• The KIPS Transactions:PartD
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• v.14D no.6
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• pp.649-656
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• 2007

Today, controller area network(CAN) is a field bus that is nowadays widespread in distributed embedded systems due to its electrical robustness, low price, and deterministic access delay. However, its use safety-critical applications has been controversial due to dependability limitation, such as those arising from its bus topology. Thus it is important to analyze the performance of the network in terms of load of data bus, maximum time delay, communication contention, and others during the design phase of the controller area network. In this paper, a simulation algorithm is introduced to evaluate the communication performance of the vehicle network and apply software base fault injection techniques. This can not only reduce any erratic implementation of the vehicle network but it also improves the reliability of the system.

• Transactions of the Korean Society of Automotive Engineers
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• v.14 no.5
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• pp.47-57
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• 2006

This paper presents a network-based traction control system(TCS), where several electric control units (ECUs) are connected by a controller area network(CAN) communication system. The control system consists of four ECUs: the electricthrottle controller, the transmission controller, the engine controller and the traction controller. In order to validate the traction control algorithm of the network-based TCS and evaluate its performance, a Hardware-In-the-Loop Simulation(HILS) environment was developed. Herein we propose a new concept of the HILS environment called the network-based HILS(Net-HILS) for the development and validation of network-based control systems which include smart sensors or actuators. In this study, we report that we have designed a network-based TCS, validated its algorithm and evaluated its performance using Net-HILS.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2328-2330
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• 2003

This paper presents a new design methodology of networked control system using CAN(Controller Area Network). Feedback control systems having control loops closed through a network are called networked control systems. We design CAN nodes which can transmit control and monitoring data through network bus and apply these to networked control system design. We analyze the variation of stability property according to network-induced delay and determine a proper sampling period of networked control system that preserves stability performance. The results of the experimental example validate effectiveness of our networked control system.

• Proceedings of the KIEE Conference
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• 2004.05a
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• pp.77-79
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• 2004

Because robot hardware architecture generally is consisted of a few sensors and motors connected to the central processing unit, this type of structure is led to time consuming and unreliable system. For analysis, one of the fundamental difficulties in real-time system is how to be bounded the time behavior of the system. When a distributed control network controls the robot, with a central computing hub that sets the goals for the robot, processes the sensor information and provides coordination targets for the joints. If the distributed system supposed to be connected to a control network, the joints have their own control processors that act in groups to maintain global stability, while also operating individually to provide local motor control. We try to analyze the architecture of network-based humanoid robot's leg part and deal with its application using the CAN(Controller Area Network) protocol.

• Journal of Advanced Marine Engineering and Technology
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• v.23 no.4
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• pp.480-487
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• 1999

In this paper we propose a neural network precompensated PID(NNP PID) controller for load frequency control of 2-area power system. While proportional integral derivative(PID) controllers are used in power system they have many problems because of high nonlinearities of the power system So a neural network-based precompensation scheme is adopted into a conventional PID controller to obtain a robust control to the nonlinearities. The applied neural network precompen-sator uses an error back-propagation learning algorithm having error and change of error as inputand considers the changing component of forward term of weighting factor for reducing of learning time. Simulation results show that the proposed control technique is superior to a conventional PID controller and an optimal controller in dynamic responses about load disturbances. The pro-posed technique can be easily implemented by adding a neural network precompensator to an existing PID controller.

• Journal of IKEEE
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• v.24 no.4
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• pp.1184-1187
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• 2020

This paper proposes an in-vehicle network controller fully exploiting the advantages of UART (Universal Asynchronous Receiver/Transmitter) and CAN (Controller Area Network). UART is used in 1-to-1 communication and it exploits parity bit for data integrity check. The proposed in-vehicle network controller converts UART into CAN, which enables multiple communications along with 1-to-1 communication. Also, the proposed in-vehicle network controller exploits CRC (cyclic redundancy check) for data integrity check, which increases communication reliability. CAN is controlled by microprocessor, but the proposed in-vehicle network controller can be controlled by any devices compliant with RS-232, RS-422, and RS-485.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.1
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• pp.145-152
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• 2016

The usage of LED(Light Emitting Diode) has been rapidly increased and energy efficient management of LED light system is regarded as an important technology to enhance the energy efficiency. In this paper, we design and implementation of LED lighting controller which can control the LED lighting by using the wire and wireless communication technology based on the LAN(Local Area Network) and PAN(Personal Area Network). The implemented system can reduce the cost of LED lighting installing and avoids complicated wiring problem. And it is possible to minimize power consumption through automatic or manual LED control that they wished. In experimental results, the implemented LED lighting controller was satisfactory for all of the desired functions.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2242-2244
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• 2003

This paper considers a networked control system (NCS) that consists of an inverted cart pendulum, a digital controller, and a controller area network (CAN) in which the actuator and sensors of the pendulum are connected to form a closed-loop system. The worst-case message response time (WCMRT) in the CAN is analyzed and the analysis results are applied to the target control system. For the case where the control system cannot satisfy the WCMRT condition and therefore time delays are inevitable, the Luck and Ray method is used to compensate the network-induced time delays. Simulations are carried out to show the feasibility of the proposed scheme.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2001.06a
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• pp.29-32
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• 2001

This paper describes a development of the integrated controller system for car electrical signal control with CAN communication protocol. The CAN protocol is a robust serial bus system for the control of distributed module in the multiplexed network. After a brief of the main features of the CAN will be addressed, this paper presents the result of the development of the integrated hardware system overall control program.

• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.476-479
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• 1992

This paper presents a neural network method for tuning PlD controller of a time-varying process. Three gains of PlD controller are tuned for a certain desirable response pattern by back-propagation neural network. The neural network is trained using changes of output features vs. changes of PlD gains. But sometimes it needs longer training time and larger structure to train the correlation between the process and controller on entire region of the process. The difficulty in system identification is that the inverse function of the system can not be clearly stated. To cope with the problem, we do not train the neural network to respond correctly for the entire regions but train for only local region where the system is heading toward by training the neural network and tuning of the PlD controller. It may be trained for fine-tuning itself. Simulation results show that the adaptive PID controller using neural network trained in the local area performs remarkably for time-varying second order process.