• Journal of Institute of Control, Robotics and Systems
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• v.7 no.6
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• pp.509-517
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• 2001

To deal with non-linearities and time-varying characteristics of hydraulic systems, in this paper, the neuro-fuzzy controller has been introduced. This controller does not require and accurate mathematical model for the nonlinear factor. In order to solve general fuzzy inference problems, the input membership function and fuzzy reasoning rules are used for determining the controller parameters. These parameters are determined by using the learning algorithm. The control performance of the neuro-fuzzy controller is evaluated through a series of experiments for the various types of inputs while applying disturbances to the hydraulic system. The performance of this controller was compared with those of PID and PD controllers. From these results, We observe be said that the position tracking performance of neuro-fuzzy is better those of PID and PD controllers.

• Proceedings of the KSME Conference
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• 2000.11a
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• pp.584-589
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• 2000

When the liquid tanks only partially filled and under translational acceleration, large quantities of liquid move uncontrollably inside the tanks and generate the liquid sloshing effect. Liquid sloshing effect could be a severe problem in launch vehicle stability and control if the liquid modes of motion couple significantly with the launch vehicle's normal modes of motion. Several methods have been employed to reduce the effect of sloshing, such as introducing baffles inside the tanks or dividing a large tank into a number of smaller ones. These techniques, although helpful in some cases, do not succeed in canceling the sloshing effects. In this paper, An attitude controller is designed for a launch vehicle with liquid sloshing effect. Both PD controller and sloshing filter are designed for the objective. PD gains and design parameters are determined by optimal algorithm. The performance of the attitude controller is evaluated via computer simulations.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.228-228
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• 2000

To deal with non-linearities and time-varying characteristics of hydraulic systems, in this paper, the neuro-fuzzy controller has been introduced. This controller does not require an accurate mathematical model for the nonlinear factor. In order to solve general fuzzy inference problems, the input membership function and fuzzy reasoning rules are used for determining the controller Parameters. These parameters are determined by using the learning algorithm. The control performance of the neuro-fuzzy controller is obtained through a series of experiments for the various types of input while applying disturbances to the cylinder. .and performance of this controller was compared with that of PID, PD controller. As a experimental result, it can be proven that the position tracking performance of the neuro-fuzzy is better than that of PID and PD controller.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.201-201
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• 2000

Recently in the design of fuel tanks(launch vehicle, ship, automobile) which transport a large amount of liquid in the cargo holds, the structural damage due to liquid-sloshing becomes an important problem. The impact pressure from sloshing is most violent when the liquid motion of a partially filled tank is in resonance with the motion of a system. In this paper, the sloshing natural periods in liquid tanks are estimated for partially filled tanks with various geometries. In addition to, controlled for a launch vehicle with liquid sloshing effect by PD controller and sloshing filter The PD gain and sloshing filter parameter arc determined by optimal algorithm.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2120-2122
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• 2002

There are several types of control systems that use fuzzy logic controller as a essential system component. The majority of research work on fuzzy PID controller focuses on the conventional two-input PI or PD type controller. However, fuzzy PID controller design is a complex task due to the involvement of a large number of parameters in defining the fuzzy rule base. In this paper we combined conventional PI type and PD type fuzzy controller and set the initial parameters of this controller from the conventional PID controller gains obtained by Ziegler-Nichols tuning or other coarse tuning methods. After that, by replacing some of these parameters with sing1e neurons and making them to be adjusted by back-propagation learning algorithm we designed a neuro-fuzzy controller which showed good performance characteristics in both computer simulation and actual application.

• Proceedings of the KIEE Conference
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• 1997.07b
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• pp.702-704
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• 1997

In this paper, we applied self-tuning controller with I-PD type to process with time delay's. Process parameters are estimated by the recursive least squares algorithm, and optimal gains are obtained. This paper shows self-tuning controller with I-PD type performs better than that of general PID type for the nonlinear system with sudden change of set-points.

• Journal of Power System Engineering
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• v.11 no.1
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• pp.134-139
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• 2007

A delivery ship is used to handle the cargo with the crane to/from the ships. The ship is inclined in the direction of a cargo which is hung on a crane. In this case, a arc shaped rail should be in the equilibrium state to get good anti-rolling performance. In this study, a device and control algorithm are developed to take accurate and quick equilibrium of the rail. The device is composed of a hinged immovable support, screw jack and damper. And the control system is based on I-PD control law to consider of control input saturation and overshoot. The controller is composed of integral controller of feedforward path and proportional-derivative controller of feedback path. The parameters of controller is designed to follow the reference signal and to remove overshoot. The simulation results show that the desirable control performance is achieved.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.738-741
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• 1995

This disseration realized the simple digital controller board using ${\mu}$-PD 70320 microprocessor has characteristics that are low cost, simple hardware organization, convenient and interchangeable with the 8086 for the servo system. We gave the control algorithm such as PD control. Self tuning adaptive control and Fuzzy control to the realized controller board and made a new real number data type for a high accuracy control. Users can select of suitable for the control algorithim. In the result of simulation and experiment shown a good performance.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.9
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• pp.76-87
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• 1992

A novel self-organizing fuzzy plus PD control algorithm is proposed, where the proposed controller consists of a typical fuzzy reasoning part and self organizing part in which both on-line and off-line algorithms are employed to modify the Look-Up Table(LUT) for the fuzzy control rules and to decide how much fuzzy rules are to be modifid after evaluating the control performance, respectively. And the fuzzy controller is replaced by a PD controller in a prespecified region nearby the set point for good settling actions, where gain parameters are determined by fuzzy rules based on the magnitude of error velocity at the instant when the output penetrates into the prespecified region. To show the effectiveness of the proposed controller, extensive computer simulation results as well as experimental results are illustrated for an inverted pendulum system.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2003.06a
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• pp.169-172
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• 2003

PID control has been well used for several decade. For PID algorithm, some tuning methods are used and with these parameters, control system is designed. But in some cases various kinds of performance are needed, so variable type of performance index must be utilized so that the designed control system meets the conditions. This paper presents some linear combinational type of performance index and with numerical methods, the PID parameters are obtained. Moreover I-PD type controller is used so that this two degree of freedom controller may give more desirable output characteristics. Simulations are done with MATLAB m file and mdl files.