• Journal of Advanced Marine Engineering and Technology
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• v.33 no.8
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• pp.1246-1253
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• 2009

This paper presents derived a calculating form of fuzzy inference, based on decomposition of $\alpha$-level sets. Based on the calculating form it is propose that fuzzy logic circuits of PI+PD controller are a body from fuzzy inference to defuzzificaion in cases where the command variable u directly is generated PWM. The effect of quantization on $\alpha$-levels is investigated. with input/out characteristics of fuzzy controller by simulation. It is concluded that 4 quantization levels are sufficient result for fuzzy control performance of DC servo system. Simulation and experimental results demonstrated that the hardware implementation of the proposed controller can successfully provide good performance on the position control of DC servo system.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.3
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• pp.193-198
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• 2002

In this paper, we Propose a PD+I fuzzy controller using an error-accumulating applying factor. In fuzzy control, analytical study was done formerly, in which fuzzy control can be classified by PD type and PI type, and also the study for getting merits of both types was done, too. But the mixed type has a complex structure and many parameters. The proposed fuzzy controller is 2-input 2-out-put and PD type fuzzy control is used as a basic structure. And the proposed controller annihilates a steady-state error and improves transient responses because of using the error-accumulating applying factor which is determined in the real time along the current state of controlled process. Futhermore it is easy to tune the system because of decreasing the number of scaling factors and the I type controller with resetting resolves the integral wind-up problem. Finally we apply the proposed scheme to various plants and show the performance betterment.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.45 no.1
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• pp.1-8
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• 2008

A two-wheel balancing vehicle, which helps people moving freely and fast, and is applied from inverted pendulum system, has been widely researched and developed, and some products are came into a market in actuality. Until now, the two-wheel balancing vehicles developed have chosen the general PID control method. In this paper, we propose a new control method to improve a control capacity for a two-wheeled balancing vehicle for human transportation. The proposed method is the fuzzy PD+I control that is one of the improved PID control, and it contains a 2input-1output fuzzy system. This fuzzy system processes signals from proportional and derivative controller, and the fuzzy output signal generates the final output by summing up integral signal. The non-linearity of the fuzzy system makes an optimal output control signal by changing weight of the proportional signal and the derivative signal in process of time. We have simulated the fuzzy PD+I control system and experimented by implementing the two-wheel balancing mobile robot to verify the advantages of the proposed fuzzy PD+I control method in comparison with general PID control. As the results of simulation and experimentation, the proposed fuzzy PD+I control method has better control performance than general PID in this system and improves it.

• Proceedings of the KIPE Conference
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• 2008.06a
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• pp.127-129
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• 2008

This paper describes a method of approximate reasoning for fuzzy control of servo system, based on decomposition of -level fuzzy sets. It is propose that logic circuits for fuzzy PI+PD are a body from fuzzy inference to defuzzificaion in cases where the output variable u directly is generated PWM. The effectiveness for robust and faster response of the fuzzy control scheme is verified for a variable parameter by comparison with a PID control and fuzzy control. A position control of DC servo system with a fuzzy logic controller successfully demonstrated.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.1 no.3
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• pp.119-125
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• 2008

This work involves analyzing partial discharge (PD), which has estimated the detected signal accumulation based on Labview, and analyzing by Fuzzy algorithm. In algorithm, we developed system configuration that detected accumulating PD signal. With practical PD logic implementation of theoretical detected system and hardware implementation, the device for 50kV setup has generated and then has applied with 15k~17kV with 1:1 time probe. Our new class of PD detected algorithm has also compared with PRPDA or Fuzzy algorithm, which has diagnose more conveniently by adding numerical values.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.1
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• pp.68-74
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• 2008

In this paper, a novel neural fuzzy control method is proposed to control nonlinear systems. A standard PD-like fuzzy controller is designed and used as a main controller for the system. Then a neural network controller is added to the reference trajectories to form a neural-fuzzy control structure and used to compensate for nonlinear effects. Two neural-fuzzy control schemes based on two well-known neural network control schemes, the feedback error learning scheme and the reference compensation technique scheme as well as the standard PD-like fuzzy control are studied. Those schemes are tested to control the angle and the position of the inverted pendulum and their performances are compared.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.411-419
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• 1998

In this paper the steady-state genetic algorithm is applied for the optimal design of fuzzy PID controllers. Basically the structure of the discussed fuzzy PID controller is extended from the conventional fuzzy PI and PD controllers where only a two-dimensional rule base of the fuzzy PID controller are designed simultaneously. Simulations results shows the superior performance of this optimal designed fuzzy PID controllers to the optimal designed conventional fuzzy PI and PD controllers.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.483-483
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• 2000

The Fuzzy Preview Control (FPC) design methodology using I-PD Preview Control (IPC) and Optimal Preview Control (OPC)[6] are discussed in this paper. First we show a new fuzzy controller with single input single output, and build a relationship between it and the I-PD Control proposed by Kitamari, as well as Optimal Control with some specific equations. We also give the stability analysis with Lyapunov theorem. On this way, we can design a Fuzzy I-PD Controller (FIC) very easier and more effective. Then, preview control element design methodology of FCP was given according to IPC and OPC. Third, to make the system more rapidly and more little overshooting, two factors are given to adjust the controller's properties. At last, the performance of FPC is revealed via computer simulation using a nonlinear plant.

• Journal of Power Electronics
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• v.11 no.6
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• pp.819-823
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• 2011

This paper presents a fuzzy PD speed control scheme for the robust speed tracking of a permanent magnet synchronous motor (PMSM). Motivated by the common control engineering knowledge that transient performance can be improved if the P gain is big and the D gain is small in the beginning, a linearizing control scheme with a fuzzy PD controller is proposed. The global system stability is analyzed and the proposed control algorithm is implemented using a TMS320F28335 DSP. Simulation and experimental results are given to verify the effectiveness of the proposed method.

• Proceedings of the KIEE Conference
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• 2006.10a
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• pp.224-226
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• 2006

In this paper, we have studied for analysis of the partial discharge(PD) signal in power transmission line. The PD signal has estimated as detected signal accumulation of a PRPDA method by using Labview, and analyzed with neuro-fuzzy algorithm. With practical PD logic implementation of theoretical detected system and hardware implementation, the device for Hipotronics Company's 22.9kV or 154kV setup has generated and then has applied with 18kV,20kV with 1:1 time probe. It's also used the LDPE O.27mmt (scratch error O.05mmt) to sample for making PD. Our new class of PD detected algorithm has also compared with previous PRPDA or Fuzzy algorithm, which has diagnose more conveniently by adding numerical values.