• 제어로봇시스템학회:학술대회논문집
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• 1999.10a
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• pp.173-176
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• 1999

This paper shows a self tuning fuzzy inference method by the genetic algorithm in the fuzzy-sliding mode control for a Polishing robot. Using this method, the number of inference rules and the shape of membership functions are determined by the genetic algorithm. The fuzzy outputs of the consequent part are derived by the gradient descent method. Also, it is guaranteed that .the selected solution become the global optimal solution by optimizing the Akaike's information criterion expressing the quality of the inference rules. It is shown by simulations that the method of fuzzy inference by the genetic algorithm provides better learning capability than the trial and error method.

• Proceedings of the KIEE Conference
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• 1997.07c
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• pp.1021-1024
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• 1997

This paper has presented a method for self-tuning tile PID controller using a BP method of multilayered NNs. The proposed controller employ input signal as a learning signal of PID control. The proposed controller is applied to load-frequency control of power system and it is investigated a dynamic characteristic. The simulation results shows that proposed NN STPID controller has the good dynamics responses against load disturbances.

• Journal of Intelligence and Information Systems
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• v.1 no.2
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• pp.73-83
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• 1995

In this paper, we propose a generating method for the optimal rules of the fuzzy rule base using evolution algorithms. With the aid of evolution algorithms optimal rules of fuzzy logic system can be automatic designed without human expert's priori experience and knowledge. can be intelligent control. The a, pp.oach presented here generating rules by self-tuning the parameters of membership functions and searchs the optimal control rules based on a fitness value which is the defined performance criterion. Computer simulations demonstrates the usefulness of the proposed method in non-linear systems.

• Interdisciplinary Bio Central
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• v.4 no.3
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• pp.9.1-9.7
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• 2012

Filamentous phages have been in the limelight as a new type of nanomaterial. In this study, genetically and chemically modified fd phage was used to generate a biomimetic phage self-assembly product. Positively charged fd phage (p8-SSG) was engineered by conjugating 3-aminopropyltriethoxysilane (APTES) to hydroxyl groups of two serine amino acid residues introduced at the N-terminus of major coat protein, p8. In particular, formation of a phage network was controlled by changing mixed ratios between wild type fd phage and APTES conjugated fd-SSG phage. Assembled phages showed unique bundle and network like structures. The bacteriophage based self-assembly approach illustrated in this study might contribute to the design of three dimensional microporous structures. In this work, we demonstrated that the positively charged APTES conjugated fd-SSG phages can assemble into microstructures when they are exposed to negatively charged wild-type fd phages through electrostatic interaction. In summary, since we can control the phage self-assembly process in order to obtain bundle or network like structures and since they can be functionalized by means of chemical or genetic modifications, bacteriophages are good candidates for use as bio-compatible scaffolds. Such new type of phage-based artificial 3D architectures can be applied in tuning of cellular structures and functions for tissue engineering studies.

• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.2849-2852
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• 1999

This paper describes the fuzzy PD controller using simplified indirect inference method. First, the fuzzy PD controller is derived from the conventional continuous time linear PD controller. Then the fuzzification, control-rule base, and defuzzification using SIIM in the design of the fuzzy controller are discussed in detail. The resulting controller is a discrete time fuzzy version of the conventional PD controller. which has the same linear structure. but are nonlinear functions of the input signals. The proposed controller enhances the self-tuning control capability. particularly when the process to be controlled is nonlinear. As the SIIM is applied, the fuzzy Inference results can be calculated with splitting fuzzy variables into each action component and are determined as the functional form of corresponding variables. So the Proposed method has the capability of the high speed inference and extending the fuzzy input variables easily. Computer simulation results have demonstrated the superior to the control Performance of the one Proposed by D. Misir et at. Final)y. we simulated the contour control of the X-Y tables with direct control strategies using the proposed fuzzy PD controller.

• Proceedings of the KIEE Conference
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• 2002.07b
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• pp.1172-1174
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• 2002

An adaptive fuzzy P + ID controller for variable speed operation of BLDC motor drives is presented in this paper. Generally, a conventional PID controller is most widely used in industry due to its simple control structure and ease of design. However, the PID controller suffers from the electrical machine parameter variations and disturbances. To improve the tracking performance for parameter and load variations, the controller proposed in this paper is constructed by using an adaptive fuzzy logic controller in place of the proportional term in a conventional PID controller. For implementing this controller, only one additional parameter has to be adjusted in comparison with the PID controller. An adaptive fuzzy controller applied to proportional term to achieve robustness against parameter variations has simple structure and computational simplicity. The controller based on optimal fuzzy logic controller has an self-tuning characteristics with clustering. Computer simulation results show the usefulness of the proposed controller.

• Proceedings of the KIEE Conference
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• 2006.10d
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• pp.53-55
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• 2006

This paper presents self tuning PI controller of IPMSM drive using neural network. NNPI controller is developed to minimize overshoot, rise time and settling time following sudden parameter changes such as speed, load torque and inertia. Also, this paper is proposed speed control of IPMSM using neural network and estimation of speed using artificial neural network(ANN) controller. The results on a speed controller of IPMSM are presented to show the effectiveness of the proposed gain tuner. And this controller is better than the fixed gains one in terms of robustness, even under great variations of operating conditions and load disturbance.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.509-511
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• 1998

Design of fuzzy logic controllers encounters difficulties in the selection of optimized membership function and fuzzy rule base, which is traditionally achieved by tedious trial-and-error process. In this paper We proposed a new method to generate fuzzy logic controllers throught genetic algorithm(GA). The controller design space is coded in base-7 strings chromosomes, where each bit gene matches the 7 discrete fuzzy value. The developed approach is subsequently applied to the design of proportional plus integral type fuzzy controller for a do-servo motor control system. It was presented in discrete fuzzy linguistic value, and used a membership function with Gaussian curve. The performance of this control system is demonstrated higher than that of a conventional PID controller and fuzzy logic controller(FLC).

• Proceedings of the KIEE Conference
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• 1993.07b
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• pp.870-872
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• 1993

With the using the microprocessor, this paper presents DC servo motor control characteristics by Self-Tuning PID controller and considers position control response with controller of DC servo motor for robot drive. As this system is supported by a channel, it is considered to enough application effect in industry region such as needing multi joint robot and precision parallel driving.

• Journal of Electrical Engineering and Technology
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• v.8 no.6
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• pp.1439-1450
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• 2013

This paper investigates a robust neuro-fuzzy control (NFC) method which can accurately follow the speed reference of an interior permanent magnet synchronous motor (IPMSM) in the existence of nonlinearities and system uncertainties. A neuro-fuzzy control term is proposed to estimate these nonlinear and uncertain factors, therefore, this difficulty is completely solved. To make the global stability analysis simple and systematic, the time derivative of the quadratic Lyapunov function is selected as the cost function to be minimized. Moreover, the design procedure of the online self-tuning algorithm is comparatively simplified to reduce a computational burden of the NFC. Next, a rotor angular acceleration is obtained through the disturbance observer. The proposed observer-based NFC strategy can achieve better control performance (i.e., less steady-state error, less sensitivity) than the feedback linearization control method even when there exist some uncertainties in the electrical and mechanical parameters. Finally, the validity of the proposed neuro-fuzzy speed controller is confirmed through simulation and experimental studies on a prototype IPMSM drive system with a TMS320F28335 DSP.