• Title, Summary, Keyword: Fuzzy control

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A fuzzy dynamic learning controller for chemical process control

  • Song, Jeong-Jun;Park, Sun-Won
    • 제어로봇시스템학회:학술대회논문집
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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.

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INTERPOLATIVE REASONING FOE COMPUTATIONALLY EFFICIENT OPTIMAL FUZZY CONTROL

  • Kacprzyk, Janusz
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • pp.1270-1273
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    • 1993
  • Fuzzy optimal control is considered. An optimal sequence of controls is sought best satisfying fuzzy constraints on the controls and fuzzy goals on the states (outputs), with a fuzzy system under control Control over a fixed and specified, implicitly specified, fuzzy, and infinite termination time is discussed. For computational efficiency a small number of reference fuzzy staters and controls is to be assumed by which fuzzy controls and stated are approximated. Optimal control policies reference fuzzy states are determined as a fuzzy relation used, via the compositional rule of inference, to derive an optimal control. Since this requires a large number of overlapping reference fuzzy controls and states implying a low computational efficiency, a small number of nonoverlapping reference fuzzy states and controls is assumed, and then interpolative reasoning is used to infer an optimal fuzzy control for a current fuzzy state.

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Neuro-Fuzzy Control of Inverted Pendulum System for Intelligent Control Education

  • Lee, Geun-Hyung;Jung, Seul
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.9 no.4
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    • pp.309-314
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    • 2009
  • This paper presents implementation of the adaptive neuro-fuzzy control method. Control performance of the adaptive neuro-fuzzy control method for a popular inverted pendulum system is evaluated. The inverted pendulum system is designed and built as an education kit for educational purpose for engineering students. The educational kit is specially used for intelligent control education. Control purpose is to satisfy balancing angle and desired trajectory tracking performance. The adaptive neuro-fuzzy controller has the Takagi-Sugeno(T-S) fuzzy structure. Back-propagation algorithm is used for updating weights in the fuzzy control. Control performances of the inverted pendulum system by PID control method and the adaptive neuro-fuzzy control method are compared. Control hardware of a DSP 2812 board is used to achieve the real-time control performance. Experimental studies are conducted to show successful control performances of the inverted pendulum system by the adaptive neuro-fuzzy control method.

A Study on Design of Neuro- Fuzzy Controller for Attitude Control of Helicopter (헬리콥터 자세제어를 위한 뉴로 퍼지 제어기의 설계에 관한 연구)

  • Choi, Yong-Sun;Lim, Tae-Woo;Jang, Gung-Won;Ahn, Tae-Chon
    • Proceedings of the KIEE Conference
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    • pp.2283-2285
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    • 2001
  • This paper proposed to a neural network based fuzzy control (neuro-fuzzy control) technique for attitude control of helicopter with strongly dynamic nonlinearities and derived a helicopter aerodynamic torque equation of helicopter and the force balance equation. A neuro-fuzzy system is a feedforward network that employs a back-propagation algorithm for learning purpose. A neuro-fuzzy system is used to identify nonlinear dynamic systems. Hence, this paper presents methods for the design of a neural network(NN) based fuzzy controller(that is, neuro-fuzzy control) for a helicopter of nonlinear MIMO systems. The proposed neuro-fuzzy control determined to a input-output membership function in fuzzy control and neural networks constructed to improve through learning of input-output membership functions determined in fuzzy control.

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Three-Phase Z-Source PWM Rectifier Based on the DC Voltage Fuzzy Control (직류전압 퍼지 제어 기반의 3상 Z-소스 PWM 정류기)

  • Qiu, Xiao-Dong;Jung, Young-Gook;Lim, Young-Cheol
    • The Transactions of the Korean Institute of Power Electronics
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    • v.18 no.5
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    • pp.466-476
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    • 2013
  • This paper describes a fuzzy control method to control the output voltage of the three-phase Z-source PWM rectifier. A fuzzy control system is a control system based on fuzzy logic, and the fuzzy controller uses a single input fuzzy theory with its fuzzification. Analytical structure of the simplest fuzzy controller is derived through the triangular membership functions with its fuzzification. By setting the membership functions of the fuzzy rules, fuzzy control is achieved. The PI portion of the output DC voltage controller is controlled by fuzzy method. To confirm the validity of the proposed method, the simulation and experiment were performed, The simulation is performed with PSIM and MATLAB/SIMULINK. For the experiment, we used a DSP(TMS320F28335) controller to compute the reference value and generate the PWM pulses. For the transient state performance of the output DC voltage control of Z-source PWM rectifier, the PI controller and fuzzy controller were compared, also the conventional PWM rectifier and Z-source PWM rectifier were compared. From the results, the Z-source rectifier could allow to buck or boost of the output DC voltage. Through the analysis of the transient state, we could observe that the fuzzy controller has better performance than the conventional PI controller.

Development of Fuzzy-Statistical Control Chart for Processing Uncertain Process Information (불명확한 공정정보 처리를 위한 퍼지-통계적 관리도의 개발)

  • 김경환;하성도
    • Journal of the Korean Society for Precision Engineering
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    • v.15 no.2
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    • pp.75-80
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    • 1998
  • Process information is known to have the continuous distribution in many manufacturing processes. Generalized p-chart has been developed for controlling processes by classifying the information characteristics into several groups. But it is improper to describe continuous processes with the classified process informal ion, which is based on the classical set concept. Fuzzy control chart, has been developed for the control of linguistic data, but it is also based on the dichotomous notion of classical set theory. In this paper, fuzzy sampling method is studied in order to process the uncertain data properly. The method is incorporated with the fuzzy control chart. Statistical characteristics of the fuzzy representative value are utilized to device the fuzzy-statistical control chart. The fuzzy-statistical control chart is compared with the generalized p-chart and both the sensitivity to the process information distribution change pared robustiness against the noise on the process information of the fuzzy-statistical control chart are shown to be superior.

