• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.4
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• pp.384-388
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• 1999

This paper describes an approach for synthesizing a modularized gain scheduled PD type fuzzy logic controller(FLC) of nonlinear magnetic bearing system where the gains of FLC are on-line adapted according to the operating point. Specifically the systematic procedure via root locus technique is carried out for the selection of the gains of FLC. Simulation results demonstrate that the proposed gain scheduled fuzzy logic controller yields not only maximization of stability boundary but also better control performance than a single operating point (without gain scheduling)fuzzy controller.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.12
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• pp.182-189
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• 1999

In this paper, we present an ultrasonic sensor based path planning method using fuzzy logic for obstacle avoidance of an intelligent vehicle in unknown environments. Generally, Robot navigation in unknown terrains is a very complex task difficult to control because of the great amount of imprecise and ambiguous sensor information that has to be considered. In this case, fuzzy logic can satisfactorily deal with such information in quite efficient manner. In this study, we propose two fuzzy logic controller which is composed of steering controller and velocity controller respectively. Our object is to develop a fuzzy controller that can enable a mobile robot to navigate from a start point to a goal point without collisions, in the least possible travel time. The ability and effectiveness for the proposed algorithm will be demonstrated by simulation and expeiment.

• Computers and Concrete
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• v.9 no.5
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• pp.389-402
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• 2012

In this study, the modulus of elasticity of low, normal and high strength steel fiber reinforced concrete has been predicted by developing a fuzzy logic model. The fuzzy models were formed as simple rules using only linguistic variables. A fuzzy logic algorithm was devised for estimating the elastic modulus of SFRC from compressive strength. Fibers used in all of the mixes were made of steel, and they were in different volume fractions and aspect ratios. Fiber volume fractions of the concrete mixtures have changed between 0.25%-6%. The results of the proposed approach in this study were compared with the results of equations in standards and codes for elastic modulus of SFRC. Error estimation was also carried out for each approach. In the study, the lowest error deviation was obtained in proposed fuzzy logic approach. The fuzzy logic approach was rather useful to quickly and easily predict the elastic modulus of SFRC.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1374-1376
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• 1996

In this paper We proposed a new method to generate fuzzy logic controllers through genetic algorithm(GA). In designing of fuzzy logic controllers encounters difficulties in the selection of optimized member-ship functions, gains and rule base, which is conventionally achieved by a tedious trial-and-error process. This paper develops genetic algorithms for automatic design of high performance fuzzy logic controllers which can overcome nonlinearities in many engineering control applications. The rule-base is coded in base-7 strings by generated from random function. Which can be presented in discrete fuzzy linguistic value, and using membership function with Gaussian curve. To verify the validity of this fuzzy logic controller it is compared with conventional fuzzy logic controller(FLC) and PID controller.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.4
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• pp.281-287
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• 2002

This paper investigates on the feasibility of fuzzy logic control for networked control systems. In order to evaluate its feasibility, a networked control system for motor speed control is implemented on a Profibus-DP network. The NCS consists of several inde-pendent, but interacting processes running on two separate stations. By using this NCS, the network-induced delay is analyzed to find the cause and effect of the delay. Furthermore, in order to prove the feasibility, the fuzzy logic controller's performance is compared with those of conventional PID controllers. Based on the experimental results, the fuzzy logic controller can be a viable choice far NCS due to its robustness against parameter uncertainty.

• Proceedings of the KIEE Conference
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• 2001.07e
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• pp.58-61
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• 2001

The purpose of this paper is a fuzzy logic controller for XY positioning system. The overall control system consists of three parts, the position controller, the speed controller, the fuzzy logic controller. Precise tracking is achieved by fuzzy logic controller. In practice, such systems contain many uncertainties. Therefore, the XY positioning system must receive and evaluate the motion of all axis for a better contouring accuracy. Cross coupled controller utilizes all axis position error information simultaneously to produce accurate contours. However, the existing Cross coupled controllers cannot overcome friction, backlash and parameter variation. So, we propose a fuzzy logic controller of XY positioning system. Experimental results show that the proposed fuzzy logic controller is effective to improve the contouring accuracy of XY positioning system.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1024-1026
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• 1996

Sources of nonlinearity In a fuzzy logic controller Include the fuzzification, the fuzzy reasoning and the defuzzification. In this paper, a closed form expression for the defuzzified output is derived in case of a fuzzy logic controller with two Inputs, triangular memberships, MacVicar-Whelan type linguistic rules, and direct fuzzy reasoning. As a result, it is shown that fuzzy logic controller is a nonlinear controller. Also its nonlinearity Is analyzed with respect to the conventional PID control and the sliding mode control.

• Journal of Industrial Technology
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• v.30 no.A
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• pp.111-117
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• 2010

To improve the performance of the prediction system, the system should reflect well the uncertainty of nonlinear data. Thus, this paper presents multiple prediction systems based on Type-2 fuzzy sets. To construct each prediction system, an Interval Type-2 TSK Fuzzy Logic System and difference data were used, because, in general, it has been known that the Type-2 Fuzzy Logic System can deal with the uncertainty of nonlinear data better than the Type-1 Fuzzy Logic System, and the difference data can provide more steady information than that of original data. Also, to improve each rule base of the fuzzy prediction systems, the HCBKA (Hierarchical Correlation Based K-means clustering Algorithm) was applied because it can consider correlationship and statistical characteristics between data at a time. Subsequently, to alleviate complexity of the proposed prediction system, a system selection method was used. Finally, this paper analyzed and compared the performances between the Type-1 prediction system and the Interval Type-2 prediction system using simulations of three typical time series examples.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10b
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• pp.1950-1955
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• 1991

A fuzzy dynamic learning controller is proposed and applied to control of time delayed, non-linear and unstable chemical processes. The proposed fuzzy dynamic learning controller can self-adjust its fuzzy control rules using the external dynamic information from the process during on-line control and it can create th,, new fuzzy control rules autonomously using its learning capability from past control trends. The proposed controller shows better performance than the conventional fuzzy logic controller and the fuzzy self organizing controller.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.1 no.1
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• pp.44-49
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• 2001

For minimum phase systems, the conventional fuzzy logic controllers (FLCs) use the error and the change-of-error as fuzzy input variables. Then the control rule table is a skew symmetric type, that is, it has UNLP (Upper Negative and Lower Positive) or UPLN property. This property allowed to design a single-input FLC (SFLC) that has many advantages. But its control parameters are not automatically adjusted to the situation of the controlled plant. That is, the adaptability is still deficient. We here design a single-input direct adaptive FLC (SDAFLC). In the AFLC, some parameters of the membership functions characterizing the linguistic terms of the fuzzy rules are adjusted by an adaptive law. The SDAFLC is designed by a stable error dynamics. We prove that its closed-loop system is globally stable in the sense that all signals involved are bounded and its tracking error converges to zero asymptotically. We perform computer simulations using a nonlinear plant and compare the control performance between the SFLC and the SDAFLC.