• 한국항해학회지
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• 제21권1호
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• pp.109-118
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• 1997

In this paper, applications of multilayer neural networks to control of flexible robot beam are considered. The multilayer nerual networks can be used to approximate any continuous function to a desired degree of accuracy and the weights are updated by Gradient Method. When a flexible beam is rotated by a motor through the fixed end, transverse vibration may occur. The motor torque should be controlled insuch a way that the motor rotates by a specified angle, while simultaneously stabilizing vibration of the flexible manipulators so that is arrested as soon as possbile at the end of rotation. Accurate control of lightweight beam during the large changes in configuration common to robotic tasks requires dynamic models that describe both rigid body motions, as well as the flexural vibrations. Therefore, a linear dynamic state-space model of for a single link flexible robot beam is derived and PD controller, LQP controller, and inverse dynamical neural networks controller are composed. The effectiveness the proposed control system is confirmed by computer simulation.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 ITC-CSCC -3
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• pp.1831-1834
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• 2002

In this paper, we present results of a study of congestion control for real-time communication based on ATM networks. In ATM networks, congestion usually results in cell loss. Based on the time limit and priority, the cells that compete for the same output line could be lined according to the character of real-time service. We adopt priority control algorithm for providing different QoS bearer services that can be implemented by using threshold methods at the ATM switching nodes, the cells of different deadline and priority could be deal with according to the necessity. Experiments show the proposed algorithm is effective in the congestion control of ATM real-time networks

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 D
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• pp.3001-3003
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• 2000

In this paper. we propose Fuzzy Self-Organizing Networks (FSON) using both Polynomial Neural Networks(PNN) and Fuzzy Neural Networks(FNN) for model identification of complex and nonlinear systems. The proposed FSON is generated from the mutually combined structure of both FNN and PNN. Accordingly it is possible to consider the nonlinearity characteristics of process and to get the better output performance with superb predictive ability. In order to evaluate the performance of proposed models. we use the nonlinear data sets. The results show that the proposed FSON can produce the model with higher accuracy and more robustness than previous any other method.

• 한국멀티미디어학회논문지
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• 제19권8호
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• pp.1404-1414
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• 2016

Cross-layer design is a concept, which captures the dependencies and interactions and enables information sharing among layers in order to improve the network performance and security. There are two key challenges in wireless networks, lossy features of links and power assumption of network nodes. Cross-layer design of congestion control and power allocation in wireless lossy networks has been studied in the existing literature; however, there has been no contribution proposed in the literature that exploits the path diversity. In this paper, we are motivated to develop a cross-layer design of congestion control and power allocation, which takes into account lossy features of wireless links and transmission powers of network nodes and can be implemented in a distributed manner. Numerical simulation is conducted to illustrate the performance of our proposed algorithm and the comparison with current alternative approaches.

• 전기전자학회논문지
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• 제23권3호
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• pp.899-903
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• 2019

네트워크 제어 시스템에서는 송신 신호의 시간 변동 지연이 불가피하다. 전송 지연이 고정된 샘플링 시간보다 길면 시스템이 불안정해진다. 이 문제를 해결하기 위해 본 논문은 논리 기반의 퍼지 신경망을 이용하여 지연을 예측하는 방법을 제안하며, 예측된 시간 지연은 네트워크 제어 시스템의 샘플링 시간으로 사용된다. 제안된 방법의 효과를 검증하기 위해, 실제 시스템에서 수집된 지연 데이터를 사용하여 논리 기반 퍼지 신경 네트워크를 훈련하고 테스트한다.

• 한국통신학회논문지
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• 제35권1B호
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• pp.154-161
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• 2010

무선 센서 네트워크에서는 다대일로 수렴하는 상향 트래픽의 특성으로 인해 네트워크의 혼잡이 빈번히 발생한다. 기존에 제안된 무선 센서 네트워크의 혼잡 제어 기법은 혼잡 발생 시 전송 주기 변경을 통해 혼잡을 회피할 수 있으나 MAC(Medium Access Control) 계층의 듀티사이클 동작에 대한 고려가 부족하였다. 본 논문에서는 무선 센서 네트워크의 혼잡 제어를 위하여 네트워크의 트래픽에 따라 센서 노드의 듀티사이클을 적응적으로 변화시키는 DCA(Duty-cycle Based Congestion Avoidance) 기법을 제안하였다. DCA 기법은 듀티사이클 조절을 이용하여 혼잡 발생 시 수신 노드의 패킷 수신율 증가를 통한 리소스 제어를 수행하고 송신 노드의 패킷 전송률 감소인 트래픽 제어를 수행하여 혼잡을 회피한다. 실험을 통해 DCA 기법은 듀티사이클 기반의 센서 네트워크에서 에너지 효율성으로 동작하며 혼잡 제어로 인해 신뢰성을 향상시킬 수 있음을 확인하였다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1995년도 Proceedings of the Korea Automation Control Conference, 10th (KACC); Seoul, Korea; 23-25 Oct. 1995
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• pp.91-94
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• 1995

A newly developed discrete-time control design method for impact machines is proposed. It is composed of identification and control using neural networks, where the optimal controller with saturationn and no use of velocity measurements is obtained. By computer simulation, the proposed method is demonstrated to be effective: as the training progresses, the cost function becomes smaller, the proposed control is superior to PID control tuned with Ziegler-Nichols (Z-N) parameters; robust performance with respect to uncertainty, disturbances and working time is so good.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2002년도 추계학술대회 논문집 Vol.15
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• pp.573-577
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• 2002

A new estimated neural networks are proposed in order to measure nonlinear road environments in realtime. This new neural networks is Error Estimated Neural Networks. The structure of it is similar to recurrent neural networks; a delayed output as the input and a delayed error between the output of plant and neural networks as a bias input. In addition, we compute the desired value of hidden layer by an optimal method instead of transfering desired values by backpropagation and each weights are updated by RLS(Recursive Least Square). Consequently, this neural networks are not sensitive to initial weights and a learning rate, and have a faster convergence rate than conventional neural networks. We can estimate nonlinear models in realtime by the proposed networks and control nonlinear models. To show the performance of this one, we control 7 degree simulation, this controller and driver were proved to be effective to drive a car in the environments of nonlinear road systems.

• 제어로봇시스템학회논문지
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• 제14권12호
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• pp.1226-1231
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• 2008

A design of attitude controller for a tiltrotor is presented augmenting L1 adaptive control, neural networks, and feedback linearization. The neural networks compensate for the modeling error caused by the lack of knowledge of tiltrotor dynamics while the L1 adaptive control allows high adaptation gains in adaptation laws thereby, satisfying tracking performance requirement. The efficacy of this control methodology is illustrated in high-fidelity nonlinear simulation of a tiltrotor by flying the tiltrotor in different flight modes from where the L1 adaptive controller with neural networks is originally designed for.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1990년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 26-27 Oct. 1990
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• pp.1-6
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• 1990

Neural network is a computational model of ft biological nervous system developed ID exploit its intelligence and parallelism. Applying neural networks so robots creates many advantages over conventional control methods such as learning, real-time control, and continuous performance improvement through training and adaptation. In this paper, dynamic control of a six-link robot will be presented using neural networks. The neural network model used in this paper is the backpropagation network. Simulated control of the PUMA 560 am shows that it can move a high speed as well as adapt to unforseen load changes and sensor noise. The results are compared with the conventional PD control scheme.