• Proceedings of the KIEE Conference
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• 1997.07b
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• pp.728-731
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• 1997

This paper proposes the systematic auto-tuning method for fuzzy controller using genetic algorithm(GA). In general, the design of fuzzy logic controller has difficulties in the acquisition of expert's knowledge and relies to a great extent on heuristic knowledge which, in many cases, cannot be objectively justified. So, the performance of the controller can be degraded in the case of plant parameter variations or unpredictable incident which the designer may have ignored. Proposed genetic algorithm searches the optimal rule structure, parameters of membership functions and scaling factors simultaneously and automatically by a new genetic coding format. Inverted pendrum system is provided to show the advantages of the proposed method.

• Journal of the Korean Institute of Intelligent Systems
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• v.6 no.2
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• pp.36-42
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• 1996

In this paper, we proposed a PID controller, which could control unknown plants using Artificial Neural Network(ANN) for auto-tuning of the PID parameters. In the proposed algorithm, the parameters of the controller were adjusted to reduce the error of the controlled plant. In this process, the sensitivity between input and output of the unknown plant was needed. So, in order to obtain this sensitivity, the ANN's learnig ability was used. Computer simualtions were performed for the regulation problems, and the results were compared with those of Ziegler-Nichols PID controller. As a result, it was shown that the proposed algorithm outperformed Ziegler-Nichols controller in rise time, overshoot, undershoot, and setting time.

• 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.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.39 no.4
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• pp.19-27
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• 2002

We proposed an auto-tuning method of learning rate for performance improvement of backpropagation algorithm. Proposed method is used a fuzzy logic system for automatic tuning of learning rate. Instead of choosing a fixed learning rate, the fuzzy logic system is used to dynamically adjust learning rate. The inputs of fuzzy logic system are ${\Delta}$ and $\bar{{\Delta}}$, and the output is the learning rate. In order to verify the effectiveness of the proposed method, we performed simulations on a N-parity problem, function approximation, and Arabic numerals classification. The results show that the proposed method has considerably improved the performance compared to the backpropagation, the backpropagation with momentum, and the Jacobs' delta-bar-delta.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.19 no.2
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• pp.114-123
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• 2005

A nonlinear speed control of a PMSM using a sequential parameter auto-tuning algorithm for servo equipments is presented. The nonlinear control scheme gives an undesirable output performance under the mismatch of the system parameters and load conditions. Recently, to improve the performance, an adaptive linearization scheme, a sliding mode control and an observer-based technique have been reported. Although a good performance can be obtained, the performance is not satisfactory any more under specific conditions such as a large inertia variation, a fast speed transient or an increased sampling time. The simultaneous estimation of principal parameters giving a direct influence on speed dynamics is generally not simple. To overcome this problem, a a sequential parameter auto-tuning algorithm at start-up is proposed, where dominant parameters are estimated in a prescribed regular sequence based on the method that one parameter is estimated during each interval. The proposed scheme is implemented on a PMSM using DSP TMS320C31 and the effectiveness is verified through simulations and experiments.

• Journal of Biosystems Engineering
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• v.33 no.1
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• pp.58-62
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• 2008

The need for position information in agriculture is gradually increasing for precise control farm vehicle and effective manage farm land. Though geomagnetic sensor has a lot of merits in estimating heading angle of vehicle because of low costs and sensing ability of magnetic north, it is easy that sensor outputs are distorted in electro magnetic field environment. This study was conducted to develop geomagnetic compass which could be available in measuring relative position from reference point correcting output distorted by external electro magnetic field in a small scale field. Magnetic inducing sensor (PNI's Vector2X) which wound enamel coated copper coil on ferrite core in order to measure and correct earth magnetic field. Magnetic azimuth was corrected using the algorithm which estimated amount of magnetic distortion from the difference between each outputs of magnetic sensors that located on the cross shaped base. Developed auto-tuning magnetic sensor was showed less then 5% as bearing accuracy in the strong magnetic field.

• 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.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.36 no.7
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• pp.797-803
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• 2012

Proportional solenoid valves are a modulating type that can control the displacement of valves continuously by means of electromagnetic forces proportional to the solenoid coil current. Because the solenoid-type modulating valves have the advantages of fast response and compact design over air-operated or motor-operated valves, they have been gaining acceptance in chemical and power plants to control the flow of fluids such as water, steam, and gas. This paper deals with the auto tuning of the position controller that can provide the proportional and integral gain automatically based on the dynamic system identification. The process characteristics of the solenoid valve are estimated with critical gain and critical period at a stability limit based on implemented relay feedback, and the controller parameters are determined by the classical Ziegler-Nichols design method. The auto-tuning algorithm was verified with experiments, and the effects of the operating point at which the relay control is activated as well as the relay amplitude were investigated.

• The Journal of Korea Robotics Society
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• v.2 no.1
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• pp.1-8
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• 2007

This paper presents a PID automatic gain-tuning algorithm for the electronic throttle valve which is driven by wire. Since the system characteristics of position control for electronic throttle valve are so complicated that both the real time robustness and the manufacturing cost must be considered for mass production. To resolve this paradox, a kind of algorithm called RLS (Recursive Least Square) is adopted for the control of the ETB (Electronic Throttle Body). Using this algorithm, the PID gains can be adjusted automatically with the estimated system parameters. Furthermore, a pre-filter is supplemented for the sake of the robustness against the friction and loads. From the industrial requests for the system, the design specifications are decided as follows: the settling time should be less than 1sec and the overshoot should be kept below 3%. The results of the experiments based on this approach show that the high robustness can be achieved while the system stability is satisfied steadily. A parameter estimation scheme and a gain-tuning algorithm have been properly combined and utilized in this research and the effectiveness is verified through the real experiments.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.541-543
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• 1998

Generally, PI control is simple and easy to implement and gains of PI control are determined by specifying a dynamics of the servo driver system. However, the gain-tuning is so difficult that it is relied on an expert's effort. This paper presents a gain auto-tuning method for PI controllers based on a fuzzy inference mechanism. First, the proposed fuzzy inference system identifies a system moment of inertia and adjusts control gains by using the difference in speed responses between a real plant and a reference model. Second, this paper proposes an improved fuzzy PI controller. To reduce the speed overshoot, we adapt a control method that selects a proper PI gains with respect to the load inertia variation. To prove the validity of the proposed gain tuning algorithm and the feasibility of the servo drive, a high performance servo drive will be implemented by DSP(TMS320C31) and intelligent power module (IPM). The proposed controller is applied to the speed control of the 300W AC servo motor. Some simulations and experimental results show that the proposed fuzzy PI controller is more robust than the conventional PI controller against the load inertia variation.