• Journal of Institute of Control, Robotics and Systems
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• v.7 no.5
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• pp.393-398
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• 2001

This paper presents a new method with time-varying boundary layer and input gain, variated by Fuzzy Logic Control(FLC) by means of the system state in Sliding Mode Control (SMC). In addition to the time-varying boundary layer, the time-varying range of the fuzzy membership function has an effect on not only chattering reduction but also fast response characteristics. On the basis of SMC with time-varying boundary and FLC with time-varying input and output range, a computer simulation for inverted pendulum results in elimination of the chattering phenomenon and fast response.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.1
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• pp.183-187
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• 2007

This paper proposes Fuzzy Speed Estimator using Fuzzy Logic Controller(FLC) as a adaptive law in Model Reference Adaptive System(MRAS) in order to realize the speed-sensorless control of an induction motor. Fuzzy Speed Estimator estimates the speed of an induction motor with a rotor flux of the reference model and the adjustable model in MRAS. Fuzzy logic controller reduces the error of the rotor flux between the reference model and the adjustable model using the error and the change of error of the rotor flux as the input of FLC. The experiment is executed to verify the propriety and the effectiveness of the proposed speed estimator.

• The Transactions of the Korea Information Processing Society
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• v.4 no.1
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• pp.167-176
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• 1997

Due to continuous change in traffic and increase in traffic volumes at the intersection, efficient traffic control system is required to manage road situations flexibly in accordance with the change occurring every hour. In this paper, we study the control systems which will help us to determine the interva ls of intersection following the autonomous analysis of complexity of the road. Fuzzy logic control concept was applied to the fuzzy logic controller(FLC) for controlling traffic signal. Furthermore the fuzzy signal systems were compare with the regular signal systems to prove higher performance of the FLC presente d in the paper. By means of simulation, the validity of FLC was proven. About 6% increase in the efficiency of traffic control based on the proposed algorithm in this paper was when we use the simulation.

• Journal of Power Electronics
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• v.6 no.3
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• pp.226-234
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• 2006

Photovoltaic (PV) generators have nonlinear V-I characteristics and maximum power points which vary with illumination level and temperature. Using a maximum power point tracker (MPPT) with an intermediate converter can increase the system efficiency by matching the PV systems to the load. This paper presents a maximum power point tracker based on fuzzy logic and a control scheme for a single-phase inverter connected to the utility grid. The fuzzy logic controller (FLC) provides an adaptive nature for system performance. Also the FLC provides excellent features such as fast response, good performance and the ability to change the fuzzy parameters to improve the control system. A single-phase AC-DC inverter is used to connect the PV system to the grid utility and local loads. While a control scheme is implemented to inject the PV output power to the utility grid at unity power factor and reduced harmonic level. The simulation results have shown the effectiveness of the proposed scheme.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.993-996
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• 1993

This paper addresses the structure and its associated learning algorithms of a feedforward multi-layered connectionist network, which has distributed learning abilities, for realizing the basic elements and functions of a traditional fuzzy logic controller. The proposed neural-network-based fuzzy logic control system (NN-FLCS) can be contrasted with the traditional fuzzy logic control system in their network structure and learning ability. An on-line supervised structure/parameter learning algorithm dynamic learning algorithm can find proper fuzzy logic rules, membership functions, and the size of output fuzzy partitions simultaneously. Next, a Reinforcement Neural-Network-Based Fuzzy Logic Control System (RNN-FLCS) is proposed which consists of two closely integrated Neural-Network-Based Fuzzy Logic Controllers (NN-FLCS) for solving various reinforcement learning problems in fuzzy logic systems. One NN-FLC functions as a fuzzy predictor and the other as a fuzzy controller. As ociated with the proposed RNN-FLCS is the reinforcement structure/parameter learning algorithm which dynamically determines the proper network size, connections, and parameters of the RNN-FLCS through an external reinforcement signal. Furthermore, learning can proceed even in the period without any external reinforcement feedback.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.13 no.1
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• pp.35-47
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• 2004

