of steering and speed of AGV is performed. The results show that the proposed controller has better performances than other conventional controllers.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.724-727
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• 1999

In this paper, the direct and indirect neural adaptive controller are combined based on the Lyapunov synthesis approach. The proposed adaptive controller is constructed from RBF neural network and a set of fuzzy IF-THEN rules. And the weighting parameters are adjusted on-line according to some adaptation law for the purpose of controlling the plant to track a given trajectory. In this scheme, fuzzy IF-THEN rules are used to decide the combined weighting factor. It is shown that all the signals in the closed-loop system are uniformly bounded under mild assumptions. The effectiveness of the proposed control scheme is demonstrated through the control of one-link rigid robotics manipulator.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.1021-1024
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• 1999

In this paper, we propose a modified feedback neural network structure for adaptive control of robot manipulators. The proposed structure is that all of network output feedback into hidden units and output units. Learning algorithm is standard back-propagation algorithm. The simulation showed the effectiveness of using the new neural network structure in the adaptive control of robot manipulators.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2001.05a
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• pp.456-459
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• 2001

In this paper, a wavelet neural network for adaptive control was proposed. The structure of this network is similar to that of the multilayer perceptron(MLP), except that here the sigmoid functions are replated by mother wavelet function in the hidden units. The simulation result showed the effectiveness of using the wavelet neural network structure in the adaptive control of one-link manipulator.

• Journal of Power Electronics
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• v.2 no.1
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• pp.1-10
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• 2002

An advanced active power filter for the compensation of instantaneous harmonic current components in nonlinear current load is presented in this paper. A novel signal processing technique using an adaptive neural network algorithm is applied for the detection of harmonic components generated by three-phase nonlinear current loads and this method can efficiently determine the instantaneous harmonic components in real time. The control strategy of the switching signals to compensate current harmonics of the three-phase inverter is also discussed and its switching signals are generated with the space voltage vector modulation scheme. The validity of this active filtering processing system to compensate current harmonics is substantiated on the basis of simulation results.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.3
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• pp.503-509
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• 2014

In this paper, a PID controller with adaptive neural network compensator is proposed to control the formations of mobile robot. The control system is composed of a kinematic controller based on the leader-following robot and dynamic controller for considering the dynamics of the mobile robot. The dynamic controller is constituted by a PID controller and the adaptive neural network compensator for improving the performance and compensating the change in dynamic characteristics. Simulation results show the performance of the PID controller and the neural network compensator for the circular trajectory and linear trajectory. And it is verified that by improving the performance of a PID controller via the adaptive neural network compensator, the following robot's tracking performance is improved.

• Proceedings of the KIEE Conference
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• 2004.04a
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• pp.143-146
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• 2004

This paper is proposed adaptive fuzzy-neural network(FNN) controller for speed control of induction motor drive. The design of this algorithm based on FNN controller that is implemented using fuzzy control and neural network. A model reference adaptive scheme is proposed in which 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 the adaptive FNN controller is evaluated by analysis for various operating conditions.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.39 no.8
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• pp.725-734
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• 2011

At high angles of attack, aircraft dynamics can display an oscillatory lateral behavior that manifests itself as a limit cycle known as wing rock. In this paper, a classical and neural network based adaptive control design methods of adaptively stabilizing the oscillatory motion by adapting uncertainties are described in detail. All methods are simulated and compared using a model for an 80o swept delta wing.

• Proceedings of the KIEE Conference
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• 1993.07a
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• pp.227-230
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• 1993

Recently, the development of novel control methodology enables us to improve the performance of AC-machine drives by using pulse width modulation (PWM) technique. Usually, the dynamic characteristic of induction motor (IM) has been represented by the 5-th order nonlinear differential equation. This dynamics, however, can be reduced to 3-rd order dynamics by applying direct control of IM input current. This methodology concludes that it is much easier to control IM by means of the field-oriented methods employing the current controller. Therefore a precise current control is crucial to achieve a high control performance both in dynamic and steady state operations. This paper presents an adaptive fuzzy current controller with artificial neural network (ANN) for field-oriented controlled IM. This new control structure is able to adaptively minimize a current ripple while maintaining constant switching frequency. Especially the proposed controller employs neuro-computing philosophy as well as adaptive learning pattern recognizing principles with respect to variations of the system parameters. The proposed approach is applied to the IM drive system, and its performance is tested through various simulations. Simulation results show that the proposed system, compared among several known classical methods, has a superb performance.