• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.4
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• pp.334-340
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• 2002

This paper proposes a self-organizing fuzzy modeling which can create a new hyperplane-shaped cluster by applying multiple regression to input/output data with relatively large fuzzy entropy, add the new cluster to fuzzy rule base and adjust parameters of the fuzzy model in repetition. Tn the coarse tuning, weighted recursive least squared algorithm and fuzzy C-regression model clustering are used and in the fine tuning, gradient descent algorithm is used to adjust parameters of the fuzzy model precisely And learning rates are optimized by utilizing meiosis-genetic algorithm. To check the effectiveness and feasibility of the suggested algorithm, four representative examples for system identification are examined and the performance of the identified fuzzy model is demonstrated in comparison with that of the conventional fuzzy models.

• Journal of Institute of Control, Robotics and Systems
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• v.2 no.4
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• pp.306-311
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• 1996

This paper discusses the maintenance of the water level of steam generators at its programmed value. The process, the water level of a steam generator, has the nonminimum phase property. So, it causes a reverse dynamics called a swell and shrink phenomenon. This phenomenon is severe in a low power condition below 15 %, in turn makes the start-up of the power plant too difficult. The control algorithm used here incorporates a pole-assignment scheme into the minimum variance strategy and we use a parallel adaptation algorithm for the parameter estimation, which is robust to noises. As a result, the total control system can keep the water level constant during full power by locating closed-loop poles appropriately, although the process has the characteristics of high complexity and nonlinearity. Also, the extra perturbation signals are added to the input signal such that the control system guarantee persistently exciting. In order to confirm the control performance of a proposed pole-assignment self-tuning controller we perform a computer simulation in full power range.

• Journal of Biosystems Engineering
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• v.21 no.3
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• pp.283-292
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• 1996

Automatic guidance of farm tractors would improve productivity by reducing operator fatigue and increasing machine performance. To control tractors within $\pm$5cm of the desired path, fuzzy and adaptive steering controllers were developed to evaluate their characteristics and performance. Two input variables were position and yaw errors, and a steering command was fed to tractor model as controller output. Trapezoidal membership functions were used in the fuzzy controller, and a minimum-variance adaptive controller was implemented into the 2-DOF discrete-time input-output model. For unit-step and composite paths, a dynamic tractor simulator was used to test the controllers developed. The results showed that both controllers could control the tractor within $\pm$5cm error from the defined path and the position error of tractor by fuzzy controller was the bigger of the two. Through simulations, the output of self-tuning adaptive controller was relatively smooth, but the fuzzy controller was very sensitive by the change of gain and the shape of membership functions. Contrarily, modeling procedure of the fuzzy controller was simple, but the adaptive controller had very complex procedure of design and showed that control performance was affected greatly by the order of its model.

• Nuclear Engineering and Technology
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• v.27 no.6
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• pp.833-844
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• 1995

In self-tuning predictive control algorithm for steam generator is presented. The control algorithm is derived by suitably modifying the generalized predictive control algorithm. The main feature of the unposed method relies on considering the measurable disturbance and a simple adaptive scheme for obtaining the controller gain when the parameters of the plant are unknown. This feature makes the proposed approach particularly appealing for water level control of steam generator when measurable disturbance is used. In order to evaluate the performance of the proposed algorithm, computer simulations are done for an PWR steam generator model. Simulation result show satisfactory performances against load variations and steam flow rate estimation errors. It can be also observed that the proposed algorithm exhibit better responses than a conventional PI controller.

• Ceramist
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• v.22 no.1
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• pp.56-69
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• 2019

Recent advances in low-power sensors and transmitters are driving the search for standalone power sources that utilize unused ambient energy. These energy harvesters can alleviate the issues related to the installation and maintenance of sensors. Particularly piezoelectric energy harvesters, with the ability to convert ambient mechanical energy into useful electricity, have received significant attention due to their high energy density, low cost and operational stability over wide temperature and pressure conditions. In order to maximize the generated electrical power, the natural frequency of the piezoelectric energy harvester should be matched with the dominant frequency of ambient vibrations. However, piezoelectric energy harvesters typically exhibit a narrow bandwidth, thus, it becomes difficult to operate near resonance under broadband ambient vibration conditions. Therefore, the resonating of energy harvesters is critical to generate maximum output power under ambient vibration conditions. For this, energy harvesters should have broadband natural frequency or actively tunable natural frequency with ambient vibrations. Here, we review the most plausible broadband energy harvesting techniques of the multi-resonance, nonlinearity, and self-resonance tuning. The operation mechanisms and recent representative studies of each technique are introduced and the advantages and disadvantages of each method are discussed. In addition, we look into the future research direction for the broadband energy harvester.

• Proceedings of the KIEE Conference
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• 1997.11a
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• pp.83-85
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• 1997

In this paper, a self-tunning fuzzy inference technique for stabilization of the inverted pendulum system is proposed. The facility of this self-tunning fuzzy controller which has swing-up control mode and a stabilization one, moves a pendulum in an initial natural stable equilibrium point and a cart in arbitrary position, to an unstable equilibrium point and a center of rail. Specially, the virtual equilibrium point(${\phi}_{VEq}$) which describes functionally considers the interactive dynamics between a position of cart and a angle of inverted pendulum is introduced. And comparing with the convention optimal controller, the proposed self-tunning fuzzy inference structure made substantially the inverted pendulum system robust and stable.

• Proceedings of the KAIS Fall Conference
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• 2010.11a
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• pp.341-343
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• 2010

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 Institute of Control, Robotics and Systems
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• v.13 no.4
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• pp.329-334
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• 2007

This paper deals with self-tuning of fuzzy control systems. The fuzzy logic controller(FLC) has parameters that an: input and output scaling factors to effect control output. Tuning method is proposed for the scaling factor. In this paper. it is studied to control and to monitor the remote system statues using SOAP for communicate between the server part and the client part. The remote control system is controlled by using a web browser or a application program. The server part is waiting for the request of client part that uses internet network for communication each other and then the request is reached. the server part saves client data to the database and send a command set to the client part and then the client part sends command to controller in a cool chamber. The administrator can control and monitor the remote system just using a web browser. The effects of membership functions, defuzzification methods and scaling factors are investigated in the FLC system.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.91.5-91
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• 2001

This paper suggests that the immune algorithm can effectively be used in tuning of a PID controller. The artificial immune network always has a new parallel decentralized processing mechanism for various situations, since antibodies communicate to each other among different species of antibodies/B-cells through the stimulation and suppression chains among antibodies that form a large-scaled network. In addition to that, the structure of the network is not fixed, but varies continuously. That is, the artificial immune network flexibly self-organizes accord Eng to dynamic changes of external environment (meta-dynamics function). However, up to the present time, models based on the conventional crisp approach have been used to describe dynamic model relationship between antibody and antigen. Therefore, there are some problems ...

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.682-684
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• 1995

The most effective way to improve the performance of a fuzzy controller may be to optimize look-up values. Look-up values are derived from processes used input-output scale factors, membership functions, rule base, fuzzy inference method and defuzzification. It is powerful way to modify or organize look-up table values. In this paper, We propose the look-up values self-organizing fuzzy controller(LSOFC). We use the plus-minus tuning method(PMTM), scanning values through the processes of addition and subtraction. We show the efficiency of this LSOFC by the results of simulation for nonlinear time-varying plant with unmodelled dynamics.