• Journal of Power Electronics
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• v.13 no.4
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• pp.679-691
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• 2013

High-power electromagnetic transmitter power supplies are an important part of deep geophysical exploration equipment. This is especially true in complex environments, where the ability to produce a highly accurate and stable output and safety through redundancy have become the key issues in the design of high-power electromagnetic transmitter power supplies. To solve these issues, a high-frequency switching power cascade based emission power supply is designed. By combining the circuit averaged model and the equivalent controlled source method, a modular mathematical model is established with the on-state loss and transformer induction loss being taken into account. A triple-loop control including an inner current loop, an outer voltage loop and a load current forward feedback, and a digitalized voltage/current sharing control method are proposed for the realization of the rapid, stable and highly accurate output of the system. By using a new algorithm referred to as GAPSO, which integrates a genetic algorithm and a particle swarm algorithm, the parameters of the controller are tuned. A multi-module cascade helps to achieve system redundancy. A simulation analysis of the open-loop system proves the accuracy of the established system and provides a better reflection of the characteristics of the power supply. A parameter tuning simulation proves the effectiveness of the GAPSO algorithm. A closed-loop simulation of the system and field geological exploration experiments demonstrate the effectiveness of the control method. This ensures both the system's excellent stability and the output's accuracy. It also ensures the accuracy of the established mathematical model as well as its ability to meet the requirements of practical field deep exploration.

• Proceedings of the KIEE Conference
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• 2005.05a
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• pp.6-8
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• 2005

In this paper, we discuss model identification of nonlinear data using GAs-based Fuzzy Polynomial Neural Networks(GAs-FPNN). Fuzzy Polynomial Neural Networks(FPNN) is proposed model based Group Method Data Handling(GMDH) and Neural Networks(NNs). Each node of FPNN is expressed Fuzzy Polynomial Neuron(FPN). Network structure of nonlinear data is created using Genetic Algorithms(GAs) of optimal search method. Accordingly, GAs-FPNN have more inflexible than the existing models (in)from structure selecting. The proposed model select and identify its for optimal search of Genetic Algorithms that are no. of input variables, input variable numbers and consequence structures. The GAs-FPNN model is select tuning to input variable number, number of input variable and the last part structure through optimal search of Genetic Algorithms. It is shown that nonlinear data model design using Genetic Algorithms based FPNN is more usefulness and effectiveness than the existing models.

• Journal of the Korea institute for structural maintenance and inspection
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• v.21 no.6
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• pp.113-125
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• 2017

In spite of bulk literature about the tuning of TMD, the effectiveness of TMD in reducing the seismic response of engineering structures is still in a row. This paper deals with the optimum tuning parameters of a passive TMD and simulated on MATLAB with a ten-story numerical shear building. A weighted multi-objective optimization method based on computer experiment consisting of coupled with central composite design(CCD) central composite design and response surface methodology(RSM) was applied to find out the optimum tuning parameters of TMD. After the optimization, the so-conceived TMD turns out to be optimal with respect to the specific seismic event, hence allowing for an optimum reduction in seismic response. The method was employed on above structure by assuming first the El Centro seismic input as a sort of benchmark excitation, and then additional recent strong-motion earthquakes. It is found that the RSM based weighted multi-objective optimized damper improves frequency responses and root mean square displacements of the structure without TMD by 31.6% and 82.3% under El Centro earthquake, respectively, and has an equal or higher performance than the conventionally designed dampers with respect to frequency responses and root mean square displacements and when applied to earthquakes.

• Journal of Ocean Engineering and Technology
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• v.25 no.6
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• pp.1-8
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• 2011

This paper describes a new method for a digital gas metal arc welding (GMAW) system. The GMAW system is an arc welding process that incorporates the GMAW power source (PS-GMAW) with a wire feed unit (WFU). The PS-GMAW requires an electric power of constant voltage. A constant magnitude is maintained for the arc current by controlling the wire-feed speed of the WFU. A mathematical model is derived, and a self-tuning fuzzy proportional-integral-derivative (PID) controller is designed and applied to control the welding current. The electrode wire feeding mechanism with this controller is driven by a DC motor, which can compensate for both the molten part of the electrode and undesirable fluctuations in the arc length during the welding process. By accurately maintaining the output welding current and welding voltage at constant values during the welding process, excellent welding results can be obtained. Simulation and experimental results are shown to prove the effectiveness of the proposed controller.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.44 no.2 s.314
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• pp.24-31
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• 2007

This paper presents self tuning PI(STPI) controller of IPMSM drive using neural network. In general, PI controller in computer numerically controlled machine process fixed gain. They may perform well under some operating conditions, but not all. To increase the robustness of fixed gain PI controller, STPI controller proposes a new method based neural network. STPI controller is developed to minimize overshoot, rise time and settling time following sudden parameter changes such as speed, load torque and inertia. Also, this paper is proposed speed control of IPMSM using neural network and estimation of speed using artificial neural network(ANN) controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The results on a speed controller of IPMSM are presented to show the effectiveness of the proposed gain tuner. And this controller is better than the fixed gains one in terms of robustness, even under great variations of operating conditions and load disturbance.

