• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2492-2494
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• 2000

Design of Fuzzy cotroller consists of intuition of human expert, and any other information about how to control system. If the rules adequately control the system, the design work is done well. If the rules are inadequate, the designer must modify the rules. Through this procedure, the system can be controlled. In this paper, we designed simply a fuzzy controller based on human knowledge, but it has errors showing some vibrations. So we updated the optimal parameters of fuzzy controller using Neural Network algorithm.

• Proceedings of the KIEE Conference
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• 2009.04b
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• pp.32-34
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• 2009

The field oriented control of induction motors is widely used in high performance applications. However, detuning caused by parameter disturbance still limits the performance of these drives. In order to accomplish 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 is proposed model reference adaptive fuzzy control(MFC) and artificial neural network(ANN) based on the vector controlled induction motor drive system. Also, this paper is proposed control of speed and current using fuzzy adaption mechanism(FAM), MFC and estimation of speed using ANN. The proposed control algorithm is applied to induction motor drive system using FAM, MFC and ANN controller. Also, this paper is proposed the analysis results to verify the effectiveness of this controller.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.23 no.9A
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• pp.2211-2220
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• 1998

This paper presetns an adaptive critic self-learning control system with cerebellar model articulation controller (CMAC)-based decoder integrated with the associative search element (ASE) and adatpive critic element(ACE)- based scheme. The tast of the system is to balance a pole that is hinged to a movable cart by applying forces to the cart's base. The problem is that error feedback information is limited. This problem can be sloved when some adaptive control devices are involved. The ASE incorporates prediction information for reinforrcement from a critic to produce evaluative information for the plant. The CMAC-based decoder interprets one state to a set of patways into the ASE/ACE. These signals correspond to te current state and its possible preceding action states. The CMAC's information interpolation improves the learning speed. And design inverted pendulum hardware system to show control capability with neural network.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2372-2377
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• 2003

Nonlinear dynamic system exist widely in many types of systems such as chemical processes, biomedical processes, and the main steam temperature control system of the thermal power plant. Up to the present time, PID Controllers have been used to operate these systems. However, it is very difficult to achieve an optimal PID gain with no experience, because of the interaction between loops and gain of the PID controller has to be manually tuned by trial and error. This paper suggests control approaches by immune fuzzy for the nonlinear control system inverted pendulum, through computer simulation. This paper defines relationship state variables $x,{\dot{x}},{\theta},\dot{\theta}$ using immune fuzzy and applied its results to stability.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.170-173
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• 2005

This paper presents an Fuzzy-Neuro Observer system for an ac servo motor dirve to track periodic commands using a neuro-fuzzy observer. AC servo motor drive system is rather similar to a linear system. However, the uncertainties, such as machanical parametric variation, external disturbance, uncertainty due to nonideal in transient state. therefore an intelligent control system that isan on-line trained neural network controller with adaptive learning rates.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.2
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• pp.146-156
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• 2003

Nonlinear dynamic system exist widely in many types of systems such as chemical processes, biomedical processes, and the main steam temperature control system of the thermal power plant. Up to the present time, PID Controllers have been used to operate these systems. However, it is very difficult to achieve an optimal PID gain with no experience, because of the interaction between loops and gain of the PID controller has to be manually tuned by trial and error. This paper suggests control approaches by immune fuzzy for the nonlinear control system inverted pendulum, through computer simulation. This paper defines relationship state variables $x,\dot{x},{\theta},\dot{\theta}$ using immune fuzzy and applied its results to stability.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1994.10a
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• pp.348-353
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• 1994

Magnetic Servo Levitation (MSL) has been proposed as a method to drive a fast-tool servo system. This paper discusses some fundamental control and modeling issues in the development of a long-range high-bandwidth fast-tool servo based on MSL. A resursive linear model is developed to describe the system's dynamics linear model is developed to describe the system's dynamics, and further used to discuss controller design. For a given controller architecture, the performance of two controllers is then compared, one based on an approximation to the inverse plant dynamics, the second based on a adaptive neural network.

• Journal of the Institute of Convergence Signal Processing
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• v.19 no.2
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• pp.77-88
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• 2018

This paper proposes a RBFNN(Radial Basis Function Neural Network) based decentralized adaptive tracking control scheme using PSO(Particle Swarm Optimization) for an uncertain electrically driven robot system with input saturation. Practically, the magnitudes of input voltage and current signals are limited due to the saturation of actuators in robot systems. The proposed controller overcomes this input saturation and does not require any robot link and actuator model parameters. The fitness function used in the presented PSO scheme is expressed as a multi-objective function including the magnitudes of voltages and currents as well as the tracking errors. Using a PSO scheme, the control gains and the number of the RBFs are tuned automatically and thus the performance of the control system is improved. The stability of the total control system is guaranteed by the Lyapunov stability analysis. The validity and robustness of the proposed control scheme are verified through simulation results.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.18 no.6
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• pp.37-44
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• 2004

This paper represents a new approach for the protective relay of power transmission lines using a Artificial Neural Network(ANN). A different fault m transmission lines need to be detected classified and located accurately and cleared as fast as possible. However, The protection range of the distance relay is always designed on the basis of fixed settings, and unfortunately these approach do not have the ability to adapt dynamically to the system operating condition. ANN is suitable for the adaptive relaying and the detection of complex faults. The backpropagation algerian based multi-layer protection is utilized for the teaming process. It allows to make control to various protection functions. As expected, the simulation result demonstrate that this approach is useful and satisfactory.

• Proceedings of the KIEE Conference
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• 2008.04a
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• pp.219-220
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• 2008

The paper is proposed maximum torque control of SynRM drive using adaptive fuzzy neuro inference system(AFNIS) and artificial neural network(ANN). The control method is applicable over the entire speed range and considered the limits of the inverter's current and voltage rated value. For each control mode, a condition that determines the optimal d-axis current $i_d$ for maximum torque operation is derived. The proposed control algorithm is applied to SynRM drive system controlled AFNIS and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper is proposed the analysis results to verify the effectiveness of the AFNIS and ANN controller.