• International Journal of Control, Automation, and Systems
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• 제3권1호
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• pp.43-55
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• 2005

In this paper, a predictive control method using self-recurrent wavelet neural network (SRWNN) is proposed for chaotic systems. Since the SRWNN has a self-recurrent mother wavelet layer, it can well attract the complex nonlinear system though the SRWNN has less mother wavelet nodes than the wavelet neural network (WNN). Thus, the SRWNN is used as a model predictor for predicting the dynamic property of chaotic systems. The gradient descent method with the adaptive learning rates is applied to train the parameters of the SRWNN based predictor and controller. The adaptive learning rates are derived from the discrete Lyapunov stability theorem, which are used to guarantee the convergence of the predictive controller. Finally, the chaotic systems are provided to demonstrate the effectiveness of the proposed control strategy.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.640-645
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• 2004

This paper proposes a direct adaptive control method using self-recurrent wavelet neural network (SRWNN) for stable path tracking of mobile robots. The architecture of the SRWNN is a modified model of the wavelet neural network (WNN). Unlike the WNN, since a mother wavelet layer of the SRWNN is composed of self-feedback neurons, the SRWNN has the ability to store the past information of the wavelet. For this ability of the SRWNN, the SRWNN is used as a controller with simpler structure than the WNN in our on-line control process. The gradient-descent method with adaptive learning rates (ALR) is applied to train the parameters of the SRWNN. The ALR are derived from discrete Lyapunov stability theorem, which are used to guarantee the stable path tracking of mobile robots. Finally, through computer simulations, we demonstrate the effectiveness and stability of the proposed controller.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2008년도 제39회 하계학술대회
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• pp.1691-1692
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• 2008

In this paper, the adaptive neural network technique is proposed to control the speed of wind power generation system. For maximizing generated power effectively, adaptive neural algorithm based on SRWMM(Self Recurrent Wavelet Neural Network) is derived to on-line adjust the excitation winding voltage of the generator. Through computer simulations, it is shown that the proposed method can achieve smooth and asymptotic rotor speed tracking.

• 제어로봇시스템학회논문지
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• 제11권9호
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• pp.781-788
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• 2005

This paper proposes an identification method using a self recurrent wavelet neural network (SRWNN) for dynamic systems. The architecture of the proposed SRWNN is a modified model of the wavelet neural network (WNN). But, unlike the WNN, since a mother wavelet layer of the SRWNN is composed of self-feedback neurons, the SRWNN has the ability to store the past information of the wavelet. Thus, in the proposed identification architecture, the SRWNN is used for identifying nonlinear dynamic systems. The gradient descent method with adaptive teaming rates (ALRs) is applied to 1.am the parameters of the SRWNN identifier (SRWNNI). The ALRs are derived from the discrete Lyapunov stability theorem, which are used to guarantee the convergence of an SRWNNI. Finally, through computer simulations, we demonstrate the effectiveness of the proposed SRWNNI.

• 제어로봇시스템학회논문지
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• 제13권5호
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• pp.406-413
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• 2007

This paper proposes a self-recurrent wavelet neural network(SRWNN) based adaptive backstepping control technique for the robust steering control of autonomous underwater vehicles(AUVs) with unknown model uncertainties and external disturbance. The SRWNN, which has the properties such as fast convergence and simple structure, is used as the uncertainty observer of the steering model of AUV. The adaptation laws for the weights of SRWNN and reconstruction error compensator are induced from the Lyapunov stability theorem, which are used for the on-line control of AUV. Finally, simulation results for steering control of an AUV with unknown model uncertainties and external disturbance are included to illustrate the effectiveness of the proposed method.

