• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1050-1052
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• 1996

This paper presents a neuro-control algorithm for the speed control of a direct drive motor without the knowledge of the dynamics of the motor and the characteristics of a nonlinear load. In the field of motor control, it is not possible to directly use the back-propagation method in order to train a network since the desired output of the network is not known. Hence, we propose an extended back-propagation algorithm to force the closed loop system to give desired results. Experimental results shown that the proposed neuro-controller can reduce the unknown load effects and have the good velocity tracking capabilities.

• Journal of the Korean Society for Precision Engineering
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• v.22 no.6 s.171
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• pp.90-97
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• 2005

Magnetic flywheel system utilizes a magnetic bearing, which is able to support the shaft without mechanical contacts, and also it is able to control rotational vibration. Magnetic flywheel system is composed of position sensors, a digital controller, actuating amplifiers, an electromagnet and a flywheel. This work applies the neuro-fuzzy control algorithm to control the vibration of a magnetic flywheel system. It proposes the design skill of an optimal controller when the system has structured uncertainty and unstructured uncertainty, i.e. it has a difficulty in extracting the exact mathematical model. Inhibitory action of vibration was verified at the specified rotating speed. Unbalance response, a serious problem in rotating machinery, is improved by using a magnetic bearing with neuro-fuzzy algorithm.

• Journal of Biosystems Engineering
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• v.28 no.2
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• pp.117-126
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• 2003

This study was conducted to develop precision control system fur cold storage facility that could offer safe storage environment for green grocery. For that reason of neuro-fuzzy control system with learning ability algorithm and single chip neuro-fuzzy micro controller was developed for cold storage facility. Dynamic characteristics and hunting of neuro-fuzzy control system were far superior to on-off and fuzzy control system. Dynamic characteristics of temperature were faster than on-off control system by 1,555 seconds(123% faster) and fuzzy control system by 460 seconds(36.4% faster). When system was arrived at steady state. hunting was ${\pm}$0.5$^{\circ}C$ in on-off control system, ${\pm}$0.4$^{\circ}C$ in fuzzy control system, and ${\pm}$0.3$^{\circ}C$ in neuro-fuzzy control system. Hunting of humidity and wind velocity was also controlled precisely by 70 to 72.5% and 1m/s For storage experiment with onion, characteristics of neuro-fuzzy control system were tested. Dynamic characteristics of neuro-fuzzy control system made cold storage facility conducted precooling ability and minimized hunting.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.2
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• pp.285-290
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• 2020

Neuro-feedback is a technology that can identify your brain state and you can intentionally change your brain state. People with attention deficit and hyperactivity disorder need this technology but existing neuro-feedback training has a problem, which is not interesting and maintains a static state for a long time. In this paper, we proposed and implemented a neuro-feedback game that combines neuro-feedback and gun-shooting games to enhance concentration training. The neuro-feedback game has been implemented with the design of EEG measurement system, game controller and gamesoft. We hope that this study will be useful for people suffering from attention deficit and hyperactivity disorder.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2005.04a
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• pp.201-208
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• 2005

Neuro-Fuzzy modeling approach is proposed to predict the dynamic behavior of a single-degree-of-freedom structure that is equipped with hybrid base isolation system. Hybrid base isolation system consists of friction pendulum systems (FPS) and a magnetorheological (MR) damper. Fuzzy model of the M damper is trained by ANFIS using various displacement, velocity, and voltage combinations that are obtained from a series of performance tests. Modelling of the FPS is carried out with a nonlinear analytical equation that is derived in this study and neuro-fuzzy training. Fuzzy logic controller is employed to control the command voltage that is sent to MR damper. The dynamic responses or experimental structure subjected to various earthquake excitations are compared with numerically simulated results using neuro-fuzzy modeling method. Numerical simulation using neuro-fuzzy models of the MR damper and FPS predict response of the hybrid base isolation system very well.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.4
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• pp.158-160
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• 2006

