• Proceedings of the KIEE Conference
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• 1999.07c
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• pp.1314-1317
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• 1999

In this paper, we design an Adaptive Neuro-Fuzzy Inference System(ANFIS) Precompensator for the performance improvement of conventional proportional integral derivative (PID) controller that the governor system of power plant constantly maintains the load frequency of two-area power system. The ANFIS Precompensator is expressed as the membership functions of premise parameters and the linear combination of consequent parameters by Sugeno's fuzzy if-then rules using nonlinear input-output relation for the set point automatic modification maintaining conventional PID controller. The proposed compensation design technique is hoped to be satisfactory method overcome difficulty of exact modelling and arising problems by the complex nonlinearities of power system, and our design shows merit that is easily implemented by adding an ANFIS precompenastor to an existing PID controller without replacement.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.39 no.7
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• pp.618-626
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• 2011

In order to complete missions in a complicated terrain or highly dangerous area, an unmanned aerial vehicle(UAV) needs a fine controller to precisely follow the desired path. A PID controller used for the path following feeds forward path curvature information to the control input to improve the path following performance. High gain for PID controller is necessary to follow path tightly. However the high gain could cause instability or performance degradation when the vehicle has slow dynamics. We present PID controller design method which considers response delay of vehicle as well as path curvature. In order to obtain path curvature the desired path is described as a 3rd order polynomial by applying cubic spline interpolation. We apply the proposed controller to the path following of a UAV which is operated in high altitude and has very slow lateral dynamics. The lateral dynamics are modelled as a first order delayed system in the controller design. Nonlinear simulation shows the UAV with proposed controller follows an arbitrary path very tightly.

• Journal of Power System Engineering
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• v.19 no.3
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• pp.48-54
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• 2015

This paper introduced a design method of soldering station that can control two types of soldering iron heater. At design time, to distinguish two types of soldering iron heater the voltage divider rule has been applied, and the distinguished resistor is inserted to handler of soldering iron to prevent misuse by users. And an algorithm to design a PID controllers is proposed. The proposed controllers parameter which can be easily realized, and are designed by using the input output data from systems, and have outstanding ability making the output response of nonlinear systems similar to the desired one. Temperature control experiments were performed to verify the ability of the suggested controller. As a result, suggested PID controller followed the desired ones, and one soldering station can control the various type of soldering irons in real time.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.11a
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• pp.748-753
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• 1996

The contact roll is often used to regulate the winding tension as well as the entrained air in a wound roli by adjusting the contact force to the winding roll. But the contact force generated by other rolls, like assistant nip rolls, in a winding(or roll changing) process may act as disturbance to the control of the winding tension. In this paper, the mechanism of a roll change process is analyzed. And, the behavior of the contact and the assistant nip rolls are mathematically modeled. A nonlinear PID(NPID) controller is designed to control the winding tension and to reject the effect of disturbance generated by the assistant nip roll on the winding tension variation. Computer simulation study showed that the performance of the suggested NPID controller is improved compared with that of the PID controller in con trolling the winding tension and in rejecting the effect of the disturbance.

• Journal of Mechanical Science and Technology
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• v.20 no.11
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• pp.1823-1833
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• 2006

An immune algorithm is a kind of evolutional computation strategies, which is developed in the basis of a real immune mechanism in the human body. Recently, scientific or engineering applications using this scheme are remarkably increased due to its significant ability in terms of adaptation and robustness for external disturbances. Particularly, this algorithm is efficient to search optimal parameters against complicated dynamic systems with uncertainty and perturbation. In this paper, we investigate an immune algorithm embedded Proportional Integral Derivate (called I-PID) control, in which an optimal parameter vector of the controller is determined offline by using a cell-mediated immune response of the immunized mechanism. For evaluation, we apply the proposed control to mitigation of vibrations for nonlinear structural systems, cased by external environment load such as winds and earthquakes. Comparing to traditional controls under same simulation scenarios, we demonstrate the innovation control is superior especially in robustness aspect.

• International Journal of Aeronautical and Space Sciences
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• v.5 no.1
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• pp.30-38
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• 2004

This paper deals with a problem of automatic landing guidance and control ofthe longitudinal airplane motion under the wind shear turbulence. Adaptive gainscheduled PID control law is proposed in this paper. Fuzzy logic is the main part ofthe adaptive PID controller as gain scheduler. To illustrate the successful applicationof the proposed control law to the automatic landing control problem, numericalsimulation is carried out based on the longitudinal nonlinear airplane model excited bythe wind shear turbulence. The simulation results show that the automatic landingmaneuver is successfully achieved with the satisfactory performance and the gainadaptation of the control law is made adequately within the limited gains.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.2
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• pp.209-214
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• 1998

In this paper, we are dealing with the problem of controlling unknown nonlinear dynamical system by using neural networks. A novel error self-recurrent(ESR) neural model is presented to perform black-box identification. Through the various outcome of the experiment, a new neural network is seen to be considerably faster than the BP algorithm and has advantages of being less affected by poor initial weights and learning rate. These characteristics make it flexible to design the controller in real-time based on neural networks model. In addition, we design an adaptive PID controller that Keyser suggested by using ESR neural networks, and present a method on the implementation of adaptive controller based on neural network for practical applications. We obtained good results in the case of robot manipulator experiment.

• Journal of the Semiconductor & Display Technology
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• v.16 no.4
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• pp.46-51
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• 2017

Recent controller design techniques often discretize the target system and implement a discrete controller that is digitized to match the target system. When constructing such a discrete system, it is necessary to first determine the sampling time. The smaller the sampling time is, the more advantageous it can be made similar to the original system, but the cost is a problem when realizing such a configuration as hardware. On the other hand, the longer the time, the more different the system is from the original system, and eventually the control becomes impossible. In this paper, we consider the above problem and propose a more logical approach to determine the sampling time in the discrete system and investigate the relation with the differential controller. We also apply this process to a nonlinear system called ARAGO disc and verify its validity through computer simulation.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.124.3-124
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• 2001

The nonlinearity of several chemical processes is usually approximated by a series of the nonlinear static element and the linear subsystem. In the case of the model that the nonlinear static element precedes the linear subsystem, it is called a Hammerstein model. It is a Wiener model when the order is reserved. Here we investigate a relay feedback identification method for Hammerstein type nonlinear processes. The proposed method separates the identification of the nonlinear static function from that of the linear subsystem by using a relay feedback method. From two times activation of nonlinear processes, we identify he whole range of the nonlinear static function as well as the ultimate information of the linear subsystem.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.236-236
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• 2000

Control of Industrial processes is very difficult due to nonlinear dynamics, effect of disturbances and modeling errors. M.Morari proposed Internal Model Control(IMC) system that can be effectively applied to the systems with model uncertainties and time delays. The advantage of IMC systems is their robustness with respect to a model mismatch and disturbances. But it was difficult to apply for nonlinear systems. Adaptive Neuro-Fuzzy Inference System which contains multiple linear models as consequent part is used to model nonlinear systems. Generally, the linear parameters in neuro-fuzzy inference system can be effectively utilized to identify a nonlinear dynamical systems. In this paper, we propose new IMC design method using adaptive neuro-fuzzy inference system for nonlinear plant. Numerical simulation results show that proposed IMC design method has good performance than classical PID controller.