• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.31 no.6
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• pp.61-71
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• 2003

In the study of control technique for a turbojet engine model, the Takagi-Sugeno fuzzy logic controller has been designed based on the model identification by the well designed PI controlled system through T-S neuro-fuzzy inference system. To enhance this designed controller, those procedures are proposed that certainty factors are adopted to each rule of objective groups which are classified by the fuzzy C-Means algorithm and the satisfaction degrees are matched to meet the objectives. This proposed technique shows its feasibility by upgrading performances of the previously well-designed T-S fuzzy controller.

• Proceedings of the KIEE Conference
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• 1999.07c
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• pp.1314-1317
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• 1999

In this paper, we design an Adaptive Neuro-Fuzzy Inference System(ANFIS) Precompensator for the performance improvement of conventional proportional integral derivative (PID) controller that the governor system of power plant constantly maintains the load frequency of two-area power system. The ANFIS Precompensator is expressed as the membership functions of premise parameters and the linear combination of consequent parameters by Sugeno's fuzzy if-then rules using nonlinear input-output relation for the set point automatic modification maintaining conventional PID controller. The proposed compensation design technique is hoped to be satisfactory method overcome difficulty of exact modelling and arising problems by the complex nonlinearities of power system, and our design shows merit that is easily implemented by adding an ANFIS precompenastor to an existing PID controller without replacement.

• Journal of the Korean Institute of Intelligent Systems
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• v.6 no.4
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• pp.49-60
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• 1996

The objective of this paper is to develop an adaptive learning method for fuzzy hypercubes using a neural network. An intelligent control system is proposed by exploiting only the merits of a fuzzy logic controller and a neural network, assuming that we can modify in real time the consequential parts of the rulebase with adaptive learning, and that initial fuzzy control rules are established in a temporarily stable region. We choose the structure of fuzzy hypercubes for the fuzzy controller, and utilize the Perceptron learning rule in order to upda1.e the fuzzy control ru1c:s on-line with the output errors. As a result, the effectiveness and the robustness of this intelligent controller are shown with application of the proposed adaptive fuzzy-neuro controller to control of the cart-pole system.

• Proceedings of the KIEE Conference
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• 2008.04a
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• pp.151-152
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• 2008

본 논문에서는 도립 역진자 시스템에서의 진자의 도립 상태를 유지하도록 하기 위하여, DSP와 FPGA를 결합하여 ANFIS 뉴로퍼지 제어기를 구현하여 실험하였다. 도립진자의 위치 추종 성능을 PID 제어기와 비교 평가하였다.

• Proceedings of the KAIS Fall Conference
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• 2007.05a
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• pp.150-153
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• 2007

The intent of this paper is to describe a neural network structure called multi dynamic neural network(MDNN), and examine how it can be used in developing a learning scheme for computing robot inverse kinematic transformations. The architecture and learning algorithm of the proposed dynamic neural network structure, the MDNN, are described. Computer simulations are demonstrate the effectiveness of the proposed learning using the MDNN.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.805-810
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• 1993

In this paper, we propose a compliance control scheme using a neurofuzzzy compliance model(NFCM). as a new control paradigm for telerobot systems. A NFCM, used as a compliance controller, is composed of a fuzzy compliance model(FCM), a neural network and a low pass filter. The NFCM is trained through a reinforcement learning algorithm, and then, can generate suitable compliant motion for a given task. A series of simulations have been performed to show applicability of the proposed algorithm send it is found that the NFCM can implement suitable compliant motion for a given task through the learning procedure.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.10a
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• pp.439-440
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• 2004

전력시스템의 부하주파수제어는 전력계통운용에 있어서 가장 중요하게 다루어야 한다. 본 논문에서는 강인한 퍼지제어기를 얻고자, 다층 신경회로망을 이용하여 퍼지제어기 멤버쉽 함수의 전건부 및 후건부 파라미터들을 시스템에 알맞게 자기 조정하기 위해 최급구배법에 근거한 오차 역전파 알고리즘으로 적응 학습시킬 수 있는 뉴로-퍼지제어기의 구조 및 알고리즘을 제안하였다.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.218-223
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• 1992

This paper describes a learning method of neural network controllers. The learning method improves the performance of indirect learning mechanism in the neuro-control of nonlinear systems. To precisely identify dynamic characteristics of the plant by utilizing a limited prior information we propose a new energy function which takes advantage of the proportional relationship between outputs of the plant and those of neural networks.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.471-473
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• 1998

본 논문에서는 뉴로-퍼지 제어기를 이용한 능동 소음제어기를 구현하였다. 능동 소음제어기는 잡음에 의하여 왜곡된 신호로부터 잡음을 제거하여 원 신호를 복원하는 제어시스템이다. 일반적으로 잡음의 특성이 시간에 따라 변화하고, 전달특성이 비선형적이므로 고정된 제어기에 의해서는 제어할 수 없다. 이 논문에서는 뉴로-퍼지 제어기를 사용하였고, 파라미터를 오차 역전과 학습을 통하여 변화시킴으로써 잡음의 특성에 효과적으로 적응하는 능동 소음제어기를 구성하였다. 시뮬레이션을 통하여 여러 종류의 신호에 대해서 랜덤 노이즈를 발생시키고 구성된 제어기의 성능을 확인하였다.

• International Journal of Control, Automation, and Systems
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• v.4 no.5
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• pp.545-558
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• 2006

In this paper, PD-like visual servoing is modified in two ways: a sliding-mode observer is applied to estimate the joint velocities, and a RBF neural network is used to compensate the unknown gravity and friction. Based on Lyapunov method and input--to-state stability theory, we prove that PD-like visual servoing with the sliding mode observer and the neuro compensator is robust stable when the gain of the PD controller is bigger than the upper bounds of the uncertainties. Several simulations are presented to support the theory results.