• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.32B no.9
• /
• pp.1171-1180
• /
• 1995

In this paper, we present a 3-D object recognition system in which the 3-D Hough transform domain is employed to represent the 3-D objects. In object modeling step, the features for recognition are extracted from the CAD models of objects to be recognized. Since the approach is based on the CAD models, the accuracy and flexibility are greatly improved. In matching stage, the sensed image is compared with the stored model, which is assumed to yield a distortion (location and orientation) in the 3-D Hough transform domain. The high dimensional (6-D) parameter space, which defines the distortion, is decomposed into the low dimensional space for an efficient recognition. At first we decompose the distortion parameter into the rotation parameter and the translation parameter, and the rotation parameter is further decomposed into the viewing direction and the rotational angle. Since we use the 3-D Hough transform domain of the input images directly, the sensitivity to the noise and the high computational complexity could be significantly alleviated. The results show that the proposed 3-D object recognition system provides a satisfactory performance on the real range images.

• Proceedings of the KIPE Conference
• /
• 2001.07a
• /
• pp.389-392
• /
• 2001

This paper presents neural load disturbance observer that used to deadbeat load torque observer and regulation of the compensation gain by parameter estimator. As a result, the response of PMSM follows that of the nominal plant. The load torque compensation method is compose of a neural deadbeat observer. To reduce of the noise effect, the post-filter, which is implemented by MA process, is proposed. The parameter compensator with RLSM (recursive least square method) parameter estimator is suggested to increase the performance of the load torque observer and main controller. The proposed estimator is combined with a high performance neural torque observer to resolve the problems. As a result, the proposed control system becomes a robust and precise system against the load torque and the parameter variation. A stability and usefulness, through the verified computer simulation, are shown in this paper.

• Journal of Power System Engineering
• /
• v.10 no.4
• /
• pp.147-152
• /
• 2006

The current type amplifier is the device that is used for an actuator as the motor's torque controller. However, it is too difficult to select the parameter value that has the desired output because the current type amplifier's transfer function is too complex. This study concern about the design of the current type amplifier with the desired output. From the modeled transfer function of the current type amplifier, the optimal parameter values of the transfer function can be selected in order to have the desired output using the Real Coded Genetic Algorithm(RCGA). The real circuit is made with the selected parameter value. The step response of the real circuit is in good agreement with the desired step response.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.11 no.5
• /
• pp.151-157
• /
• 2011

This study analyzed the posture balance of time variation for exercising body a period of time. Posture balance measured output values for the posture balance system of body moving in the multi-parameter. Posture moving variation had three methods such as open and closed eye, head moving and upper body moving. There were checked a parameter that measured vision, vestibular, somatosensory, CNS. This system was evaluated a data through the stability. This system has catched a signal for physical condition of body data such as a data acquisition system, data signal processing and feedback system. The output signal was generated Fourier analysis that using frequency of 0.1Hz, 0.1-0.5Hz, 0.5-1Hz and 1Hz over. The posture balance system will be used to support assessment for body moving the posture balance of time variation. It was expected to monitor a physical parameter for health verification system.

• The Transactions of the Korean Institute of Electrical Engineers B
• /
• v.53 no.3
• /
• pp.188-195
• /
• 2004

This paper presents neural load torque observer that is used to deadbeat load torque observer and gain compensation by parameter estimator As a result, the response of the PMSM(permanent magnet synchronous motor) follows that nominal plant. The load torque compensation method is composed of a neural deadbeat observer To reduce the noise effect, the post-filter implemented by MA(moving average) process, is adopted. The parameter compensator with RLSM (recursive least square method) parameter estimator is adopted to increase the performance of the load torque observer and main controller The parameter estimator is combined with a high performance neural load torque observer to resolve the problems. The neural network is trained in on-line phases and it is composed by a feed forward recall and error back-propagation training. During the normal operation, the input-output response is sampled and the weighting value is trained multi-times by error back-propagation method at each sample period to accommodate the possible variations in the parameters or load torque. As a result, the proposed control system has a robust and precise system against the load torque and the Parameter variation. A stability and usefulness are verified by computer simulation and experiment.

