• Journal of the Korean Institute of Navigation
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• v.21 no.1
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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.

• Proceedings of the IEEK Conference
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• 2002.07c
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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

• Proceedings of the KIEE Conference
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• 2000.07d
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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.

• Journal of Korea Multimedia Society
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• v.19 no.8
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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.

• Journal of IKEEE
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• v.23 no.3
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• pp.899-903
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• 2019

In networked control systems, time-varying delay of the transmitting signal is inevitable. If the transmission delay is longer than the fixed sampling time, the system will be unstable. To solve this problem, this paper proposes the method to predict the delay using logic-based fuzzy neural networks, and the predicted time delay will be used as a sampling time in the networked control systems. To verify the effectiveness of the proposed method, the delay data collected from the real system are used to train and test the logic-based fuzzy neural networks.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.1B
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• pp.154-161
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• 2010

In wireless sensor networks, due to the many-to-one convergence of upstream traffic, congestion more probably appears. The existing congestion control protocols avoid congestion by controlling incoming traffic, but the duty-cycle operation of MAC(Medium Access Control) layer has not considered. In this paper, we propose DCA(Duty-cycle Based Congestion Avoidance), an energy efficient congestion control scheme using duty-cycle adjustment for wireless sensor networks. The DCA scheme uses both a resource control approach by increasing the packet reception rate of the receiving node and a traffic control approach by decreasing the packet transmission rate of the sending node for the congestion avoidance. Our results show that the DCA operates energy efficiently and achieves reliability by its congestion control scheme in duty-cycled wireless sensor networks.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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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.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2002.11a
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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.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.12
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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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• 1990.10a
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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.