• Title/Summary/Keyword: Fuzzy control

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A Fuzzy Controller using Fuzzy Relations on Input Variables

  • Lee, Jihong
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 1993.06a
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    • pp.895-898
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    • 1993
  • Instead of Cartesian product for in combining multiple inputs for fuzzy logic controllers, a method using fuzzy relation in inference is proposed. Moreover, fuzzy control rule described by fuzzy relations is derived from given conventional fuzzy control rule by fitting concept. It will be shown through several examples that the proposed technique gives smoother interpolation than conventional ones.

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Stable Path Tracking Control of a Mobile Robot Using a Wavelet Based Fuzzy Neural Network

  • Oh, Joon-Seop;Park, Jin-Bae;Choi, Yoon-Ho
    • International Journal of Control, Automation, and Systems
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    • v.3 no.4
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    • pp.552-563
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    • 2005
  • In this paper, we propose a wavelet based fuzzy neural network (WFNN) based direct adaptive control scheme for the solution of the tracking problem of mobile robots. To design a controller, we present a WFNN structure that merges the advantages of the neural network, fuzzy model and wavelet transform. The basic idea of our WFNN structure is to realize the process of fuzzy reasoning of the wavelet fuzzy system by the structure of a neural network and to make the parameters of fuzzy reasoning be expressed by the connection weights of a neural network. In our control system, the control signals are directly obtained to minimize the difference between the reference track and the pose of a mobile robot via the gradient descent (GD) method. In addition, an approach that uses adaptive learning rates for training of the WFNN controller is driven via a Lyapunov stability analysis to guarantee fast convergence, that is, learning rates are adaptively determined to rapidly minimize the state errors of a mobile robot. Finally, to evaluate the performance of the proposed direct adaptive control system using the WFNN controller, we compare the control results of the WFNN controller with those of the FNN, the WNN and the WFM controllers.

The MPPT Control of Photovoltaic System using the Fuzzy PI Controller (퍼지 PI 제어기를 이용한 태양광 발전시스템의 MPPT 제어)

  • Ko, Jae-Sub;Chung, Dong-Hwa
    • Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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    • v.28 no.2
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    • pp.9-18
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    • 2014
  • This paper proposes the fuzzy PI controller for maximum power point tracking(MPPT) control of photovoltaic system. The output characteristics of the solar cell are a nonlinear and affected by a temperature, the solar radiation. The MPPT control is a very important technique in order to increase an output and efficiency of the photovoltaic system. The conventional perturbation and observation(PO) and incremental conductance(IC) are the method which finding maximum power point(MPP) by the continued self-excitation vibration, and uses the fixed step size. If the fixed step size is a large, the tracking speed of maximum power point is faster, but the tracking accuracy in the steady state is decreased. On the contrary, when the fixed step size is a small, the tracking accuracy is increased and the tracking speed is slower. Therefore, this paper proposes the MPPT control using the fuzzy PI controller that can be improve a MPPT control performance. The fuzzy PI controller is adjusted a input of PI controller by fuzzy control and compensated a cumulative error of fuzzy control by PI controller. The fuzzy PI MPPT control is compared to conventional PO and IC MPPT method for various temperature and radiation condition. This paper proves the validity of the fuzzy PI controller using these results.

A New Approach to Adaptive Damping Control for Statistic VAR Compensators Based on Fuzzy Logic

  • Sedaghati, Alireza
    • 제어로봇시스템학회:학술대회논문집
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    • 2005.06a
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    • pp.825-829
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    • 2005
  • This paper presents an approach for designing a fuzzy logic-based adaptive SVC damping In controller for damping low frequency power oscillations. Power systems are often subject to low Frequency electro-mechanical oscillations resulting from electrical disturbances. Generally, power system stabilizers are designed to provide damping against this kind of oscillations. Another means to achieve damping is to design supplementary damping controllers that are equipped with SVC. Various approaches are available for designing such controllers, many of which are based on the concepts of damping torque and others which treat the damping controller design as a generic control problem and apply various control theories on it. In our proposed approach, linear optimal controllers are designed and then a fuzzy logic tuning mechanism is constructed to generate a single control signal. The controller uses the system operating condition and a fuzzy logic signal tuner to blend the control signals generated by two linear controllers, which are designed using an optimal control method. First, we design damping controllers for the two extreme conditions; the control action for intermediate conditions is determined by the fuzzy logic tuner. The more the operating condition belongs to one of the two fuzzy sets, the stronger the contribution of the control signal from that set in the output signal. Simulation studies done on a one-machine infinite-bus and a four-machine two-area test system, show that the proposed fuzzy adaptive damping SVC controller effectively enhances the damping of low frequency oscillations.