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Development of Quality Information Control Technique using Fuzzy Theory (퍼지이론을 이용한 품질 정보 관리기법 개발에 관한 연구)

  • 김경환;하성도
    • Proceedings of the Korean Society of Precision Engineering Conference
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    • pp.524-528
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    • 1996
  • Quality information is known to have the characteristic of continuous distribution in many manufacturing processes. It is difficult to describe the process condition by classifying the distribution into discrete ranges which is based on the set concept. Fuzzy control chart has been developed for the control of linguistic data but it still utilizes the dichotomous notion of classical set theory. In this paper, the fuzzy sampling method is studied in order to manage the ambiguous data properly and incorporated for generating fuzzy control chart. The method is based on the fuzzy set concept and considered to be appropriate for the realization of a complete fuzzy control chart. The fuzzy control chart was compared with the conventional generalized p-chart in the sensitivity for quality distribution and robustiness against the noise. The fuzzy control chart with the fuzzy sampling method showed better characteristics.

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Development of Neuro-Fuzzy System for Cold Storage Facility (저온저장고의 뉴로-퍼지 제어시스템 개발)

  • 양길모;고학균;홍지향
    • Journal of Biosystems Engineering
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    • v.28 no.2
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    • pp.117-126
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    • 2003
  • This study was conducted to develop precision control system fur cold storage facility that could offer safe storage environment for green grocery. For that reason of neuro-fuzzy control system with learning ability algorithm and single chip neuro-fuzzy micro controller was developed for cold storage facility. Dynamic characteristics and hunting of neuro-fuzzy control system were far superior to on-off and fuzzy control system. Dynamic characteristics of temperature were faster than on-off control system by 1,555 seconds(123% faster) and fuzzy control system by 460 seconds(36.4% faster). When system was arrived at steady state. hunting was ${\pm}$0.5$^{\circ}C$ in on-off control system, ${\pm}$0.4$^{\circ}C$ in fuzzy control system, and ${\pm}$0.3$^{\circ}C$ in neuro-fuzzy control system. Hunting of humidity and wind velocity was also controlled precisely by 70 to 72.5% and 1m/s For storage experiment with onion, characteristics of neuro-fuzzy control system were tested. Dynamic characteristics of neuro-fuzzy control system made cold storage facility conducted precooling ability and minimized hunting.

A FUZZY PID Control of Supply Duct Outlet Air Temperature for PEM (FUZZY PID 방법을 이용한 개별 공조시스템의 급기온도 제어)

  • 장영준;박영철;정광섭;한화택;이정재
    • Korean Journal of Air-Conditioning and Refrigeration Engineering
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    • v.14 no.4
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    • pp.278-284
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    • 2002
  • The work presented here provides a control of the supply duct outlet air temperature in PEM (personal environment module) using fuzzy PID controller. In previous work, PID control systems were used, but the result shows that the outlet air temperature and electric heater regulating voltage were oscillated. Fuzzy PID control systems are designed to improve the system response obtained using PID control and implemented experimentally Also, PID controller and fuzzy controller without PID logic are provided to compare the result with that of the fuzzy PID controller. Data obtained shows that the fuzzy PID control system satisfies the design criteria and works proper1y in controlling the supply air temperature. Also it has bettor performance than the previous result obtained using PID control.

A Study on Filament Winding Tension Control using a fuzzy-PID Algorithm (퍼지-PID 알고리즘을 이용한 필라멘트 와인딩 장력제어에 관한 연구)

  • 이승호;이용재;오재윤
    • Journal of the Korean Society for Precision Engineering
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    • v.21 no.3
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    • pp.30-37
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    • 2004
  • This thesis develops a fuzzy-PID control algorithm for control the filament winding tension. It is developed by applying classical PID control technique to a fuzzy logic controller. It is composed of a fuzzy-PI controller and a fuzzy-D controller. The fuzzy-PI controller uses error and integrated error as inputs, and the fuzzy-D controller uses derivative of error as input. The fuzzy-PI controller uses Takagi-Sugeno fuzzy inference system, and the fuzzy-D controller uses Mamdani fuzzy inference system. The fuzzy rule base for the fuzzy-PI controller is designed using 19 rules, and the fuzzy rule base for the fuzzy-D controller is designed using 5 rules. A test-bed is set-up for verifying the effectiveness of the developing control algorithm in control the filament winding tension. It is composed of a mandrel, a carriage, a force sensor, a driving roller, nip rollers, a creel, and a real-time control system. Nip rollers apply a vertical force to a filament, and the driving roller drives it. The real-time control system is developed by using MATLAB/xPC Target. First, experiments for showing the inherent problems of an open-loop control scheme in a filament winding are performed. Then, experiments for showing the robustness of the developing fuzzy-PID control algorithm are performed under various working conditions occurring in a filament winding such as mandrel rotating speed change, carriage traversing, spool radius change, and reference input change.