• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.38 no.4
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• pp.335-341
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• 2010

To improve the performance of inner loop based on PD controller for a unmanned helicopter, neural networks are applied. The performance of PD controller designed on the response characteristics of error dynamics decreases because of uncertain nonlinearities of the system. The nonlinearities are decoupled to modified dynamic inversion model(MDIM) and are compensated by the neural networks. For the training of the neural networks, online weight adaptation laws which are derived from Lyapunov's direct method are used to guarantee the stability of the controller. The results of the improved performance of PD controller by neural networks are illustrated in the simulation of unmanned helicopter with nonlinearities,

• The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.11 no.1
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• pp.39-47
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• 1997

본 논문에서는 퍼지제어기와 신경회로망 적응 관측기를 적용하여 강인성을 AC드라이브 시스템을 제안하였다. 퍼지제어기는 유도전동기의 속도 제어시 빠른 속도 응답 특성을 얻기 위하여 사용하였다. 신경회로망 적응관측기는 전동기 파라메터 변화에 대하여 강인한 제어 시스템이 되도옥 자속 관측기오 토오크 적응관측기로 구성하였다. 사용된 신경회로망은 자속과 토오크의 동특성을 학습시키기 위하여 역전파 알고리즘을 사용하였따. 컴퓨터 시뮬레이션의 결과를 통해 제안된 시스템이 전동기 파라메터 변동과 부화이론에 강인하고 속도응답 특성이 우수함을 입증하였다.

• 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.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2052-2054
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• 2003

본 논문에서는 혼돈 비선형 시스템의 지능 제어를 위해 간접 적응 제어 기법에 기반한 웨이블릿 신경 회로망 제어기 설계 방법을 제안한다. 제어기 성능에 큰 영향을 미칠 수 있는 웨이블릿 신경 회로망 구조의 파라미터 동정은 본질적으로 강인하고 전역 최적해에 근사한 값을 결정할 수 있는 유전 알고리듬을 사용한다. 본 논문에서 제안한 제어 방법은 유전 알고리듬을 이용한 혼돈 비선형 시스템의 오프라인 동정 모델 및 기준 신호와 플랜트 출력으로 정의되는 제어 오차를 이용하여 원하는 제어 입력을 생성한다. 한편 본 논문에서 제안한 웨이블릿 신경 회로망 제어기를 대표적인 연속 시간 혼돈 비선형 시스템인 Duffing 시스템에 적용하여 설계된 제어기의 효율성 및 우수성을 검증하고자 한다.

• 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 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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• 2004.07d
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• pp.2248-2250
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• 2004

본 논문에서는 유연한 단일링크 매니퓰레이터의 끝단 위치 추적제어를 위해 웨이블렛 신경망을 이용한 강인 적응제어기를 제안한다. 전체 제어기는 웨이블렛 신경망에 의해 추정된 피드백 선형화 제어기와 그 추정오차를 보상하기 위한 보상제어기로 구성된다. 시스템의 출력값은 최소위상을 보장하기 위하여 재정의하여 사용된다. 구성된 웨이블렛 신경망의 연결 가중치는 Lyapunov 안정도 이론에 기초해서 조절된다. 제안된 제어기의 성능 향상은 PD 제어기와 비교함으로써 입증된다.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.33 no.3
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• pp.39-46
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• 2005

In this paper, adaptive higher harmonic control technique using Neural Networks (NN) is proposed. First, linear transfer function is estimated to relate the input harmonics and output harmonics, then NN which has the universal function approximation property is applied to expand application range of the transfer function. Optimal control gain matrix computed from the transfer function is used to train NN weights. Online weight adaptation laws are derived from Lyapunov's direct method to guarantee internal stability. Results of the simulation of 6-input 2-output nonlinear system show that adaptive HHC is applicable to the system with uncertain transfer function.

• The Transactions of the Korean Institute of Power Electronics
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• v.3 no.4
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• pp.382-387
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• 1998

The adaptive hysteresis band current control method using neural network is proposed to hold the switching frequency of PWM inverter constant at any operating points of ac motor. The adaptive hysteresis band equation is derived as the teaching signal of neural network. and then the structure and learning algorithm of the neural network a are suggested. The simulation results show that the switching frequency of PWM inverter is held constant at any operating conditions of ac motor and the proposed method has good transient performance of stator current.

• 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.