• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.265-271
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• 1992

It is the purpose of this paper to present a dialogical designing method for control system using a rough grasp of the unknown process property. We deal with a single-input single-output feedback control system with a fuzzy controller. The process property is roughly estimated by the step response, and the fuzzy controller is interactively modified according to the operator's requests. The modifying rules mainly derived from computer simulation are useful for almost every process, such as an unstable process and a non-minimum phase process. The fuzzy controller is tuned by taking notice of four characteristics of the step response: (1) rising time, (2) overshoot, (3) amplitude and (4) period of vibration. The tuning position of the controller is fourfold: (1) antecedent gain factor GE or GCE, (2) consequent gain factor GDU, (3) arrangement of the antecedent fuzzy labels and (4) arrangement of the control rules. The rules give an instance to the respective items of the controller in an effective order. The modified fuzzy PI controller realizes a good response of a stable process. However, because the GDU tuning becomes difficult for the unstable process, it is necessary to evaluate the stability of the process from the initial step response. The fuzzy PI controller is applied to the process whose initial step response converges with GDU tuning. The fuzzy PI controller with modified sampling time is applied to the process whose step response converges under the repeated application of the GDU tuning. The fuzzy PD controller is applied to the process whose step response never converges by the GDU tuning.

• The Transactions of the Korean Institute of Power Electronics
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• v.3 no.3
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• pp.173-183
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• 1998

In this paper, an auto-tuning method for fuzzy controller's membership functions based on the neural network is presented. The neural network emulator offers the path which reforms the fuzzy controller's membership functions and fuzzy rule, and the reformed fuzzy controller uses for speed control of induction motor. Thus, in the case of motor parameter variation, the proposed method is superior to a conventional method in the respect of operation time and system performance. 32bit micro-processor DSP(TMS320C31) is used to achieve the high speed calculation of the space voltage vector PWM and to build the self-learning fuzzy control algorithm. Through computer simulation and experimental results, it is confirmed that the proposed method can provide more improved control performance than that PI controller and conventional fuzzy controller.

• Proceedings of the KIPE Conference
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• 1998.10a
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• pp.125-130
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• 1998

This paper presents a speed control for ultrasonic motors using a PI controller and disturbance torque observer. Since the PI gains and the observer's poles are generally fixed, the control performance deteriorates when the driving conditions vary much. Therefore, we propose the speed control scheme that the PI gains and the observer's poles are adjusted on-line in accordance with the speed ripple using fuzzy reasoning.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.31 no.6
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• pp.61-71
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• 2003

In the study of control technique for a turbojet engine model, the Takagi-Sugeno fuzzy logic controller has been designed based on the model identification by the well designed PI controlled system through T-S neuro-fuzzy inference system. To enhance this designed controller, those procedures are proposed that certainty factors are adopted to each rule of objective groups which are classified by the fuzzy C-Means algorithm and the satisfaction degrees are matched to meet the objectives. This proposed technique shows its feasibility by upgrading performances of the previously well-designed T-S fuzzy controller.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.2
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• pp.303-311
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• 2012

In this study, we introduce a dynamic process model as well as the design methodology of optimized fuzzy controller for its efficient application to vacuum production system to produce a semiconductor, solar module and display and so on. In a vacuum control field, PID control method is widely used from the viewpoint of simple structure and preferred performance. But, PID control method is very sensitive to the change of environment of control system as well as the change of control parameters. Therefore, it's difficult to get a preferred performance results from target system which has a complicated structure and lots of nonlinear factors. To solve such problem, we propose the design methodology of an optimized fuzzy PID controller through a following series of steps. First a dynamic characteristic of the target system is analyzed through a series of experiments. Second the process model is built up and its characteristic is compared with real process. Third, the optimized fuzzy PID controller is designed using genetic algorithms. Finally, the fuzzy controller is applied to target system and then its performance is compared with that of other conventional controllers(PID, PI, and Fuzzy PI controller). The performance of the proposed fuzzy controller is evaluated in terms of auto-tuned control parameters and output responses considered by ITAE index, overshoot, rise time and steady state time.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.256-261
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• 2004

This paper presents a simple two-degree of freedom PID tuning table based on the CDM design method. The structure of the control system will be composed of plant, P or PI or PID controller and a pre-filter. The finalized formula can be used based on the experimental test of the plant in the same manner as the Ziegler-Nichols' second method. That is; users just need to find the critical gain and critical period experimentally and the parameters of the P, PI or PID controller with the pre-filter can be obtained by substituting the values of critical gain and critical period in the tuning table. The simulation results of the control systems utilizing the proposed controllers compared with those using the Ziegler-Nichols' second method will also be demonstrated.

