• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.5
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• pp.46-50
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• 1997

In fuzzy logic control, static fuzzy rules cannot cope with significant changes of parameters of plants or environment. To solve this prohlem, self-organizing fuzzy control. neural-network-hased fuzzy logic control and so on have heen introduced so far. However, dynamically changed fuzzy rules of these schemes may make a fuzzy logic controller Fall into dangerous situations because the changed fuzzy rules may he incomplete or inconsistent. This paper proposes a new adaptive filzzy logic control scheme using a predictivc neural network. Although some parameters of a controlled plant or environment are changed, proposed fuzzy logic controller changes its decision outputs adaptively and robustly using unchanged initial fuzzy rules and the predictive errors generated hy the predictive neural network by on-line learning. Experimental results with a D<' servo-motor position control problem show that propnsed cnntrol scheme is very useful in the viewpoint of adaptability.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.6
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• pp.720-729
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• 2005

In this paper, we design the indirect adaptive controller using Wavelet Neural Network(WNN) for unknown nonlinear systems. The proposed indirect adaptive controller using WNN consists of identification model and controller. Here, the WNN is used in both Identification model and controller The WNN has advantage of indicating the location in both time and frequency simultaneously, and has faster convergence than MLPN and RBFN. There are several training methods for WNN, such as GD, GA, DNA, etc. In this paper, we present the Extended Kalman Filter(EKF) based training method. Although it is computationally complex, this algorithm updates parameters consistent with previous data and usually converges in a few iterations. Finally, ore illustrate the effectiveness of our method through computer simulations for the Buffing system and the one-link rigid robot manipulator. From the simulation results, we show that the indirect adaptive controller using the EKF method has better performance than the GD method.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.46 no.2
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• pp.141-149
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• 2018

This paper presents a neural network controller design for complex damage to a blended wing Unmanned Aerial Vehicle(UAV): partial loss of main wing and vertical tail. Longitudinal/lateral axis instability and the change of flight dynamics is investigated via numerical simulation. Based on this, neural network based adaptive controller combined with two types of feedback linearization are designed in order to compensate for the complex damage. Performance of two kinds of dynamic inversion controllers is analyzed against complex damage. According to the structure of the dynamic inversion controller, the performance difference is confirmed in normal situation and under damaged situation. Numerical simulation verifies that the instability from the complex damage of the UAV can be stabilized via the proposed adaptive controller.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1995.10b
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• pp.243-248
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• 1995

본 논문에서는 신경회로망의 학습능력을 이용하여 AC 모터의 속도제어에 이용된 기 존의 PI제어기의 문제점을 보완하고자 한다. 기존의 아날로그 PI제어기에서는 각 비례, 적분 파라메타를 개발자가 조정하여 고정하면 부하가 변동될 경우 적응성이 떨어지는 문제점을 안고 있었다. 본 논문에서 제시된 디지털 신경망제어기는 학습을 통해 새로운 환경에 적응 가능하다는 점에 가정하여 설계하고 성능을 비교 평가하였다. 본 논문에서 사용된 신경회로 망의 구조는 신경망중에서 가장 범용적으로 사용되는 다층 퍼셉트론 모델구조를 선택하였 다. 신경망 제어기장치로는 인텔 8097 마이크로 콘트롤러를 이용하였다.

• 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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• 2004.11c
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• pp.361-363
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• 2004

시간 및 주파수 특성 분석이 용이한 웨이블릿을 신경회로망에 적용시킨 웨이블릿 신경 회로망의 파라미터 학습 방법에는 오차 역전파 알고리듬 및 유선 알고리듬 등 여러 가지 방법이 있으나 이러한 학습 방법들은 수렴 시간이 오래 걸리는 단점을 가진다. 따라서 본 논문에서는 웨이블릿 신경 회로망의 최적 파라미터를 결정하기 위한 학습 방법으로 일반적으로 비선형 시스템 추정에 주로 사용되는 확장 칼만 필터 알고리듬을 적용한 신경회로망을 제안한다. 또한 제안된 학습 알고리듬을 이용한 웨이블릿 신경 회로망으로 간접 적응 제어기를 설계하여 연속 시간 혼돈 시스템인 Duffing 시스템의 제어에 적용함으로써 확장 칼만 필터 학습 알고리듬을 적용한 웨이블릿 신경 회로망 모델의 우수성을 보인다.

• Journal of IKEEE
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• v.14 no.3
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• pp.182-189
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• 2010

A new Adaptive neural state-feedback controller for the fully nonaffine pure-feedback nonlinear system are presented in this paper. By reformulating the original pure-feedback system to a standard normal form with respect to newly defined state variables, the proposed controller requires no backstepping design procedure. Avoiding backstepping makes the controller structure and stability analysis considerably simple. The proposed controller employs only one neural network to approximate unknown ideal controllers, which highlights the simplicity of the proposed neural controller. Simulation examples demonstrate the efficiency and performance of the proposed approach.

• 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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• 2001.11c
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• pp.297-300
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• 2001

본 논문에서는 로봇 매니퓰레이터의 제어에 사용할 수 있는 신경망 외란 관측기를 제안하도록 한다. 제안한 신경망 외란 관측기는 다층신경 망의 구조로 신경망 외란관측기의 오차와 제어 오차가 충분히 작은 콤팩트 집합에 절대 상시 유계된다. 본 논문에서 제안하는 신경망 외란 관측기는 기존의 적응 제어기의 단점을 해결한 방식으로 복잡한 회귀 모델을 필요로 하지 않는다. 끝으로 제안한 방식을 3관절 로봇에 적용하여 그 타당성을 확인한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.3 no.2
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• pp.383-388
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• 1999

In this paper, we propose the fuzzy-neural controller with genetic algorithm(GA) for precise on-line control. We design the proposed controller having a ability to adjust membership function for a plant by advanced algorithm of fuzzy-neural network after approximative one being completed by genetic algorithm. Finally we compare the result for a speed control of DC servo motor by the proposed controller with GA-fuzzy one in order to evaluate its performance and precision.