• Nuclear Engineering and Technology
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• v.39 no.1
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• pp.63-74
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• 2007

In this paper, a reconfigurable controller consisting of a normal controller and a standby controller is designed to control the thermoelectric (TE) power in the SP-100 space reactor. The normal controller uses a model predictive control (MPC) method where the future TE power is predicted by using support vector regression. A genetic algorithm that can effectively accomplish multiple objectives is used to optimize the normal controller. The performance of the normal controller depends on the capability of predicting the future TE power. Therefore, if the prediction performance is degraded, the proportional-integral (PI) controller of the standby controller begins to work instead of the normal controller. Performance deterioration is detected by a sequential probability ratio test (SPRT). A lumped parameter simulation model of the SP-100 nuclear space reactor is used to verify the proposed reconfigurable controller. The results of numerical simulations to assess the performance of the proposed controller show that the TE generator power level controlled by the proposed reconfigurable controller could track the target power level effectively, satisfying all control constraints. Furthermore, the normal controller is automatically switched to the standby controller when the performance of the normal controller degrades.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.84-89
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• 2003

Fuzzy controller is one of the succeed controller used in the process control in case of model uncertainties. But it my be difficult to fuzzy controller to articulate the accumulated knowledge to encompass all circumstance. Hence, it is essential to provide a tuning capability. There are many parameters in fuzzy controller can be adapted, scale factor tuning of normalized fuzzy controller is one of the adaptation parameter. Two adaptation methods are implemented in this work on an experimental thermal process, which simulate heating process in liquefied petroleum gases (LPG) recovery process in one of petrochemical industries: Gradient decent (GD) adaptation method; supervisory fuzzy controller. A comparison between the two methods is discussed.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10b
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• pp.1950-1955
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• 1991

A fuzzy dynamic learning controller is proposed and applied to control of time delayed, non-linear and unstable chemical processes. The proposed fuzzy dynamic learning controller can self-adjust its fuzzy control rules using the external dynamic information from the process during on-line control and it can create th,, new fuzzy control rules autonomously using its learning capability from past control trends. The proposed controller shows better performance than the conventional fuzzy logic controller and the fuzzy self organizing controller.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.613-618
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• 1992

We propose a new control architecture which consists of a PI controller and a neural network(NN) controller connected together in parallel. This architecture is well adapted to a wide range of uncertainties and variations of systems. The NN controller is learned through weights of the emulator which identify the dynamic chracteristics of the systems. A performance evaluation level of two NN's decides automatically which controller of the two controllers will be used mainly. The PI controller operates mainly during learning phase of the NN controller whereas a good performance is obtained from the NN controller only, when the NN controller is learned sufficiently.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.5
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• pp.379-383
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• 2003

Parallel type fuzzy controller is designed by using a hybrid connected type fuzzy-PID controller and a model reference fuzzy controller. The first controller, consists of a fuzzy-PI and a fuzzy-PD making a hybrid type fuzzy-PID controller, plays a role as firstly reaching stable responses and secondly overcoming disturbance in plants. The second controller, model reference fuzzy controller, plays a role as reaching faster responses than other controllers. We have confirmed that the controller produces rapid and stable responses and overcomes disturbance by using parallel type fuzzy controller in a DC motor application.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.6
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• pp.466-472
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• 2003

This paper describes a tracking control for the mobile robot based on fuzzy systems. Since the mobile robot has the nonholonomic constraints, these constraints should be considered to design a tracking controller for the mobile robot. One of the well-known tracking controllers for the mobile robot is the back-stepping controller. The conventional back-stepping controller includes the dynamics and kinematics of the mobile robot. The conventional back-stepping controller is affected by the derived velocity reference by a kinematic controller. To improve the performance of the conventional back-stepping controller, this paper uses the fuzzy systems known as the nonlinear controller. The new velocity reference for the back-stepping controller is derived through the fuzzy inference. Fuzzy rules are selected for gains of the kinematic controller. The produced velocity reference has properly considered the varying reference trajectories. Simulation results show that the proposed controller is more robust than the conventional back-stepping controller.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.3
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• pp.192-197
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• 2007

This paper presents Hybrid PI controller of IPMSM drive using fuzzy adaptive mechanism(FAM) control. In general, PI controller in computer numerically controlled machine process fixed gain. They may perform well under some operating conditions, but not all. To increase the robustness, fixed gain PI controller, Hybrid PI controller proposes a new method based self tuning PI controller. Hybrid PI controller is developed to minimize overshoot and settling time following sudden parameter changes such as speed, load torque, inertia, rotor resistance and self inductance. The results on a speed controller of IPMSM are presented to show the effectiveness of the proposed gain tuner. And this controller is better than the fixed gains one in terms of robustness, even under great variations of operating conditions and load disturbance.

• Proceedings of the KIEE Conference
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• 1993.07a
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• pp.51-53
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• 1993

This paper proposes a design of the tracking controller using artificial neural network and the compare the result with a result of optimal controller. In practical use, conventional Optimal controller has some limits. First, optimal controller can be designed only for linear system. Second, for many systems state observation is difficult or sometimes impossible. But the controller using artificial neural network does not need mathmatical model of the system including state observation, so it can be used for both linear and nonlinear system with no additional cost for nonlinearity. Designed multi layer neural network controller is composed of two parts, feedforward controller gives a steady state input & feedback controller gives transient input via minimizing the quadratic cost function. From the comparison of the results of the simulation of linear & nonlinear plant, the plant controlled by using neural network controller shows the trajectory similar to that of the plant controlled by an optimal controller.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.65.2-65
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• 2001

There are not only lots of controllers such as UMC(Unit Master Controller), BMC(Boiler Master Controller), Fuel Flow controller, Air flow controller, Feed water flow controller, S/H R/H Temperature controller and so on, but also compensation controller such as BTU compensator, Fuel/Water ratio controller and O2 Co controller to take automatic control in the super critical once through boiler. It is important to make complete automation of boiler to use the compensation controller like BTU compensator. For example, In case of some boiler condition, operator has to change combustion parameter for changing the coal, on the contrary BTU compensator can calculate set value of the fuel flow and reset the fuel flow demand by itself. This paper shows us the logic and instruction regarding compensation controller of boiler that can be operated automatically.

• Proceedings of the KIPE Conference
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• 1997.07a
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• pp.345-351
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• 1997

In this paper, controller for PWM converter considering unsymetrical input voltage is designed and current controller using LQ controller with conditional integrator is proposed. And the proposed current controller is compared with other current controller-predictive controller, decoupling PI controller. As simulation results, LQ controller with Conditional Integrator shows the improved performance for DC link voltage regulation through transient test of load variation. And when unsymeritrical input voltage is applied to converter with conventional current controller considering only symetrical input voltage, input current is distorted but it is showed that current controller considering unsymetrical input has robust control characteristics under phase voltage unbalance.