• Journal of Institute of Control, Robotics and Systems
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• v.8 no.8
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• pp.713-718
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• 2002

Motion controllers are essential components for operating industrial equipments. Compared with general industrial controllers, motion controllers allow motion control requiring greater speed and precision. This paper presents a method for controlling multi-axes motors via industrial networks. To achieve a line or arc interpolation, the master system delivers instructions to slave systems connected to the network. The network instruction transmitted from the master controller is re-interpolated by the individual slaves through sub-interpolators. The re-interpolated feedrate information is transmitted to the motion control loop in which the current position and the reference position are then calculated. In this way, the interpolation driving between control units is achieved via industrial networks.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.11
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• pp.1100-1106
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• 2004

In this report, a self-organizing fuzzy neural network is proposed to control chemical feeding, which is one of the most important problems in water treatment process. In the case of the learning according to raw water quality, the self-organizing fuzzy network, which can be driven by plant operator, is very effective, Simulation results of the proposed method using the data of water treatment plant show good performance. This algorithm is included to chemical feeder, which is composed of PLC, magnetic flow-meter and control valve, so the intelligent control of chemical feeding is realized.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1014-1019
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• 2005

In this paper, a neural network technique for automatic steering control of a four wheel drive autonomous highway snowplow vehicle is presented. Controllers are designed by the LQR method based on the vehicle model. Then, neural network is used as an auxiliary controller to minimize lateral tracking error under the presence of load. Simulation studies of LQR control and neural network control are conducted for the vehicle model under a virtual snowplowing situation. Tracking performances are also compared for two and four wheeled steering vehicles.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2296-2300
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• 2005

This paper deals with the real-time message network system by a CAN (controller area network) based on the real-time distributed control scheme to integrate actuators and sensors in a humanoid robot. In order to apply the real-time distributed processing for a humanoid robot, each control unit should have the real-time efficient control method, fast sensing method, fast calculation and real-time valid data exchange method. Moreover, the data from sensors and encoders must be transmitted to the higher level of control units in maximum time limit. This paper describes the real-time message network system design and the performance of the system.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1598-1601
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• 2005

In this paper, a heterogeneous home network control system using HNCP is proposed and implemented. A power line and 802.15.4 are used as media for the system. Information about home environment gathered by sensors is transferred to a power line connected device through the 802.15.4. HNCP stimulate the home network based on the both media. Sensor device definition for the HNCP address and message set is proposed. TinyOS supports the HNCP stack on the wireless sensor board. The home network control system implemented with these techniques has a benefit of user friendly operation of home appliances based on the sensing data. Implementation and experiment shows validity of the system.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2001.04a
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• pp.598-601
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• 2001

As many sensors and actuators are used in many automated system, various industrial networks are adopted for digital control system. In order to take advantages of the networking, however, the network implementation should be carefully designed to satisfy real-time requirements considering network delays. This paper presents the implementation scheme of a networked control system via Profibus-DP network. More specifically, the effect of the network delay on the control performance was evaluated on a Profibus-DP testbed, and a GA based PID tuning algorithm is proposed to demonstrate the fesibility of the networked control system.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.315-318
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• 2005

This paper characterizes the performance for a remote path tracking control of the mobile robot in IP network viamiddleware. The middleware is used to alleviate the effect of the delay time on a mobile robot path tracking in Network-Based Control environment. The middleware also can be implemented in a modular structure. Thus, a controller upgrade or modification for other types of network protocols or different control objectives can be achieved easily. A case study on a mobile robot path-tracking with IP network delays is described. The effectiveness of the proposed approach is verified by experimental results.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.261-264
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• 1992

This paper proposes a neuro-fuzzy adaptive controller which includes the procedure of initializing the identification neural network(INN) and that of learning the control neural network(CNN). The identification neural network is initialized with the informations of the plant which are obtained by a fuzzy controller and the control neural network is trained by the weight informations of the identification neural network during on-line operation.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.7-12
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• 1990

In this paper, we propose a new manipulator control scheme based on the CMAG neural network. The proposed control consists of two components. The feedforward component is an output of trained CMAC neural network and the feedback component is a modified sliding mode control. The CMAC accepts the position, velocity and acceleration of manipulator as input and outputs two values for the controller : One is the nominal torque used for feedforward compensation(M1 network) and the other is the inertia matrix related information used for the feedback component(M2 network). Since the used control algorithm guarantees the robust trajectory tracking in spite of modeling errors, the CMAC mapping errors due to the memory limitation are little worth consideration.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2003.10a
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• pp.945-948
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• 2003

In this paper, we propose a robust PID control method with neural network to minimize the influence of the disturbance to happen in the system. The proposed method, the neural network filters out the disturbance of control system. The plant input which a disturbance is included is compensated to the output of neural network and the plant is controlled only PID controller. Through the DC motor control simulation and MM-LDM position control experiment, we could confirm the proposed method is robust at the disturbance in control system.