• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 1999.11a
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• pp.262-266
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• 1999

In this paper, we consider to apply of 2-DOF (Degree of Freedom) PID controller at D.C servo motor system. Many control system use I-PD, PID control system, but the position control system have difficulty in controling variable load and changing parameter. We propose neural network 2-DOF PID control system having feature for removal disturbrances and tracking function in the target value point. The back propagation algorithm of neural network used for tuning the 2-DOF parameter ($\alpha$, $\beta$, ${\gamma}$, η). We investigate the 2-DOF PID control system in the position control system and verify the effectiveness of proposal method through the result of computer simulation.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.9
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• pp.948-955
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• 2008

The conventional PID controller has been widely employed in industry. However, the PID controller with one degree of freedom(DOF) can not optimize both set-point tracking response and disturbance rejection response at the same time. In order to solve this problem, a few types of 2DOF PID controllers have been suggested. In this paper, a tuning formula for a 2DOF PID controller is presented. The optimal parameter sets of the 2DOF PID controller are determined based on the first-order plus time delay process and a real-coded genetic algorithm(RCGA) such that the ITAE performance criterion is minimized. The tuning rule is then addressed using calculated parameter sets and another RCGA. A set of simulation works are carried out on three processes with time delay to verify the effectiveness of the proposed rule.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2000.11a
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• pp.159-167
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• 2000

During the operation of crane system in the container yard, it is necessary to control the crane trolley position so that the swing of the hanging container is minimized. Recently an automatic control system with high speed and rapid transportation is required. Therefore, we designed a controller to control the crane system with disturbances and weight change. In this paper, we present the neural network two degree of freedom PID controller to control the swing motion and trolley position. Then we executed the computer simulation to verify the performance of the proposed controller and compared the performance of the neural network PID controller with our proposed controller in terms of the rope swing and the precision of position control. Computer simulation results show that the proposed controller has better performances than neural network PID with disturbances.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.6
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• pp.744-752
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• 1999

In this paper, we studied the response characteristics of $\alpha$, $\beta$ separated type, combined type, PI typed, and feedforward type in 2DOF-PID controller through the simulation and the experiments designed with the multivariable flow control system. The parameters $\alpha$ and $\beta$ give an affect to characteristics of controller in separated type but $\gamma$ does not give an affect to the characteristics of 2-DOF PID. The more $\beta$ increases, the more overshoot decreases and especially, in case of PI type represent clearly. The $\alpha$, $\beta$ separated type has a very small overshoot and its magnitudes in 2-DOF PID onctroller increases in order of $\alpha$, $\beta$ combined type, PI type, feedforward type, conventional type. The response characteristics of simulation are similar to that of experiments but the experimental characteristics in the multivariable flow control system has the delayed response. The time delay of response in experiments depends on 2-DOF parameter $\alpha$, $\beta$, $\gamma$ and the overshoot increase as the $\alpha$, $\beta$, $\gamma$ increase. So, we can have a satisfactory response by tuning D gain.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 1998.10a
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• pp.153-158
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• 1998

In this paper, we present a design of optimal 2-DOF PID controller for control of gantry crane which has to control swing motion and trolley position. For tuning the parameter of 2-DOF PID controller, we used evolution strategy(ES). During operate the crane system in yard, the goal is transporting the load to a goal position as quick as possible without rope oscillation. The crane is generally operated by an expert operator, but recently an automatic control system with high speed and rapid transportation is required. However, we developed an optimal controller which has to control the crane system with disturbance.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.6
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• pp.927-934
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• 2017

This paper is concerned with applicability of two-degree-of-freedom controllers to the recently suggested i-PID controllers, in which unknown parts of the plant are taken into account without any modeling procedure. First, i-PID controller with two-degree-of-freedom is applied to a specific model, called Anisochronic model, to confirm the usefulness of this method. Second, using the original examples of i-PID controllers, it is confirmed that performance/robustness of system are to be improved due to two-degree-of-freedom, especially when the input changes suddenly. It is seen that as the desired robustness increases the optimal value of two-degree-of-freedom parameter ${\alpha}_A$ would be negative. It is checked and verified that if this value was limited to 1 or less as is generally known, performance would be degraded.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.9
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• pp.1202-1209
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• 2018

This paper is concerned with the problem of setting controller's parameters when applying the intelligent PID (i-PID), which has recently been proposed and had many successful results, to the two-degree-of-freedom (2DoF) PID controller structure. Generally, the parameter settings of conventional PID controllers are known to be quite difficult and be dependent on the characteristics of the plants. In addition, it is less known how the two 2DoF parameters are set up for the improvement of transient characteristics. Here, we are going to present one of the criteria for parameter setting in the case of using a 2DoF i-PID, by evaluating the error signals to the set-point and disturbance. That is, we first, obtain parameters of i-PID by optimizing the disturbance responses, and then determine two parameters of 2DoF component through optimizing set-point response. The standard values of all parameters are calculated for the 7 types of test batches and rounded up as a table.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.3
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• pp.321-328
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• 1998

When we control the level of the steam generator in the nuclear power plants, a swell and shrink arises from many disturbances such as feed water rate, feed water temperature, main steam flow rate, and coolant temperature. If we use the conventional type of PI controller in this system, we will not have stability during controlling at lower power, the removal function of disturbances, and a load follow-up control effectively. In this paper, we study the application of a 2-Degree of Freedom(2-DOF) PID controller to the level control of the steam. generator of nuclear power plants through the simulation and the experimental steam generator. We use the parameters $\alpha$, $\beta$, $\gamma$ of the 2-DOF PID controller for the removal of disturbances and the parameters Kp,Ti,Td of the conventional type of PID controller for controlling setpoint. The back-propagation learning algorithm of neural network is used for tuning the 2-DOF PID controller. We can find satisfactory results of the removal of the disturbances and the tracking function in the change of setpoint through the simulation and experimental steam generator.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.781-786
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• 1998

Since a gas turbine is still a significant contributor to peak time, it is very important to tune the gains of P. I. D to get a maximum power and stability within permissible limits. In the gas turbine, the main control loop must adjust the fuel flow to ensure the correct output power and frequency. but it is not easy, because the control loop is composed of many subsystems. In this paper we acquire a transfer function based on the operations data of Gun-san gas turbine and study to apply a loop compensation type 2-DOF PID controller tuning by neural-network to control loop of gas turbine to reduce phenomena caused by integral and derivative actions through simulation. We obtained satisfactory results to disturbances of subcontrol loop such as, fuel flow, air flow, turbine extraction temperature.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.12 no.1
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• pp.78-84
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• 1998

The fluid system such as, the quantity control of raw water, chemicals control in the purification, the waste water system as well as in the feed water or circulation system of the power plant and the ventilation system is controlled with the valve and moter pump. The system's performance and the energy saving of the fluid systems depend on control of method and delicacy. Until, PI controller use in these system but it cannot control delicately because of the coupling in the system loop. In this paper we configure a single flow system to the multi variable system and suggest the application of 2-DOF PID controller and the tuning methods by the neural network to the electrical power of the flow control system. the 2-DOF controller follows to a setpoint has a robustness against the disturbance in the results of simulation. Keywords Title, Intelligent control, Neuro control, Flow control, 2 - DOF control., 2 - DOF control.