In this paper, a neuro-fuzzy observer system is designed using neuro-fuzzy system for speed control of AC servo motor. This neuro-fuzzy observer is proposed to with the problems occur in the Luenberger observer and sliding observer. The problems of Luenberger and sliding observer are to have to know the dynamics and internal parameters of the system. Performance of the neuro-fuzzy observer system has verified through the experiment with dynamometer load. It is shown that feasibility of the neuro-fuzzy observer is verified.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.04b
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• pp.262-266
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• 1995

This paper presents a new adaptive-neuro control scheme to control the velocity and position of SCARA robot with parameter uncertainties. The adaptive control of linear system found wiedly in many areas of control application. While techniques for the adaptive control of linear systems have been well-established in the literature, there are a few corresponding techniques for nonlinear systems. In this paper an attempt is made to present a newcontrol scheme for theadaptive control of ponlinear robot based on a feedforward neural network. The proposed approach incorporates a neuro controller used within a reinforcement learning framework, which reduces the problem to one of learning a stochastic approximation of an unknown average error surface Emphasis is focused on the fact that the adaptive-neuro controoler dose not need any input/output information about the controlled system. The simulation result illustrates the effectiveness of the proposed adaptive-neuro control scheme.

• Proceedings of the KIEE Conference
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• 1997.07b
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• pp.606-608
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• 1997

In this paper, Neuro-Fuzzy Controller(NFC), a fuzzy system realized using a neural network, is to adopt for the multivariable system. In the multivariable system, the interactive effects between the variables should be taken into account. A simple compensator, using the steady-state information can be obtained for open-loop stable systems, is presented to cope with this problem. However, it should be supposed that the plant is unknown to the control system designer, but an estimate of the DC gain has been obtained by carrying out experiments on the plant. Also, if the variables are not combinated completely, it is difficult to design the controller. Therefore, we design a neuro-fuzzy controller which controls a multivariable system with only input output informations, and compare its performance with that of a PI controller. In the proposed controller, the construction of the membership functions and rule base, which is highly heuristic, can be achieved using a training process. This allows the combination of knowledge of human experts and evidence from input-output data.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.2
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• pp.144-150
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• 2002

In this paper a design of neuro-fuzzy-based predictive controller for nonlinear systems with time-delay is proposed. The proposed control system contains two neuro-fuzzy systems called ANFIS(Adaptive Neuro-Fuzzy Inference System). One is run as a series-parallel mode and the other is run as a parallel mode. An ANFIS running in series-parallel mode emulates the response of the nonlinear system with time-delay. Another ANFIS running in parallel mode generates the predicted output of the nonlinear system to compensate for the time-delays. Therefore, the proposed control system can be thought of as an extension of Smith-predictor scheme to the nonlinear systems with time-delay. A detailed design Procedure is presented and finally computer simulations are executed for the effectiveness of the proposed control scheme.

• Journal of Institute of Control, Robotics and Systems
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• v.3 no.2
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• pp.139-148
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• 1997

본 논문에서는 비선형 마찰이 존재하는 조준경 안정화 시스템에 대해서 마찰력 보상과 성능개선을 위한 신경망제어기의 설계방법을 제시한다. 제안한 신경망제어기는 비례, 적분, 진상(PI/LEAD) 제어기와 신경회로망과의 병렬로 구성되며, 제어 목적은 비선형 마찰과 외란이 존재하여도 안정거울의 각속도 추적성능과 안정화 성능의 향상에 있다. 신경회로망의 입력으로 안정거울의 각속도 추적오차와 추적오차의 적분, 제어입력이 필터를 통과한 신호가 사용되며, 신경호로망은 간접학습구조에 의해 학습된다. 조준경 시스템의 비선형 마찰력인 쿨롱마찰력의 크기가 외부환경에 따라 변하는 경우와 시스템으로 외란이 인가되는 경우에 대하여도 제안한 병렬제어기는 기존의 PI/LEAD 제어기보다 추적과 안정화 성능면에서 우수함을 컴퓨터 모의 실험으로 확인한다.