• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1371-1376
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• 2003

In the thermal power plant, the main steam temperature is typically regulated by the fuel flow rate and the spray flow rate, and the reheater steam temperature is regulated by the gas recirculation flow rate. However, Strictly maintaining the steam temperature can be difficult due to heating value variation to the fuel source, time delay changes in the main steam temperature, the change of the dynamic characteristics in the reheater. Up to the present time, PID Controller has been used to operate this system. However, it is very difficult to achieve an optimal PID gain with no experience, since the gain of the PID controller has to be manually tuned by trial and error. This paper focuses on tuning of the 2-DOF PID Controller on the DCS for steam temperature control using immune based multiobjective approach. The stable range of a 2-DOF parameter for only this system could be found for the start-up procedure and this parameter could be used for the tuning problem. Therefore tuning technique of multiobjective based on immune network algorithms in this paper can be used effectively in tuning 2-DOF PID controllers.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.10
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• pp.933-939
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• 2007

Safety critical systems such as x-by-wire systems require in-vehicle network systems that can interconnect various sensors, actuators, and controllers. These networks need to have high data rate, deterministic operation, and fault tolerance. Recently, FlexRay protocol that is a time-triggered protocol has been introduced, and many automotive companies have been focusing on this protocol. This paper presents a design method of FlexRay network system and implementation of FlexRay-based motor control system.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.169-172
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• 2005

Tiny camera module using in modern cellular phone requires precise assembly processes. Higher camera resolution and more functions such as zoom lens make the number of camera parts bigger. As market grows rapidly, automatic assembly process is required. However, diverse product line and short life cycle make it difficult. To attack this, a flexible and expandable lens assembly system is proposed. For the fast manufacturing line formation, modular concept is adopted. Also each module is designed to have intelligence to save system formation time. The assembly system is built up on the standard flat-form which provides vibration free base, air and electric supply, controllers, etc. Futhermore, the assembly cell has the capability of handling tiny, thin, or transparent parts which are very difficult to align with vision.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.8
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• pp.689-696
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• 2000

In this paper we propose an eminent controller for wheeled mobile robots. This controller consists of an input-output linearization controller trying to stabilize the system and a neural network controller to compensate for uncertainties. The uncertainties are divided into two parts. First unstructured uncertainties include the elements related with system order such as friction disturbance. Second structure uncertainties are the incorrect system parameters A neural network structure of the proposed overall controller learns structural errors of the wheeled mobile robots with uncertainties and includes the neural network output. This controller learns quickly the model and has good tracking performance Simulation results show that the proposed controller is more efficient than analog controllers.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.2
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• pp.127-137
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• 2000

This paper presents a novel neural based controller which controls the water level of the nuclear power plant steam generator. The controller consists of a model reference feedback linearization controller and a PI controller for stabilizing the feedback linearization controller. The feedback linearization controller consists of a neural network model and an inversing module which uses the neural network model for computing the control input to the steam generator. We chose Piecewise Linearly Trained Network(PLTN) and Recurrent Neural Netwrok(RNN) for an approximator of the plant and used these approximators in calculating the input from the feedback linearization controller. Combining the above two controllers gives a result of better performance than the case which uses only a PI controller Each control result of PLTN and RNN is given.

• Proceedings of the KIPE Conference
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• 2000.07a
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• pp.615-619
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• 2000

This paper presents an implementation of digital motion control system of Surface Permanent-Magnet Synchronous Motor(SPMSM) vector drives with a direct torque control(DTC) using the 16bit DSP TMS320F240 The DSP controller enable enhanced real time algorithm and cost-effective design of intelligent control for motors which can be yield enhanced operation fewer system components lower system cost increased efficiency and high performance The system presented are stator flux and torque observer of stator flux feedback model that inputs are current and voltage sensing of motor terminal and angle for a low speed operating area two hysteresis band controllers an optimal switching look-up table and IGBT voltage source inverter by using fully integrated control software. The developed control system are shown a good motion control response characteristic results and high performance features using 1.0Kw purposed servo drive SPMSM.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.1703_1704
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• 2009

Fuzzy neural ship controllers is used in ship steering control. It can make full use of the advantage of all kinds of intelligent algorithms. This provides an efficient way for this paper. An RBF neural network and GA optimization are employed in a fuzzy neural controller to deal with the nonlinearity, time varying and uncertain factors. Utilizing the designed network to substitute the conventional fuzzy inference, the rule base and membership functions can be auto-adjusted by GA optimization. The parameters of neural network can be decreased by using union-rule configuration in the hidden layer of the network. The ship control quality is effectively improved in case of appending additional sea state disturbance. The performance of controller is evaluated by the system simulation using simulink tools.

• Journal of the Korean Society of Industry Convergence
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• v.3 no.1
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• pp.9-15
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• 2000

This paper presents a implementation of digital motion control system for induction motor vector drives using the 16bit DSP TMS320F240. The DSP controller enable enhanced real time algorithm and cost-effective design of intelligent controllers for motors which can be yield enhanced operation, fewer system components, lower system cost, increased efficiency and high performance. The system presented are speed and current sensing, sine look-up table and generated SVPWM by fully integrated control software. The developed system in a implementation are shown a good speed response and motion control characteristic results, and high performance features in general purposed 2.2[kW] machine. The system can be adapted variform motor drive system.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.461-465
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• 1996

Position and rate control modes arc the two common modes for controlling remote manipulators with joysticks or hand controllers. Generally, position mode is easier for teleoperation than rate modes, when the manipulation work space is small or comparable to the human operator's control space. When the telemanipulator's work space is very large, human operator's control motion range must be large to allow telemanipulator's full range of motion resulting poor control resolution. One way to solve the poor resolution problem is to use indexing. However, rate mode can provide any higher degree of resolution without use of indexing. If two modes are mixed, master controller will be more convenient. The mixed mode algorithm, changes operating mode from position mode to rate mode or vise versa using fuzzy logic. The fuzzy logic algorithm, which has been designed to recognize the teleoperator's intended motion properly, provides an intelligence to a master controller.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.2
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• pp.139-144
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• 2011

We consider the stabilization problem for a class of networked control systems with random delays in the discrete-time domain. The controller-to-actuator and sensor-to-controller time-delays are modeled as two Markov chains, and the resulting closed-loop systems are Markovian jump nonlinear systems with two modes. The T-S (Takagi-Sugeno) fuzzy model is employed to represent a nonlinear system with Markovian jump parameters. The aim is to design a fuzzy controller such that the closed-loop Markovian jump fuzzy system is stochastically stable. The necessary and sufficient conditions on the existence of stabilizing fuzzy controllers are established in terms of LMIs (Linear Matrix Inequalities). It is shown that fuzzy controller gains are mode-dependent. Finally, a simulation example is presented to illustrate the effectiveness of the proposed design method.