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Traffic Fuzzy Control : Software and Hardware Implementations

  • Jamshidi, M.;Kelsey, R.;Bisset, K.
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 1993.06a
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    • pp.907-910
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    • 1993
  • This paper describes the use of fuzzy control and decision making to simulate the control of traffic flow at an intersection. To show the value of fuzzy logic as an alternative method for control of traffic environments. A traffic environment includes the lanes to and from an intersection, the intersection, vehicle traffic, and signal lights in the intersection. To test the fuzzy logic controller, a computer simulation was constructed to model a traffic environment. A typical cross intersection was chosen for the traffic environment, and the performance of the fuzzy logic controller was compared with the performance of two different types of conventional control. In the hardware verifications, fuzzy logic was used to control acceleration of a model train on a circular path. For the software experiment, the fuzzy logic controller proved better than conventional control methods, especially in the case of highly uneven traffic flow between different directions. On the hardware si e of the research, the fuzzy acceleration control system showed a marked improvement in smoothness of ride over crisp control.

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On the Design of Simple-structured Adaptive Fuzzy Logic Controllers

  • Park, Byung-Jae;Kwak, Seong-Woo
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.3 no.1
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    • pp.93-99
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    • 2003
  • One of the methods to simplify the design process for a fuzzy logic controller (FLC) is to reduce the number of variables representing the rule antecedent. This in turn decreases the number of control rules, membership functions, and scaling factors. For this purpose, we designed a single-input FLC that uses a sole fuzzy input variable. However, it is still deficient in the capability of adapting some varying operating conditions although it provides a simple method for the design of FLC's. We here design two simple-structured adaptive fuzzy logic controllers (SAFLC's) using the concept of the single-input FLC. Linguistic fuzzy control rules are directly incorporated into the controller by a fuzzy basis function. Thus some parameters of the membership functions characterizing the linguistic terms of the fuzzy control rules can be adjusted by an adaptive law. In our controllers, center values of fuzzy sets are directly adjusted by an adaptive law. Two SAFLC's are designed. One of them uses a Hurwitz error dynamics and the other a switching function of the sliding mode control (SMC). We also prove that 1) their closed-loop systems are globally stable in the sense that all signals involved are bounded and 2) their tracking errors converge to zero asymptotically. We perform computer simulations using a nonlinear plant.

Adaptive Fuzzy Logic Control Using a Predictive Neural Network (예측 신경망을 이용한 적응 퍼지 논리 제어)

  • 정성훈
    • Journal of the Korean Institute of Intelligent Systems
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    • v.7 no.5
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    • pp.46-50
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    • 1997
  • In fuzzy logic control, static fuzzy rules cannot cope with significant changes of parameters of plants or environment. To solve this prohlem, self-organizing fuzzy control. neural-network-hased fuzzy logic control and so on have heen introduced so far. However, dynamically changed fuzzy rules of these schemes may make a fuzzy logic controller Fall into dangerous situations because the changed fuzzy rules may he incomplete or inconsistent. This paper proposes a new adaptive filzzy logic control scheme using a predictivc neural network. Although some parameters of a controlled plant or environment are changed, proposed fuzzy logic controller changes its decision outputs adaptively and robustly using unchanged initial fuzzy rules and the predictive errors generated hy the predictive neural network by on-line learning. Experimental results with a D<' servo-motor position control problem show that propnsed cnntrol scheme is very useful in the viewpoint of adaptability.

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A Design of Control Chart for Fraction Nonconforming Using Fuzzy Data (퍼지 데이터를 이용한 불량률(p) 관리도의 설계)

  • 김계완;서현수;윤덕균
    • Journal of Korean Society for Quality Management
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    • v.32 no.2
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    • pp.191-200
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    • 2004
  • Using the p chart is not adequate in case that there are lots of data and it is difficult to divide into products conforming or nonconforming because of obscurity of binary classification. So we need to design a new control chart which represents obscure situation efficiently. This study deals with the method to performing arithmetic operation representing fuzzy data into fuzzy set by applying fuzzy set theory and designs a new control chart taking account of a concept of classification on the term set and membership function associated with term set.

A Fuzzy BOXES Scheme for the Cartpole Control

  • Kwon, Sung-Gyu
    • 제어로봇시스템학회:학술대회논문집
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    • 2005.06a
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    • pp.1710-1715
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    • 2005
  • Two fuzzy controllers are coordinated to control a cartpole such that the pole is balanced as well as the cart is brought back to the track origin. The coordination is due to the BOXES scheme that is established through the evaluation of the outcomes of the control action by one of the fuzzy controllers. It is found that the control scheme is good at selecting proper fuzzy controller so that the pole is balanced fast while the cart moves back to the track origin steadily.

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Fuzzy control by identification of fuzzy model of dynamic systems (다이나믹시스템의 퍼지모델 식별을 통한 퍼지제어)

  • 전기준;이평기
    • 제어로봇시스템학회:학술대회논문집
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    • 1990.10a
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    • pp.127-130
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    • 1990
  • The fuzzy logic controller which can be applied to various industrial processes is quite often dependent on the heuristics of the experienced operator. The operator's knowledge is often uncertain. Therefore an incorrect control rule on the basis of the operator's information is a cause of bad performance of the system. This paper proposes a new self-learning fuzzy control method by the fuzzy system identification using the data pairs of input and output and arbitrary initial relation matrix. The position control of a DC servo motor model is simulated to verify the effectiveness of the proposed algorithm.

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