• Journal of the Korea Computer Industry Society
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• v.2 no.4
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• pp.425-434
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• 2001

In this paper, a series of processes to configure a feedback control system by using a PID controller in a programmable logic controller (PLC). The PLC (SIMATIC S7-400) with a PID module (FM455C) is connected by online to an IBM PC with the Windows environment, which serves as a PLC programmer. PID controllers including P/PD/PI controllers have been designed in order to show design procedures, and finally, a PID controller for the plant of cart system. Performances of the control system have been investigated by the MATLAB simulation, the simulation in the PLC programmer. Physical performances have been recorded and examined for the real cart system.

• International journal of advanced smart convergence
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• v.10 no.2
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• pp.175-186
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• 2021

We have studied the speed regulation of the permanent magnet synchronous motor (PMSM) servo system in this paper. To optimize the PMSM servo system's speed-control performance with disturbances, a non-linear speed-control technique using a back-propagation neural network (BP-NN) algorithm forthe controller design of the PMSM speed loop is introduced. To solve the slow convergence speed and easy to fall into the local minimum problem of BP-NN, we develope an improved BP-NN control algorithm by limiting the range of neural network outputs of the proportional coefficient Kp, integral coefficient Ki of the controller, and add adaptive gain factor β, that is the internal gain correction ratio. Compared with the conventional PI control method, our improved BP-NN control algorithm makes the settling time faster without static error, overshoot or oscillation. Simulation comparisons have been made for our improved BP-NN control method and the conventional PI control method to verify the proposed method's effectiveness.

• Journal of the Semiconductor & Display Technology
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• v.22 no.2
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• pp.96-103
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• 2023

This paper presents a robust position tracking controller for a motor used in semiconductor equipment, utilizing the motor angle measurement. Precise position control is challenging due to the presence of uncertainties in various motor applications. The proposed controller consists of a PD (Proportional-Derivative) controller and a PIO (Proportional-Integral Observer) to estimate the system's state and equivalent disturbance compensating for the uncertainties. Since the stability alternates as the observer gain increases, we have investigated it through the closedloop root locus under the system parameters change. The analysis has showed that the inertia of the motor is the main parameter that affects it, and by adjusting the control gain appropriately, the system can be rendered to be stable even when the inertia of the motor changes. The effectiveness of the proposed control algorithm is validated through computer simulations, followed by a comparison of its performance with the results of a previous study.

• Journal of Electrical Engineering and Technology
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• v.8 no.3
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• pp.478-489
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• 2013

The controllability and stability of power systems can be increased by Flexible AC Transmission Devices (FACTs). One of the FACTs devices is Interline Power-Flow Controller (IPFC) by which the voltage stability, dynamic stability and transient stability of power systems can be improved. In the present paper, the convenient operation and control of IPFC for transient stability improvement are considered. Considering that the system's Lyapunov energy function is a relevant tool to study the stability affair. IPFC energy function optimization has been used in order to access the maximum of transient stability margin. In order to control IPFC, a Brain Emotional Learning Based Intelligent Controller (BELBIC) and PI controller have been used. The utilization of the new controller is based on the emotion-processing mechanism in the brain and is essentially an action selection, which is based on sensory inputs and emotional cues. This intelligent control is based on the limbic system of the mammalian brain. Simulation confirms the ability of BELBIC controller compared with conventional PI controller. The designing results have been studied by the simulation of a single-machine system with infinite bus (SMIB) and another standard 9-buses system (Anderson and Fouad, 1977).