• 제어로봇시스템학회논문지
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• 제12권3호
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• pp.233-240
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• 2006

This paper presents the robust control method using a self recurrent wavelet neural network (SRWNN) via adaptive backstepping design technique for stable walking of biped robots with unknown model uncertainties. The SRWNN, which has the properties such as fast convergence and simple structure, is used as the uncertainty observer of the biped robots. The adaptation laws for weights of the SRWNN and reconstruction error compensator are induced from the Lyapunov stability theorem, which are used for on-line controlling biped robots. Computer simulations of a five-link biped robot with unknown model uncertainties verify the validity of the proposed control system.

• 제어로봇시스템학회논문지
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• 제4권2호
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• pp.209-214
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• 1998

In this paper, we are dealing with the problem of controlling unknown nonlinear dynamical system by using neural networks. A novel error self-recurrent(ESR) neural model is presented to perform black-box identification. Through the various outcome of the experiment, a new neural network is seen to be considerably faster than the BP algorithm and has advantages of being less affected by poor initial weights and learning rate. These characteristics make it flexible to design the controller in real-time based on neural networks model. In addition, we design an adaptive PID controller that Keyser suggested by using ESR neural networks, and present a method on the implementation of adaptive controller based on neural network for practical applications. We obtained good results in the case of robot manipulator experiment.

• 조명전기설비학회논문지
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• 제21권7호
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• pp.103-108
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• 2007

신경회로망은 많은 제어 시스템 분야에서 이용되고 있으나, 단일 궤환 신경회로망 제어기로 사용할 경우 입출력 패턴을 구하기 쉽지 않고, 부하급변 및 외란이 인가되는 경우에는 만족할만한 성능을 얻을 수 없었다. 이러한 문제를 해결하기 위해 본 논문에서는 신경회로망 출력노드의 활성화 함수 대신에 제어 대상체를 사용하는 새로운 알고리즘을 제안하였다. 결과적으로 제안된 신경회로망 자율 적응 제어 시스템은 구조가 간략화 되었으며 입출력 패턴의 문제가 해결되었고 일반적인 역전파 알고리즘을 이용하여 실시간으로 학습이 가능하게 되었다. 제안된 신경망 자율 적응 제어의 알고리즘 효과는 고속연산을 실행하는 DSP(TMS320C32)에 알고리즘을 탑재하여 A.C. 서보 모터의 속도제어에 의해서 확인하였다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년 학술대회 논문집 정보 및 제어부문
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• pp.315-317
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• 2006

In this paper, we design a terminal sliding mode controller based on neural network for nonlinear systems with uncertainties. Terminal sliding mode control (TSMC) method can drive the tracking errors to zero within finite time. Also, TSMC has the advantages such as improved performance, robustness, reliability and precision by contrast with classical sliding mode control. For the control of nonlinear system with uncertainties, we employ the self-recurrent wavelet neural network(SRWNN) which is used for the prediction of uncertainties. The weights of SRWNN are trained by adaptive laws based on Lyapunov stability theorem. Finally, we carry out simulations to illustrate the effectiveness of the proposed control.

• International Journal of Control, Automation, and Systems
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• 제6권3호
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• pp.453-459
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• 2008

The neural network is currently being used throughout numerous control system fields. However, it is not easy to obtain an input-output pattern when the neural network is used for the system of a single feedback controller and it is difficult to obtain satisfactory performance with when the load changes rapidly or disturbance is applied. To resolve these problems, this paper proposes a new mode to implement a neural network controller by installing a real object for control and an algorithm for this, which can replace the existing method of implementing a neural network controller by utilizing activation function at the output node. The real plant object for controlling of this mode implements a simple neural network controller replacing the activation function and provides the error back propagation path to calculate the error at the output node. As the controller is designed using a simple structure neural network, the input-output pattern problem is solved naturally and real-time learning becomes possible through the general error back propagation algorithm. The new algorithm applied neural network controller gives excellent performance for initial and tracking response and shows a robust performance for rapid load change and disturbance, in which the permissible error surpasses the range border. The effect of the proposed control algorithm was verified in a test that controlled the speed of a motor equipped with a high speed computing capable DSP on which the proposed algorithm was loaded.