• Journal of Power Electronics
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• v.17 no.1
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• pp.76-87
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• 2017

Finite-set predictive current control (FS-PCC) is advantageous for power converters due to its high dynamic performance and has received increasing interest in multilevel inverters. Among multilevel inverter topologies, the cascaded H-bridge (CHB) inverter is popular and mature in the industry. However, a main drawback of FS-PCC is its large computational burden, especially for the application of CHB inverters. In this paper, an FS-PCC method based on a deadbeat solution for three-phase zero-common-mode-voltage CHB inverters is proposed. In the proposed method, an inverse model of the load is utilized to calculate the reference voltage based on the reference current. In addition, a cost function is directly expressed in the terms of the voltage errors. An optimal control actuation is selected by minimizing the cost function. In the proposed method, only three instead of all of the control actuations are used for the calculations in one sampling period. This leads to a significant reduction in computations. The proposed method is tested on a three-phase 5-level CHB inverter. Simulation and experimental results show a very similar and comparable control performance from the proposed method compared with the traditional FS-PCC method which evaluates the cost function for all of the control actuations.

• Journal of Power Electronics
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• v.17 no.1
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• pp.262-271
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• 2017

This paper studies the performances of five typical Phase-locked Loops (PLLs) for distorted and unbalanced grid, which are the Decoupled Double Synchronous Reference Frame PLL (DDSRF-PLL), Double Second-Order Generalized Integrator PLL (DSOGI-PLL), Double Second-Order Generalized Integrator Frequency-Lock Loop (DSOGI-FLL), Double Inverse Park Transformation PLL (DIPT-PLL) and Complex Coefficient Filter based PLL (CCF-PLL). Firstly, the principles of each method are meticulously analyzed and their unified small-signal models are proposed to reveal their interior relations and design control parameters. Then the performances are compared by simulations and experiments to investigate their dynamic and steady-state performances under the conditions of a grid voltage with a negative sequence component, a voltage drop and a frequency step. Finally, the merits and drawbacks of each PLL are given. The compared results provide a guide for the application of current control, low voltage ride through (LVRT), and unintentional islanding detection.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.5
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• pp.634-642
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• 1993

In this paper a new trajectory control method is proposed for a robot manipulator using a time delay neural network(TDNN) as a feedforward controller with an algorithm to learn inverse dynamics of the manipulator. The TDNN structure has so favorable characteristics that neurons can extract more dynamic information from both present and past input signals and perform more efficient learning. The TDNN neural network receives two normalized inputs, one of which is the reference trajectory signal and the other of which is the error signals from the PD controller. It is proved that the normalized inputs to the TDNN neural network can enhance the learning efficiency of the neural network. The proposed scheme was investigated for the planar robot manipulator with two joints by computer simulation.

• 유체기계공업학회:학술대회논문집
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• 2006.08a
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• pp.209-212
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• 2006

Insect and birds in nature flap their wings to generate fluid dynamic forces that are required for the locomotion. Most of the previous published papers discussed mainly on the effect of flapping parameters such as flapping frequency and amplitude on the thrust at a fixed Reynolds number. However, it is not much known on the values of the flapping parameters that the flapping wing requires to generate the thrust at the low Reynolds number flow. In this paper, the onset of the thrust generation is investigated using the lattice Boltzmann method. The wake patterns and velocity profiles behind a flat plate in heaving oscillation are investigated for the heaving amplitude of 0.5C. The time-averaged thrust coefficient value is investigated by changing the reduced frequency from 0.125 to 3.0 for three values of heaving amplitude (h/C=0.25, 0.325, 0.50). It is also found that the critical Strouhal number over which the flat plate starts to produce the thrust is around 0.1 and the thrust is an exponential function of the Strouhal number.

• Transactions of the Korean Society of Mechanical Engineers
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• v.14 no.2
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• pp.298-309
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• 1990

In this paper, a new method is suggested to analyze impulsive stresses at loading poing of concentrated impact load under certain impact conditions determined by impact velocity, stiffness of plate and mass of impact body, etc. The impulsive stresses are analyzed by using the three dimensional dynamic theory of elasticity so as to analytically clarify the generation phenomenon of cone crack at the impact fracture of fragile materials (to be discussed if the second paper). The Lagrange's plate theory and Hertz's law of contact theory are used for the analysis of impact load, and the approximate equation of impact load is suggested to analyze the impulsive stresses at the impact point to decide the ranage of impact load factor. When impact load factors are over and under 0.263, approximate equations are suggested to be F(t)=Aexp(-Bt)sinCt and F(t)=Aexp(-bt) {1-exp(Ct)} respectively. Also, the inverse Laplace transformation is done by using the F.F.T.(fast fourier transform) algorithm. And in order to clarity the validity of stress analysis method, experiments on strain fluctuation at impact point are performed on a supported square glass plate. Finally, these analytical results are shown to be in close agreement with experimental results.