In this paper presents a new approach to the design and real-time implementation of fuzzy control system based-on digital signal processors(DSP:IMS320C80) in order to improve the precision and robustness for system of industrial robot(Dual-Arm with 8 joint Robot). The need to meet demanding control requirement in increasingly complex dynamical control systems under significant uncertainties, leads toward design of intelligent manipulation robots. The IMS320C80 is used in implementing real time fuzzy control to provide an enhanced motion control for robot manipulators. In this paper, a Self-Organizing Fuzzy Controller(SOFC) for the industrial robot manipulator with a actuator located at the base is studied. A fuzzy logic composed of linguistic conditional statements is employed by defining the relations of input-output variables of the controller. In the synthesis of a FLC(Fuzzy Logic Controller), one of the most difficult problems is the determination of linguistic control rules from the human operators. To overcome this difficult SOFC is proposed for a hierarchical control structure consisting of basic and high levels that modify control rules. The proposed SOFC scheme is simple in structure, Int in computation, and suitable for implementation of real-time control. Performance of the SOFC is illustrated by simulation and experimental results for a Dual-Arm robot with eight joints.

• Proceedings of the KIPE Conference
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• 2004.07b
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• pp.655-659
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• 2004

This paper proposes a fuzzy logic based vector control for the gearless traction machine drive systems using a permanent-magnet synchronous motor (PMSM). The performance of the proposed Fuzzy Logic Control(FLC)-based PMSM drive are investigated and compared to those obtained from the conventional PI controll-based drive system. We have confirmed theoretically and experimentally at different dynamic operating conditions such as step change in command speed, step change in load, etc. The comparative experimental results show that the FLC is more robust and, hence, found to be a suitable replacement of the conventional Pl controller for the high-performance elevator drive system.

• Journal of Microbiology and Biotechnology
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• v.23 no.3
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• pp.335-342
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• 2013

In this study, we compared the performance of two control systems, fuzzy logic control (FLC) and conventional control (CC). The control systems were applied for controlling temperature and substrate moisture content in a solidstate fermentation for the biosynthesis of amylase and protease enzymes by Aspergillus oryzae. The fermentation process was achieved in a 200 L rotating drum bioreactor. Three factors affecting temperature and moisture content in the solid-state fermentation were considered. They were inlet air velocity, speed of the rotating drum bioreactor, and spray water addition. The fuzzy logic control system was designed using four input variables: air velocity, substrate temperature, fermentation time, and rotation speed. The temperature was controlled by two variables, inlet air velocity and rotational speed of bioreactor, while the moisture content was controlled by spray water. Experimental results confirmed that the FLC system could effectively control the temperature and moisture content of substrate better than the CC system, resulting in an increased enzyme production by A. oryzae. Thus, the fuzzy logic control is a promising control system that can be applied for enhanced production of enzymes in solidstate fermentation.

• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.6
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• pp.577-582
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• 1999

Using a sole input variable simplifies the design process for the fuzzy logic controller(FLC). This is called single-input fuzzy logic controller(SFLC). However it is still deficient in the capability of adapting to the varying operating conditions. We here design a single-input adaptive fuzzy logic controller(AFLC) using a switching function of the sliding mode control. The AFLC can directly incorporate linguistic fuzzy control rules into the controller. Hence some parameters of the membership functions characterizing the linguistic terms of the fuzzy rules can be adjusted by an adaptive law. In the proposed AFLC center values of fuzzy sets are directly adjusted by a fuzzy logic system. We prove that 1) its closed-loop system is globally stable in the sense that all signals involved are bounded and 2)its tracking error converges to zero asymptotically. We perform computer simulation using a nonlinear plant.

• Journal of Power System Engineering
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• v.20 no.6
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• pp.11-19
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• 2016

A variable speed refrigeration system(VSRS) has been received high attention for energy saving ability. This paper investigates effects of design factors such as membership function range and sampling time to control performances for systematical designing fuzzy logic controller of the VSRS. Some comparisons of control performance between the fuzzy and PI are conducted including comparative evaluation of robustness against noise by using computer simulations. The simulation results showed that the fuzzy is very useful design method for engineers in the industrial fields which have big noises system and deal with inherent nonlinear system like the VSRS.