• 제목/요약/키워드: Digital servo control algorithm

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A Study on Precision Position Measurement Method for Analog Quadrature Encoder (정현파 엔코더를 이용한 정밀위치 측정방법에 관한 연구)

  • Kim Myong-Hwan;Kim Jang-Mok;Kim Cheul-U
    • The Transactions of the Korean Institute of Power Electronics
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    • v.9 no.5
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    • pp.485-490
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    • 2004
  • This paper presents a new interpolation algorithm for measuring high resolution position information which is prepared to a nino servo control motor using analog quadrature encoder. In the past, there are large capacity of memory(ROM or RAM) and two high price and resolution A/D(Analog-to-Digital Converter) for sensing two quadrature signals from a analog sinusoidal encoder interpolation. But high resolution of position from sinusoidal encoder can be obtained by using only small capacity of memory, one A/D converter and comparator. Experimental results show that the proposed algorithm is useful for measuring high resolution position.

The Development of a Miniature Humanoid Robot System (소형 휴머노이드 로븟 시스템 개발)

  • 성영휘;이수영
    • 제어로봇시스템학회:학술대회논문집
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    • 2000.10a
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    • pp.43-43
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    • 2000
  • In this paper, we introduce a case study of developing a miniature humanoid robot that has 16 degrees of freedom and is able to perform statically stable walking. The developed humanoid robot is 37cm tall and weighs 1,200g. RC servo motors are used as actuators. The robot can walk forward and turn to any direction on even surface. It equipped with a small digital camera, so it can transmit vision data to a remote host computer via wireless modem. The robot can be operated in two modes; One is a remote-controlled mode, in which the robot behaves according to the command given by a human operator through the user-interface program running on a remote host computer, the other is a stand-alone mode, in which the robot behaves autonomously according to the pre-programmed strategy. The user-interface program also contains a robot graphic simulator that is used to produce and verify the robot's gait motion. In our walking algorithm, the ankle joint is mainly used lot balancing the robot. The experimental results shows that the developed robot can perform statically stable walking on even surface.

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The Development of a Miniature Humanoid Robot System (소형 휴머노이드 로봇 시스템 개발)

  • Sung, Young-Whee;Yi, Soo-Yeong
    • Journal of Institute of Control, Robotics and Systems
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    • v.7 no.5
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    • pp.420-426
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    • 2001
  • In this paper, we introduce a case study of developing a miniature humanoid robot that has 16 degrees of freedom and is able to perform statically stable walking. The developed humanoid robot is 37cm tall and weighs 1,200g. RC servo motors are used as actuators. The robot can walk forward and turn to any direction on an even surface. It equipped with a small digital camera, so it can transmit vision data to a remote host computer via wireless modem. The robot can be operated in two modes: One is a remote-controlled mode, in which the robot behaves according to the command given by a human operator through the user-interface program running on a remote host computer, the other is a stand-alone mode, in which the robot behaves autonomously according the pre-programmed strategy. The user-interface program also contains a robot graphic simulator that is used to produce and verify the robot\`s gait motion. In our walking algorithm, the ankle joint is mainly used for balancing the robot. The experimental results shows that the developed robot can perform statically stable walking on an even surface.

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Diminution of Current Measurement Error in Vector Controlled AC Motor Drives

  • Jung Han-Su;Kim Jang-Mok;Kim Cheul-U;Choi Cheol;Jung Tae-Uk
    • Journal of Power Electronics
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    • v.5 no.2
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    • pp.151-159
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    • 2005
  • The errors generated from current measurement paths are inevitable, and they can be divided into two categories: offset error and scaling error. The current data including these errors cause periodic speed ripples which are one and two times the stator electrical frequency respectively. Since these undesirable ripples bring about harmful influences to motor driving systems, a compensation algorithm must be introduced to the control algorithm of the motor drive. In this paper, a new compensation algorithm is proposed. The signal of the integrator output of the d-axis current regulator is chosen and processed to compensate for the current measurement errors. Usually the d-axis current command is zero or constant to acquire the maximum torque or unity power factor in the ac drive system, and the output of the d-axis current regulator is nearly zero or constant as well. If the stator currents include the offset and scaling errors, the respective motor speed produces a ripple related to one and two times the stator electrical frequency, and the signal of the integrator output of the d-axis current regulator also produces the ripple as the motor speed does. The compensation of the current measurement errors is easily implemented to smooth the signal of the integrator output of the d-axis current regulator by subtracting the DC offset value or rescaling the gain of the hall sensor. Therefore, the proposed algorithm has several features: the robustness in the variation of the mechanical parameters, the application of the steady and transient state, the ease of implementation, and less computation time. The MATLAB simulation and experimental results are shown in order to verify the validity of the proposed current compensating algorithm.

Simple Robust Digital Position Control Algorithm of BLDD Motor using Neural Network with State Feedback (상태궤환과 신경망을 이용한 BLDD Motor의 간단한 강인 위치 제어 알고리즘)

  • 고종선;안태천
    • The Transactions of the Korean Institute of Power Electronics
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    • v.3 no.3
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    • pp.214-221
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    • 1998
  • A new control approach using neural network for the robust position control of a BRUSHLESS direct drive(BLDD) motor is presented. The linear quadratic controller plus feedforward neural network is employed to obtain the robust BLDD motor system approximately linearized using field-orientation method for an AC servo. The neural network is trained in on-line phases and this neural network is composed by a feedforward recall and error back-propagation training. Since the total number of nodes are only eight, this system will be easily realized by the general microprocessor. During the normal operation, the input-output response is sampled and the weighting value is trained by error back-propagation at each sample period to accommodate the possible variations in the parameters or load torque. And the state space analysis is performed to obtain the state feedback gains systematically. In addition, the robustness is also obtained without affecting overall system response.

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