• 소음진동
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• 제8권5호
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• pp.832-840
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• 1998

Experimental studies on the adaptive vibration control of composite beams have been performed using a piezoelectric actuator and the neuro-controller. The variations in natural frequencies of the specimen and the actuation characteristics of the piezoelectric actuator according to the delamination in the bonding layer have been studied. In addition, the simulation of adaptive vibration control has been performed for the composite specimens with delaminated piezoelectric actuator using neuro-controller. The hardware for the adaptive vibration control experiment was prepared. A DSP(digital signal processor) has been used as a digital controller. Using neuro-controller, the adaptive vibration control experiment has been performed. The vibration control results using the neuro-controller show that the present neuro-controller has good performance and robustness with the system parameter variations.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2003년도 춘계학술대회 논문집
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• pp.983-987
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• 2003

Vibration causes noise and makes structure unstable. Especially, due to the effort of lightening, deformation of flexible structure is increased in its shape. Just a little disturbance causes vibration and low damping ratio causes residual vibration lasts long time. In this paper, by using a neuro-controller, which is one of the algorithm of adaptive control. we performed adaptive control of flexible cantilever plate and opened box structure with piezoelectric materials. The proposed adaptive vibration control algorithm, a neuro-controller, is proved in its effectiveness by applying to a opened box structure. The neuro-controller was implemented with DSP, and the real-time adaptive vibration control experiment results confirm that neuro-controller is reliable.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2003년도 추계학술대회
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• pp.1602-1607
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• 2003

Vibration causes noise and sometimes makes structure unstable. Especially, due to the efforts of lightening, deformation of flexible structure is increased in its shape. Just a little disturbance can cause vibration and low damping ratio makes residual vibration last long time. This research is concerned with the model reference neuro-controller design for the vibration suppression of smart structures. By using a model reference neurocontroller, which is one of the algorithms of adaptive control, we performed an adaptive control of flexible cantilever plate and opened box structure with piezoelectric materials. The proposed adaptive vibration control algorithm, a model reference neuro-controller, was proved in its effectiveness by applying to an opened box structure. The model reference neuro-controller is implemented with DSP, and the real-time adaptive vibration control experiment results confirm that the model reference neuro-controller is reliable.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1993년도 한국자동제어학술회의논문집(국제학술편); Seoul National University, Seoul; 20-22 Oct. 1993
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• pp.401-405
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• 1993

This paper presents a neuro adaptive control method for nonlinear dynamical systems based on artificial neural network systems. The proposed neuro adaptive controller consists of 3 layers artificial neural network system and parallel PD controller. At the early stage in learning or identification process of the system characteristics the PD controller works mainly in order to compensate for the inadequacy of the learning process and then gradually the neuro contrller begins to work instead of the PD controller after the learning process has proceeded. From the simulation studies the neuro adaptive controller is seen to be robust and works effectively for nonlinear dynamical systems from a practical applicational points of view.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 G
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• pp.2879-2881
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• 1999

This paper presents the adaptive Active Noise Control(ANC) system using the Neuro-Fuzzy controller. In general, the character of noise is time-varing and nonlinear Thus controller must have the adaptivness so that applied in Active Noise Control system to cancel the noise. This paper propose the Neuro-Fuzzy controller trained with back-propagation teaming algorithm to optimize the parameters of controller The objects of this paper are cancel the noise, extract the original(speech) signal polluted by noise and design the Neuro-Fuzzy controller.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제9권4호
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• pp.309-314
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• 2009

This paper presents implementation of the adaptive neuro-fuzzy control method. Control performance of the adaptive neuro-fuzzy control method for a popular inverted pendulum system is evaluated. The inverted pendulum system is designed and built as an education kit for educational purpose for engineering students. The educational kit is specially used for intelligent control education. Control purpose is to satisfy balancing angle and desired trajectory tracking performance. The adaptive neuro-fuzzy controller has the Takagi-Sugeno(T-S) fuzzy structure. Back-propagation algorithm is used for updating weights in the fuzzy control. Control performances of the inverted pendulum system by PID control method and the adaptive neuro-fuzzy control method are compared. Control hardware of a DSP 2812 board is used to achieve the real-time control performance. Experimental studies are conducted to show successful control performances of the inverted pendulum system by the adaptive neuro-fuzzy control method.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2003년도 춘계학술대회 논문집
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• pp.305-311
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• 2003

We propose a new technique of adaptive-neuro controller design to implement real-time control of robot manipulator, Unlike the well-established theory for the adaptive control of linear systems, there exists relatively little general theory for the adaptive control of nonlinear systems. Adaptive control technique is essential for providing a stable and robust performance for application of robot control. The proposed neuro control algorithm is one of loaming a model based error back-propagation scheme using Lyapunov stability analysis method. Through simulation, the proposed adaptive-neuro control scheme is proved to be a efficient control technique for real time control of robot system using DSPs(TMS320C50)

• Composites Research
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• 제14권1호
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• pp.39-46
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• 2001

본 논문에서는 신경망 제어기를 이용하여 복합재 보의 적응 다중 모드 진동 제어에 관한 실험적 연구를 수행하였다. 신경망 제어기는 계산량이 많기 때문에 실시간 적용에 어려움이 따른다. 본 논문에서는 진동 신호를 모드별로 분리하기 위한 적응 노치 필터를 제안하였다. 연결 강도의 개수가 적어서 계산량이 적은 두 개의 신경망 제어기를 이용하여 각 모드의 제어력을 계산하였다. 끝단 질량의 위치의 차이로 인해 고유 진동수가 다른 두 시편 A, B에 대하여 적응 노치 필터와 신경망 제어기를 이용한 적응 진동 제어를 수행한 결과, 두 경우 모두 효과적으로 진동 제어가 이루어졌다. 이러한 결과로 시스템 파라미터의 변환에 대한 신경망 제어기의 적응 진동 제어 성능을 확인할 수 있다.

• Journal of Advanced Marine Engineering and Technology
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• 제24권2호
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• pp.72-81
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• 2000

In this paper, we design an adaptive neuro-fuzzy inference system(ANFIS) precompensator for load frequency control of 2-area power systems. While proportional integral derivative (PID) controllers are used in power systems, they may have some problems because of high nonlinearities of the power systems. So, a neuro-fuzzy-based precompensation scheme is incorporated with a convectional PID controller to obtain robustness to the nonlinearities. The proposed precompensation technique can be easily implemented by adding a precompensator to an existing PID controller. The applied neruo-fuzzy inference system precompensator uses a hybrid learning algorithm. This algorithm is to use both a gradient descent method to optimize the premise parameters and a least squares method to solve for the consequent parameters. Simulation results show that the proposed control technique is superior to a conventional Ziegler-Nichols PID controller in dynamic responses about load disturbances.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2009년도 제40회 하계학술대회
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• pp.778_779
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• 2009

The maximum output torque developed by the machine is dependent on the allowable current rating and maximum voltage that the inverter can supply to the machine. Therefore, to use the inverter capacity fully, it is desirable to use the control scheme considering the voltage and current limit condition, which can yield the maximum torque per ampere over the entire speed range. The paper is proposed maximum torque control of induction motor drive using adaptive learning neuro fuzzy controller and artificial neural network(ANN). The control method is applicable over the entire speed range and considered the limits of the inverter's current and voltage rated value. For each control mode, a condition that determines the optimal d, q axis current $_i_{ds}$, $i_{qs}$ for maximum torque operation is derived. The proposed control algorithm is applied to induction motor drive system controlled adaptive learning neuro fuzzy controller and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper is proposed the analysis results to verify the effectiveness of the adaptive learning neuro fuzzy controller and ANN controller.