• Title/Summary/Keyword: Fuzzy logic speed control

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A study on Moving OBstacle Avoidance for an Intelligent Vehicle Using Fuzzy Controller (퍼지 제어기를 이용한 지능형 차량의 이동장애물 회피에 관한 연구)

  • Kim, Hun-Mo
    • Journal of Institute of Control, Robotics and Systems
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    • v.6 no.2
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    • pp.155-163
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    • 2000
  • This paper presents a path planning method of the sensor based intelligent vehicle using fuzzy logic controller for avoidance of moving obstacles in unknown environments. Generally it is too difficult and complicated to control intelligent vehicle properly by recognizing unknown terrain with sensors because the great amount of imprecise and ambiguous information has to be considered. In this respect a fuzzy logic can manage such the enormous information in a quite efficient manner. Furthermore it is necessary to use the relative velocity to consider the mobility of obstacles, In order to avoid moving obstacles we must deliberate not only vehicle's relative speed toward obstacles but also self-determined acceleration and steering for the satisfaction of avoidance efficiency. In this study all the primary factors mentioned before are used as the input elements of fuzzy controllers and output signals to control velocity and steering angle of the vehicle. The main purpose of this study is to develop fuzzy controllers for avoiding collision with moving obstacles when they approach the vehicle travelling with straight line and for returning to original trajectory. The ability are and effectiveness of the proposed algorithm are demonstrated by simulations and experiments.

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The Design of Fuzzy P+ID Controller for Brushless DC Motor Speed Control (BLDC 전동기의 속도 제어를 위한 퍼지 P+ID 제어기 설계)

  • Kim, Young-Sik;Kim, Sung-Joong
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.7 no.5
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    • pp.823-829
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    • 2006
  • In this paper presents approaches to the design of a hybrid fuzzy logic proportional plus conventional integral- derivative(fuzzy P+ID) controller in an incremental form. This controller is constructed by using an incremental fuzzy logic controller in place of the proportional term in a conventional PID controller. The PID type controller has been widely used in industrial application due to its simply control structure, easy of design, and inexpensive cost. However, control performance of the PID type controller suffers greatly from high uncertainty and nonlinearity of the system, large disturbances and so on. This paper presents a hybrid fuzzy logic proportional plus conventional integral derivative controller In comparison with a conventional PID controller, only one additional parameter has to be adjusted to tune the Fuzzy P+ID controller. In this case, the stability of a system remains unchanged after the PID controller is replaced by the Fuzzy P+ID controller without modifying the original controller parameters. Finally, the proposed hybrid Fuzzy P+ID controller is applied to BLDC motor drive. Simulation results demonstrated that the control performance of the proposed controller is better than that of the conventional controller.

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Design of RFNN Controller for high performance Control of SynRM Drive (SynRM 드라이브의 고성능 제어를 위한 RFNN 제어기 설계)

  • Ko, Jae-Sub;Chung, Dong-Hwa
    • Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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    • v.25 no.9
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    • pp.33-43
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    • 2011
  • Since the fuzzy neural network(FNN) is universal approximators, the development of FNN control systems have also grown rapidly to deal with non-linearities and uncertainties. However, the major drawback of the existing FNNs is that their processor is limited to static problems due to their feedforward network structure. This paper proposes the recurrent FNN(RFNN) for high performance and robust control of SynRM. RFNN is applied to speed controller for SynRM drive and model reference adaptive fuzzy controller(MFC) that combine adaptive fuzzy learning controller(AFLC) and fuzzy logic control(FLC), is applied to current controller. Also, this paper proposes speed estimation algorithm using artificial neural network(ANN). The proposed method is analyzed and compared to conventional PI and FNN controller in various operating condition such as parameter variation, steady and transient states etc.

Design of Fuzzy Controller for the Improvement of Auto-Vehicle's Comfortability (무인 자동차의 승차감 개선을 위한 퍼지제어기의 설계)

  • Cho, H.R.;Kang, G.M.;Bae, J.I.;Jo, B.K.;Kim, Y.S.;Yang, S.Y.
    • Proceedings of the KIEE Conference
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    • 1998.07b
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    • pp.678-680
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    • 1998
  • Based on fuzzy logic algorithm this paper constructed fuzzy logic controller for automated vehicles. For passenger's convenience especially comfortability controller need to reduce the frequency of input variable's changing. So we established membership functions for comfortability as mil as speed following. It made possible to control comfortability directly. To demonstration the efficiency of fuzzy logic controller, we carried out simulation with a Automobile's transfer function. First, we designed the PID controller by using Ziegler-Nichols tunning method. Second, we calculated time response for each controller, then we compared the speed patterns of fuzzy controlled system and PID controlled system. Also we compared the difference of input variable. By comparing two controller's response, we can confirm the merit of fuzzy controller about comfortability. Fuzzy controller can reduce input changing frequency.

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Variable Step-Size MPPT Control based on Fuzzy Logic for a Small Wind Power System (소형풍력발전시스템을 위한 퍼지로직 기반의 가변 스텝 사이즈 MPPT 제어)

  • Choi, Dae-Keun;Lee, Kyo-Beum
    • The Transactions of the Korean Institute of Power Electronics
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    • v.17 no.3
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    • pp.205-212
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    • 2012
  • This paper proposes the fuzzy logic based variable step-size MPPT (Maximum Power Point Tracking) method for the stability at the steady state and the improvement of the transient response in the wind power system. If the change value of duty ratio is set on stability of the steady state, MPPT control traces to maximum power point slowly. And if the change value is set on improvement of the transient response, the system output oscillates at the maximum power point. By adjusting the step size with fuzzy logic, it can be improved the MPPT response speed and stability at steady state when MPPT control is performed to track the maximum power point. The effectiveness of the proposed method has been verified by simulations and experimental results.