• Journal of Mechanical Science and Technology
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• v.19 no.spc1
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• pp.371-376
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• 2005

Relative contributions of lower extremity joints on the support moment were investigated in this study. Three-dimensional gait analyses were performed in normal walking and in unexpected step-down walking. For both gait studies, inverse dynamics were performed to obtain each joint moment of the lower extremity, which was applied to the forward dynamics simulation to determine the contributions on the support moment at different phases of walking. The forward dynamic simulation results showed that, in normal walking, the ankle plantar flexors contributed significantly during single-limb-support. However, the ankle plantar flexors, knee extensors and hip extensors worked together during double-limb-support. In unexpected step-down walking, the important contributors on the support of the body during single-limb-support were not only ankle plantar flexors but also knee extensors. This study, analyzing the relative contributions of the lower limb joint moments for the body support, would be helpful to understand different unexpected walking conditions and compensatory mechanisms for various pathological gaits.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1994.10a
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• pp.266-272
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• 1994

An essential ingredient in precision machining is a positioning system that responds quickly and precisely to very small input signal. In this paper, two different positioning systems were presented fot the precision positioning control. The one is a friction drive system, the other is a ballscrew system. The friction drive system was composed of an air sliding guide and a friction drive. The ballscrew system was made of a ballscrew and a linear guide. Nonlinear behaviors of the given systems tend to make the system inaccurate. The paper looked at the phenomena that has caused the positioning error. These apparently nonlinear phenomena can be attributed mainly to the presence of the nonlinear friction and slip effect plus the dynamic change from the microdynamic to the macrodynamic and form the macrodynamic to the microdynamic. For the control of the positioning system, the control algorithm based on a neural network is suggested. The FEL(Feedback Error Learning) controller can learn the inverse dynamics of a nonlinear system by using the neural network controller, and stabilize the system by a linear controller. In the experiment, PTP control is implemented withen the maximum error of 0.05 .mu.m ~0.1 .mu. m when i .mu.m step reference input is applied and that of maximum 1 .mu. m when 100 .mu.m step reference input is given. Sinusoidal inputs with the amplitude of 1 .mu.m and 100 .mu. m are used for the tracking control of the positioning system. Experimental results of the proposed algorithm are shown to be superior to those of conventional PD controls.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.8
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• pp.186-192
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• 1999

This paper introduces a study of a method for the forward kinematic analysis, which finds the 6 DOF motions and velocities from the given six cylinder lengths in the Stewart platform. From the viewpoints of kinematics, the solution for the inverse kinematic is easily found by using the vectors of the links which are composed of the joint coordinates in base and plate frames, to act contrary to the serial manipulator, but forward kinematic is difficult because of the nonlinearity and complexity of the Stewart platform dynamic equation with the multi-solutions. Hence we, first in this study, introduce the linear estimator using the Luenberger's observer, and the estimator using the nonlinear measured model for the forward kinematic solutions. But it is difficult to find the parameter of the design for the estimation gain or to select the estimation gain and the constant steady state error exists. So this study suggests the estimator with the estimation gain to be learned by the neural network with the structure of multi-perceptron and the learning method using back propagation and shows the estimation performance using the simulation.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2001.06a
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• pp.141-144
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• 2001

In this paper, we proposed a neural network controller with a fuzzy compensator which compensate a output of neural network controller. Even if learn by neural network controller, it can occur a bad results from disturbance or load variations. So in order to adjust above case. we used the fuzzy compensator to get an expected results. And the weight of main neural network can be changed with the result of learning an inverse model neural network of plant, so a expected dynamic characteristics of plant can be got. In order to confirm a performance of the proposed controller, we implemented the controller using the DSP processor and applied in a hydraulic servo system. And then we observed an experimental results.

• Journal of the Institute of Convergence Signal Processing
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• v.13 no.4
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• pp.233-237
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• 2012

In this paper, We propose a control system which compensate a output of a main Neual Network using a RTDNN(Recurrent Time Delayed Neural Network) with a FLC(Fuzzy Logic Controller)After a learn of main neural network, it can occur a Over shoot or Under shoot from a disturbance or a load variations. In order to adjust above case, we used the fuzzy compensator to get an expected results. And the weight of main neural network can be changed with the result of learning a inverse model neural network of plant, so a expected dynamic characteristics of plant can be got. We can confirm good response characteristics of proposed neural network controller by the results of simulation.