• Journal of the Korean Society for Precision Engineering
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• v.26 no.12
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• pp.55-61
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• 2009

Machining systems have been evolved to produce more detailed products of high added value. This has been possible, in large part, due to the development of highly accurate multi-axis CNC machine tools. The conventional CNC of machine tools has individual axis controllers to maximize tracking performance. On the other hand, cross-coupling controllers can be integrated into the conventional CNC to enhance contouring performance. For this multi-axis cross-coupling control system, it is necessary to automatically adjust the controller gains depending on operating conditions and/or other external conditions from an optimization perspective. This paper proposes automatic modeling of feed drive systems that minimizes the difference in behavior between the system model and the actual system. Based on the modeling, an integrated auto-tuning method is also proposed to improve both tracking and contouring accuracy of a 3-axis cross-coupling control system as well as users' convenience. The proposed methods are evaluated by both simulation and experiments.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.2
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• pp.246-252
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• 2012

Recently, concern over climate change and global politics associated with traditional fossil fuel energy sources has driven significant increase in wind energy utilization over the past decade around world. Generally, life-time of wind turbine system should be guaranteed for twenty years. Therefore, performance deterioration of wind turbine system occurs owing to aging effects for long term operation. In this work, a new type of auto tuning algorithm for overcoming the problem of performance deterioration is proposed. Furthermore, various simulations are carried out to verify the feasibility of the proposed scheme.

• The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.8 no.3
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• pp.64-70
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• 1994

We propose a new method to deal with optimal auto-tuning of the PID controller which is used to process control in various fields. First of all, in this method, 1st order system which was modeled from the unit step response of the system is Pade-approximated, then initial values are determined by the Ziegler-Nichols method. Finally, we can find the parameters of PID controller so as to maximize the fuzzy criterion function which includes the maximum overshoot, damping ratio, rising time and settling time. The Proposed method also shows good adaptability for variations in characteristics and dead m e of the system.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.2 no.3
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• pp.295-302
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• 1998

In this paper, a new auto-tuning PI controller for the speed servo system of a PMSM is designed by using limited step response characteristics. The method is proposed that gets information about auto-tuning of PI regulator by the injection of step input, called limited input, during a transient response time of control. System parameter estimation and speed control could be continuously executed. This means that in despite of system uncertainty the system information obtained by limited input can be continuously applied to the PI regulator. We demonstrate the effectiveness of the proposed auto-tuning algorithm through simulation and experiment result of the speed control for a PMSM having monotone increasing step response.

• Journal of Electrical Engineering and Technology
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• v.2 no.2
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• pp.178-187
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• 2007

One important issue in power systems is dynamic instability due to loosing balance relation between electrical generation and a varying load demand that justifies the necessity of stabilization. Moreover, Power System Stabilizer (PSS) must have capability of producing appropriate stabilizing signals over a wide range of operating conditions and disturbances. To overcome these drawbacks, this paper proposes a new method for robust design of PSS by using an auto-tuning fuzzy control in combination with Real-Coded Genetic Algorithm (RCGA). This method includes two fuzzy controllers; internal fuzzy controller and supervisor fuzzy controller. The supervisor controller tunes the internal one by on-line applying of nonlinear scaling factors to inputs and outputs. The RCGA-based method is used for off-line training of this supervisor controller. The proposed PSS is tested in three operational conditions; nominal load, heavy load, and in the case of fault occurrence in transmission line. The simulation results are provided to compare the proposed PSS with conventional fuzzy PSS and conventional PSS. By evaluating the simulation results, it is shown that the performance and robustness of proposed PSS in different operating conditions is more acceptable

• The Journal of the Convergence on Culture Technology
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• v.4 no.4
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• pp.419-427
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• 2018

This paper designs and develops auto-tuning temperature controller with multi-channel, which controller with multi-channel could control a number of control system simultaneously. This controller has multi-channel input and output. And a number of control algorithms run in this controller simultaneously and independently. Firstly we present design method of controller with multi-channel. Secondly we design electrical circuit of sensor input, controller output and power control for temperature control board. And finally we design data protocol for serial communication to monitor control state and present verification of temperature controller with muiti-channel through field experiment.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.795-797
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• 1995

In this paper, we present a simple auto-tuning PID controller using genetic algorithms. The basic idea of the scheme is to parameterize a Ziegler-Nichols-like tuning formula by a single parameter ${\alpha}$, then to use GA to select optimal tuning parameter. Also, simple rule mechanisms make the controller adapt against large variations in parametric and dynamics uncertainties in the plant. These scheme lead to improved performance of the transient and steady state behavior of the closed loop system, including processes with long delay-time and nonminimum phase systems.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.3
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• pp.261-268
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• 2002

This paper represents that auto tunings of membership functions and weights in the fuzzy neural networks are effectively performed by immune algorithm. A number of hybrid methods in fuzzy-neural networks are considered in the context of tuning of learning method, a general view is provided that they are the special cases of either the membership functions or the gain modification in the neural networks by genetic algorithms. On the other hand, since the immune network system possesses a self organizing and distributed memory, it is thus adaptive to its external environment and allows a PDP (parallel distributed processing) network to complete patterns against the environmental situation. Also, it can provide optimal solution. Simulation results reveal that immune algorithms are effective approaches to search for optimal or near optimal fuzzy rules and weights.

• Journal of the Korea Convergence Society
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• v.4 no.2
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• pp.43-50
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• 2013

Sliding mode control(SMC) are carried out in this literature. And to make the controllers perform better, fuzzy logic was chosen,it makes PID controller auto-tuning parameters and reduced the chattering problem of sliding mode control. Since SMC take error and derivative of error as inputs, after comparison some results are obtained.PID controller response faster yet sliding mode control is much steadier. However certain problems cannot be ignored that the chattering phenomenal cannot be reduced entirely and this motion may hurt the machine; this project only considered a simple system, there is no guarantee PID can work as well as in this case for a much more complex system. MATLAB simulink was the main approach to obtain the performance of the two controllers: to observe the control output of the two controllers, electric circuit and special controllers are designed and tested in MATLAB.

• Proceedings of the KAIS Fall Conference
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• 2009.05a
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• pp.458-460
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• 2009

The parameters of PID controller should be readjusted whenever system character change. In spite of a rapid development of control theory, this work needs much time and effort of expert. In this paper, to resolve this defect, after the sample of parameters in the changeable limits of system character is obtained, these parametrs are used as desired values of back propagation learning algorithm, also neural network auto tuner for PID controller is proposed by determing the optimum structure of neural network. Simulation results demonstrate that auto-tuning proper to system character can work well.