• 한국정밀공학회지
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• 제34권2호
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• pp.125-131
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• 2017

2-Axis Pan and Tilt Motion Platform, a complex multivariate non-linear system, may incur any disturbance, thus requiring system controller with robustness against various disturbances. In this study, we designed an adaptive backstepping compensated controller by estimating the disturbance and error using the Radial Basis Function Neural Network (RBF NN). In this process, Uniformly Ultimately Bounded (UUB) was demonstrated via Lyapunov and stability was confirmed. By generating progressive disturbance to the irregular frequency and amplitude changes, it was verified for various environmental disturbances. In addition, by setting the RBF NN input vector to the minimum, the estimated disturbance compensation process was analyzed. Only two input vectors facilitated compensatory function of RBF NN via estimating the modeling and control error values as well as irregular disturbance; the application of the process resulted in improved backstepping controller performance that was confirmed through simulation.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 하계종합학술대회 논문집 V
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• pp.2783-2786
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• 2003

In this paper, the speed controller of permanent-magnet synchronous motor (PMSM) using the RBF neural (NN) disturbance observer is proposed. The suggested controller is designed using the input-output feedback linearization technique for the nominal model of PMSM and incorporates the RBF NN disturbance observer to compensate for the system uncertainties. Therefore the proposed algorithm is robust against the uncertainties of the system. Finally, the computer simulation is rallied out to verify the effectiveness of the proposed method.

• 전자공학회논문지CI
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• 제37권3호
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• pp.27-38
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• 2000

본 논문은 장애물 회피 능력을 갖는 자발적 주행 로봇 (Khepera)을 제어하는 재귀 신경망을 진화와 학습의 상호 적응에 의해 결정하는 방안을 제시한다. 제안한 동시 적응 방안은 다음 두 가지 성질을 갖는다. 유전자 알고리즘에 의해 해집단내 여러 개의 신경망 제어기들은 전역적 탐색을 수행하여 점진적으로 장애물과의 충돌이 적게 일어나도록 진화되고, 동시에 각 신경망 제어기는 상보적 재강화 역전파 (CRBP： Complementary Reinforcement Backpropagation) 학습에 의해 국부적 탐색을 수행하여 주행 특성이 로봇이 처한. 외부 환경에 적응되어진다. 실험 결과, 학습과 결합한 진화에 의해 얻어진 신경망 제어기가 진화자체만에 의해 얻어진 신경망 제어기보다 더 나은 충돌 회피 능력을 보여 주며, 원하는 주행 성능에 보다 빨리 도달하는 것을 확인할 수 있다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2007년도 춘계학술대회 논문집 전기기기 및 에너지변환시스템부문
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• pp.94-96
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• 2007

This paper is proposed an efficiency optimization control algorithm for IPMSM which minimizes the copper and iron losses. The design of the speed controller based on adaptive fuzzy teaming control-fuzzy neural networks(AFLC-FNN) controller that is implemented using adaptive, fuzzy control and neural networks. The control performance of the AFLC-FNN controller is evaluated by analysis for various operating conditions. Analysis results are presented to show the validity of the proposed algorithm.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2001년도 Proceedings ICPE 01 2001 International Conference on Power Electronics
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• pp.656-659
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• 2001

In this paper a neural network controller for achieving maximum power tracking as well as output voltage regulation, for a wind energy conversion system(WECS) employing a permanent magnet synchronous generator, is proposed. The permanent magnet generator (PMG) supplies a dc load via a bridge rectifier and two buck-boost converters. Adjusting the switching frequency of the first buck-boost converter achieves maximum power tracking. Adjusting the switching frequency of the second buck-boost converter allows output voltage regulation. The on-times of the switching devices of the two converters are supplied by the developed neural network(NN). The effect of sudden changes in wind speed ,and/or in reference voltage on the performance of the NN controller are explored. Simulation results showed the possibility of achieving maximum power tracking and output voltage regulation simultaneously with the developed neural network controller. The results proved also the fast response and robustness of the proposed control system.

• 전자공학회논문지SC
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• 제40권4호
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• pp.243-250
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• 2003

본 논문에서는 RBF 신경망 외란 관측기를 이용한 영구자석형 동기모터의 속도추종 제어기를 제안한다. 먼저 공칭 모델에 대하여 입출력 선형화에 기반한 속도 제어기를 설계하고 RBF 신경망 외란 관측기에 의해 시스템의 블확실성을 보상한다. 시스템의 파라미터와 부하 토크의 변동을 동시에 추정하는 RBF 신경망 외란 관측기를 이용함으로써 제안한 제어 알고리즘은 시스템의 불화실성에 강인한 특성을 갖는다. 마지막으로 모의실험을 통하여 제안된 제어기의 타당성을 검증한다.

• 대한기계학회논문집A
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• 제28권7호
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• pp.955-960
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• 2004

In this paper, a neural network controller that can be implemented in parallel with a PD controller is suggested for motion control of a hydraulic servo cylinder. By applying a self-excited oscillation method, the system design parameters of open loop transfer function of servo cylinder system are identified. Based on system design parameters, the PD gains are determined for the desired closed loop characteristics. The Neural Network is incorporated with PD control in order to compensate the inherent nonlinearities of hydraulic servo system. As an application example, a motion control using PD-NN has been performed and proved its superior performance by comparing with that of a PD control.

• Journal of Mechanical Science and Technology
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• 제19권2호
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• pp.505-519
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• 2005

In this paper, we develop an anti-sway control in proposed techniques for an ATC system. The developed algorithm is to build the optimal path of container motion and to calculate an anti-collision path for collision avoidance in its movement to the finial coordinate. Moreover, in order to show the effectiveness in this research, we compared NNP PID controller to be tuning parameters of controller using NN with 2 DOF PID controller. The simulation and experimental results show that the proposed control scheme guarantees performances, trolley position, sway angle and settling time in NNP PID controller than other controller. As the results in this paper, the application of NNP PID controller is analyzed to have robustness about disturbance which is wind of fixed pattern in the yard. Accordingly, the proposed algorithm in this study can be readily used for industrial applications.

• 한국정밀공학회지
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• 제22권1호
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• pp.61-72
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• 2005

In this paper, we develop anti-sway control in proposed techniques for an ATC system. The developed algorithm is to build the optimal path of container motion and to calculate an anti-collision path for collision avoidance in its movement to the finial coordinate. Moreover, in order to show the effectiveness in this research, we compared NNP PID controller to be tuning parameters of controller using NN with 2 DOF PID controller. The experimental results for an ATC simulator show that the proposed control scheme guarantees performances, trolley position, sway angle, and settling time in NNP PID controller than other controller. As a result, the application of NNP PID controller is analyzed to have robustness about disturbance which is wind of fixed pattern in the yard. Accordingly, the proposed algorithm in this study can be readily used for industrial applications

• 대한전기학회:학술대회논문집
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• 대한전기학회 2008년도 제39회 하계학술대회
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• pp.1759-1760
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• 2008

This paper proposes a neural network(NN)-based adaptive sliding mode controller for discrete-time nonlinear systems. By using disturbance estimation technique, a sliding mode controller is designed, which forces the sliding variable to be zero. Then, NN compensator with hidden-layer-to-output-layer weight update rule is combined with sliding mode controller in order to reduce the error of the estimates of both disturbances and nonlinear functions. The whole closed loop system rejects disturbances excellently and is proved to be ultimately uniformly bounded(UUB) provided that certain conditions for design parameters are satisfied.