Position Control of The Robot Manipulator Using Fuzzy Logic and Multi-layer Neural Network (퍼지논리와 다층 신경망을 이용한 로봇 매니퓰레이터의 위치제어)

  • Kim, Jong-Soo;Jeon, Hong-Tae
    • Journal of the Korean Institute of Intelligent Systems
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    • v.2 no.1
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    • pp.17-32
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    • 1992
  • The multi-layer neural network that has broadly been utilized in designing the controller of robot manipulator possesses the desirable characteristics of learning capacity, by which the uncertain variation of the dynamic parameters of robot can be handled adaptively, and parallel distributed processing that makes it possible to control on real-time. However the error back propagation algorithm that has been utilized popularly in the learning of the multi-layer neural network has the problem of its slow convergence speed. In this paper, an approach to improve the convergence speed is proposed using the fuzzy logic that can effectively handle the uncertain and fuzzy informations by linguistic level. The effectiveness of the proposed algorithm is demonstrated by computer simulation of PUMA 560 robot manupulator.

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Fuzzy-Neuro Controller for Speed of Slip Energy Recovery and Active Power Filter Compensator

  • Tunyasrirut, S.;Ngamwiwit, J.;Furuya, T.;Yamamoto, Y.
    • 제어로봇시스템학회:학술대회논문집
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    • 2000.10a
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    • pp.480-480
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    • 2000
  • In this paper, we proposed a fuzzy-neuro controller to control the speed of wound rotor induction motor with slip energy recovery. The speed is limited at some range of sub-synchronous speed of the rotating magnetic field. Control speed by adjusting resistance value in the rotor circuit that occurs the efficiency of power are reduced, because of the slip energy is lost when it passes through the rotor resistance. The control system is designed to maintain efficiency of motor. Recently, the emergence of artificial neural networks has made it conductive to integrate fuzzy controllers and neural models for the development of fuzzy control systems, Fuzzy-neuro controller has been designed by integrating two neural network models with a basic fuzzy logic controller. Using the back propagation algorithm, the first neural network is trained as a plant emulator and the second neural network is used as a compensator for the basic fuzzy controller to improve its performance on-line. The function of the neural network plant emulator is to provide the correct error signal at the output of the neural fuzzy compensator without the need for any mathematical modeling of the plant. The difficulty of fine-tuning the scale factors and formulating the correct control rules in a basic fuzzy controller may be reduced using the proposed scheme. The scheme is applied to the control speed of a wound rotor induction motor process. The control system is designed to maintain efficiency of motor and compensate power factor of system. That is: the proposed controller gives the controlled system by keeping the speed constant and the good transient response without overshoot can be obtained.

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Position Control of the Robot Manipulator Using Fuzzy Logic and Multi-layer neural Network (퍼지논리와 다층 신경망을 이용한 로보트 매니퓰레이터의 위치제어)

  • 김종수;이홍기;전홍태
    • Journal of the Korean Institute of Telematics and Electronics B
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    • v.28B no.11
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    • pp.934-940
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    • 1991
  • The multi-layer neural network that has broadly been utilized in designing the controller of robot manipulator possesses the desirable characteristics of learning capacity, by which the uncertain variation of the dynamic parameters of robot can be handled adaptively, and parallel distributed processing that makes it possible to control on real-time. However the error back propagation algorithm that has been utilized popularly in the learning of the multi-layer neural network has the problem of its slow convergencs speed. In this paper, an approach to improve the convergence speed is proposed using fuzzy logic that can effectively handle the uncertain and fuzzy informations by linguistic level. The effectiveness of the proposed algorithm is demonstrated by computer simulation of PUMA 560 robot manipulator.

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A New Effective Learning Algorithm for a Neo Fuzzy Neuron Model

  • Yamakawa, Takeshi;Kusanagi, Hiroaki;Uchino, Eiji;Miki, Tsutomu
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 1993.06a
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    • pp.1017-1020
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    • 1993
  • This paper describes a neo fuzzy neuron which was produced by a fusion of fuzzy logic and neuroscience. Some learning algorithms are presented. The guarantee for the global minimum on the error-weight space is proved by a reduction to absurdity. Enhanced is that the learning speed of the neo fuzzy neuron exceeds 100,000 times of that of conventional multi-layer neural networks.

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A Comparison of Different Intelligent Control Techniques For a PM dc Motor

  • Amer S. I.;Salem M. M.
    • Journal of Power Electronics
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    • v.5 no.1
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    • pp.1-10
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    • 2005
  • This paper presents the application of a simple neuro-based speed control scheme of a permanent magnet (PM) dc motor. To validate its efficiency, the performance characteristics of the proposed simple neuro-based scheme are compared with those of a Neural Network controller and those of a Fuzzy Logic controller under different operating conditions. The comparative results show that the simple neuro-based speed control scheme is robust, accurate and insensitive to load disturbances.