• Journal of Power Electronics
• /
• v.8 no.4
• /
• pp.354-362
• /
• 2008

This paper presents neural load torque compensation method which is composed of a deadbeat load torque observer and gains compensation by a parameter estimator. As a result, the response of the PMSM (permanent magnet synchronous motor) obtains better precision position control. To reduce the noise effect, the post-filter is implemented by a MA (moving average) process. The parameter compensator with an RLSM (recursive least square method) parameter estimator is adopted to increase the performance of the load torque observer and main controller. The parameter estimator is combined with a high performance neural load torque observer to resolve problems. The neural network is trained in online phases and it is composed by a feed forward recall and error back-propagation training. During normal operation, the input-output response is sampled and the weighting value is trained multi-times by the error back-propagation method at each sample period to accommodate the possible variations in the parameters or load torque. As a result, the proposed control system has a robust and precise system against load torque and parameter variation. Stability and usefulness are verified by computer simulation and experiment.

• Journal of Power Electronics
• /
• v.6 no.3
• /
• pp.235-244
• /
• 2006

Recently, speed sensorless vector control for synchronous reluctance motors (SYRMs) has deserved attention because of its advantages. Although rotor angle calculation using flux estimation is a straightforward approach, the DC offset can cause an increasing pure integrator error in this estimator. In addition, this method is affected by parameter fluctuation. In this paper, to control the motor at the low speed region, a modified programmable cascaded low pass filter (MPCPLF) with sensorless online parameter identification based on a block pulse function is proposed. The use of the MPCLPF is suggested because in programmable, cascade low pass filters (PCLPF), which previously have been applied to induction motors, the drift increases vastly wl)en motor speed decreases. Parameter identification is also used because it does not depend on estimation accuracy and can solve parameter fluctuation effects. Thus, sensorless speed control in the low speed region is possible. The experimental system includes a PC-based control with real time Linux and an ALTERA Complex Programmable Logic Device (CPLD), to acquire data from sensors and to send commands to the system. The experimental results show the proposed method performs well, speed and angle estimation are correct. Also, parameter identification and sensorless vector control are achieved at low speed, as well as, as at high speed.

• Proceedings of the KSR Conference
• /
• 2011.10a
• /
• pp.3133-3141
• /
• 2011

The next generation domestic high speed railway system is a power distributed type and uses vector control method for motor speed control. Nowadays, inverter driven induction motor system is widely used. However, recently PMSM drives are deeply considered as a alternative candidate instead of an induction motor drive system due to their advantages in efficiency, noise reduction and maintenance. The next-generation high speed train is composed of 2 converter units, 4 inverter units, and 4 Traction Motor units. Each motor is connected to the inverter directly. In this paper, the effect of IPMSM parameter variations to the system operation characteristics of the multi inverter drive high speed train system are investigated. The parallel connected inverter input-output characteristics are analyzed to the parameter mismatches of IPMSM using the 1C1M control simulator based on Matlab/Simulink.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.4 no.2
• /
• pp.157-178
• /
• 2001

In this paper, the nonlinear control of high precision pointing stabilization system using feedback-linearization design methodology based on system parameter identification is discussed. Modern nonlinear servomechanism theory is adapted to cope with the hard nonlinearities inherent in the turret system. The mathematical models of electrical turret driving system to develop a high performance control algorithm are derived, and the parameter estimation algorithm identifying the unknown system parameters such as vicious and coulomb frictions, stiffness and inertia is developed. Through computer simulation and experiments, it is shown that pointing and tracking accuracy and stabilization against the wideband stochastic disturbance induced by vehicle running on the bump course are improved. Therefore, it is considered the proposed nonlinear control technique is effective in counteracting the nonlinearities and disturbances.

• Journal of Institute of Control, Robotics and Systems
• /
• v.4 no.6
• /
• pp.729-737
• /
• 1998

In this paper, using $\mu$ synthesis algorithm with structured uncertainty, we design controller and apply it for the Two-Inertia resonance(TMS: Two Mass Spring) system. The TMS system is one of the simplest models which generate a torsional vibration. In this system, it is required to design a controller achieving the control performance while suppressing the torsional vibration. Furthermore, when vibration frequency for the system is varying by reason of parameter variations, we should consider parameter variations in controller design. Then, we design two other controller schemes of the PI controller and the standard $H_{\infty}$ controller and compare these controllers with the controller designed by the $\mu$ synthesis robust control method by using simulations and experiments.