• Proceedings of the IEEK Conference
• /
• 2000.06e
• /
• pp.47-50
• /
• 2000

In this paper, we propose PID type direct control method using single neuron neural network. The proposed method has an output error and 2 time-delay as inputs and is designed to have input weights composed of P, I, D parameters to be controlled through teaming. We could verify the better performance of this system than the conventional method through simulations. And the reduced calculation, due to single neuron, makes it possible the real time processing, and the simple implementation.

• 제어로봇시스템학회:학술대회논문집
• /
• 2000.10a
• /
• pp.238-238
• /
• 2000

In this paper, to increase the utilization of uranium resources contained in the spent fuel, the spent fuel is reused. for this, the spent fuel is dismantled or spent fuel rod is extracted from the spent fuel assembly. Therefore, to achieve the performance of compacting the spent fuel assembly, we proposed the controller consisting of adaptive and fuzzy with teaming algorithm. In order to show the performance of proposed algorithm compares, we compared the controller with conventional controller in plant.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
• /
• 2003.10a
• /
• pp.219-226
• /
• 2003

In this paper, it is presented a new scheme of adaptive-neuro control system to implement real-time control of robot manipulator. Unlike the well-established theory fir the adaptive control of linear systems, there exists relatively little general theory fir the adaptive control of nonlinear systems. Adaptive control technique is essential fir providing a stable and robust performance fir application of robot control. The proposed neuro control algorithm is one of teaming 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 f3r real-time control of robot system using DSPs.

• Transactions of the Korean Society of Machine Tool Engineers
• /
• v.15 no.5
• /
• pp.114-122
• /
• 2006

This paper presents a new control scheme that combines a sliding mode control and a neural network. In the proposed sliding mode control, a continuous control is employed removing the switching phenomena and the equivalent control within the boundary layer is estimated through on-line teaming of the neural network. The performances of the proposed control are compared with off-line neural network and on-line neural sliding mode control by computer simulation. The simulation results show that the proposed control reduces high frequency chattering and tracking error in example of the two link manipulator.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2009.05a
• /
• pp.733-736
• /
• 2009

Power Line Communication(PLC) is need not additional communication line. So establishment expense is inexpensive and application is simple. Therefore, lower part network of various application field is possible. However, there are high subordinate interference and noise problem on limited transmission data and communication interference element. Wireless Sensor Network(WSN) is need not infrastructure, Self-regulating network architecture of sensor nodes is possible. So at short time, network construction is available. But, power consumption is increased by active sensing for QoS elevation and unnecessary information transmission, low electric power design and necessity of improve protocol are refered to life shortening problem and is studied. In this paper, supplement problem of power line communication and wireless sensor network mutually and because advantage becomes linkage integration control system using synergy effect of two technologies as more restriction be and tries to approach structurally control network that is improved for smooth network environment construction. Honeywell's hybrid sensor network does comparative analysis(benchmarking). Confirm performance elevation proposing teaming of power line communication and wireless sensor network. Through simulation, service delay decreases and confirms that performance elevation.

• Journal of the Korean earth science society
• /
• v.24 no.3
• /
• pp.160-171
• /
• 2003

This study was designed to analyze the types of concepts about earth science related to seasonal changes, so as to develop a generative learning model focused on dissolving cognitive conflicts between the aforementioned concepts through debates and using said debates to find out how effectively the model works. There are 100 types of earth science concepts concering seasonal changes, 66 of which are unscientific in nature, including misconceptions. Through a second field trial and a research and development (R&D) process, a test on these concepts was developed, consisting of 14 items. For the experimental group, a four-phase generative learning strategy that reflects the types of earth science concepts and cognitive conflicts between such concepts was developed through pre-analysis and discussion, respectively. On the other hand, a traditional teaching and teaming strategy was used for the control group. A meaningful statistic gap found between the two groups through a covariance analysis, the significance level of which was 0.05. This result may be interpreted to mean that the generative teaming strategy is a possible alternative for correcting misconceptions about scientific concepts of seasonal changes.

• Journal of Institute of Control, Robotics and Systems
• /
• v.11 no.11
• /
• pp.930-935
• /
• 2005

In this paper, position tracking control of an autonomous helicopter is presented. Combining an LQR method and a proportional control forms a simple PD control. Since LQR control gains are set for the velocity control of the helicopter, a position tracking error occurs. To minimize a position tracking error, neural network is introduced. Specially, in the frame of the reference compensation technique for teaming neural network compensator, a position tracking error of an autonomous helicopter can be compensated by neural network installed in the remotely located ground station. Considering time delay between an auto-helicopter and the ground station, simulation studies have been conducted. Simulation results show that the LQR with neural network performs better than that of LQR itself.

• Journal of Institute of Control, Robotics and Systems
• /
• v.8 no.12
• /
• pp.1023-1030
• /
• 2002

Since the dynamics of autonomous underwater vehicles (AUVs) are highly nonlinear and their hydrodynamic coefficients vary with different vehicle's operating conditions, high performance control systems of AUVs are needed to have the capacities of teaming and adapting to the variations of the vehicle's dynamics. In this paper, a linearly parameterized neural network (LPNN) is used to approximate the uncertainties of the vehicle dynamics, where the basis function vector of the network is constructed according to the vehicle's physical properties. The network's reconstruction errors and the disturbances in the vehicle dynamics are assumed be bounded although the bound may be unknown. To attenuate this unknown bounded uncertainty, a certain estimation scheme for this unknown bound is introduced combined with a sliding mode scheme. The proposed controller is proven to guarantee that all signals in the closed-loop system are uniformly ultimately bounded (UUB). Numerical simulation studies are performed to illustrate the effectiveness of the proposed control scheme.

• Journal of Institute of Control, Robotics and Systems
• /
• v.9 no.6
• /
• pp.435-441
• /
• 2003

This paper addresses that an approximation and tracking control of realtime recurrent neural networks(RTRN) using two-dimensional iterative teaming algorithm for an electro-hydraulic servo system. Two dimensional learning rule is driven in the discrete system which consists of nonlinear output fuction and linear input. In order to control the trajectory of position, two RTRN with the same network architecture were used. Simulation results show that two RTRN using 2-D learning algorithm are able to approximate the plant output and desired trajectory to a very high degree of a accuracy respectively and the control algorithm using two identical RTRN was very effective to trajectory tracking of the electro-hydraulic servo system.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 1996.11a
• /
• pp.439-443
• /
• 1996

A fuzzy teaming controller is experimentally designed to control the ball k beam system in this paper. Although most fuzzy controllers have been built just to emulate human decision-making behavior, it is necessary to construct the rule bases by using a learning method with self-improvement when it is difficult or impossible to get them only by expert's experience. The algorithm introduces a reference model to generate a desired output and minimizes a performance index function based on the error and error-rate using the gradient-decent method. In our balancing experiment of the ball ＆ beam system, this paper shows that the fuzzy control rules by learning are superior to the expert's experience.