• Journal of Korean Association for Spatial Structures
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• v.7 no.2 s.24
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• pp.53-61
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• 2007

A conventional passive TMD is only effective when it is tuned properly. In many practical applications, inevitable off-tuning of a TMD occurs because the mass in a building floor could change by moving furnishings, people gathering, etc. When TMDs are offtuned, TMDs their effectiveness is sharply reduced. Moreover, the off-tuned TMs can excessively amplify the vibration levels of the primary structures. This paper discusses the application of a new class of MR damper, for the reduction of floor vibrations duo to machine and human movements. The STMD introduced uses a MR damper called to semi-active damper to achieve reduction in the floor vibration. Here, the STMD and the groundhook algorithm are applied to a single degree of freedom system representative or building floors. The performance or the STMD is compared to that or the equivalent passive TMD. In addition, the effects of off-tuning due to variations in the mass of the floor system. Comparison of the results demonstrates the efficiency and robustness of STMD with respect to equivalent TMD.

• JSTS:Journal of Semiconductor Technology and Science
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• v.16 no.1
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• pp.11-22
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• 2016

Clock skew scheduling is one of the essential steps to be carefully performed during the design process. This work addresses the clock skew optimization problem integrated with the consideration of the inter-dependent relation between the setup and hold times, and clock to-Q delay of flip-flops, so that the time margin is more accurately and reliably set aside over that of the previous methods, which have never taken the integrated problem into account. Precisely, based on an accurate flexible model of setup time, hold time, and clock-to-Q delay, we propose a stepwise clock skew scheduling technique in which at each iteration, the worst slack of setup and hold times is systematically and incrementally relaxed to maximally extend the time margin. The effectiveness of the proposed method is shown through experiments with benchmark circuits, demonstrating that our method relaxes the worst slack of circuits, so that the clock period ($T_{clk}$) is shortened by 4.2% on average, namely the clock speed is improved from 369 MHz~2.23 GHz to 385 MHz~2.33 GHz with no time violation. In addition, it reduces the total numbers of setup and hold time violations by 27.7%, 9.5%, and 6.7% when the clock periods are set to 95%, 90%, and 85% of the value of Tclk, respectively.

• Journal of Navigation and Port Research
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• v.34 no.2
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• pp.117-122
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• 2010

Since fuzzy controllers are nonlinear, it is more difficult to set the controller gains and to analyse the stability compared to conventional PID controllers. This paper proposes a fuzzy PD+I controller for tracking control which uses a linear fuzzy inference(product-sum-gravity) method based on a conventional linear PID controller. In this scheme the fuzzy PD+I controller works similar to the control performance as the linear PD plus I(PD+I) controller. Thus it is possible to analyse and design an fuzzy PD+I controller for given systems based on a linear fuzzy PD controller. The scaling factors tuning scheme, another topic of fuzzy controller design procedure, is also introduced in order to fine performance of the fuzzy PD+I controller. The scaling factors are adjusted by a real-coded genetic algorithm(RCGA) in off-line. The simulation results show the effectiveness of the proposed fuzzy PD+I controller for tracking control problems by comparing with the conventional PID controllers.

• Journal of Power Electronics
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• v.14 no.5
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• pp.1008-1027
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• 2014

An electric scooter with a V-belt continuously variable transmission (CVT) driven by a permanent magnet synchronous motor (PMSM) has a lot of nonlinear and time-varying characteristics, and accurate dynamic models are difficult to establish for linear controller designs. A PMSM servo-drive electric scooter controlled by a novel hybrid modified recurrent Legendre neural network (NN) control system is proposed to solve difficulties of linear controllers under the occurrence of nonlinear load disturbances and parameters variations. Firstly, the system structure of a V-belt CVT driven electric scooter using a PMSM servo drive is established. Secondly, the novel hybrid modified recurrent Legendre NN control system, which consists of an inspector control, a modified recurrent Legendre NN control with an adaptation law, and a recouped control with an estimation law, is proposed to improve its performance. Moreover, the on-line parameter tuning method of the modified recurrent Legendre NN is derived according to the Lyapunov stability theorem and the gradient descent method. Furthermore, two optimal learning rates for the modified recurrent Legendre NN are derived to speed up the parameter convergence. Finally, comparative studies are carried out to show the effectiveness of the proposed control scheme through experimental results.

• Proceedings of the KIPE Conference
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• 2019.07a
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• pp.71-73
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• 2019

The output current of the Grid Connected Inverter (GCI) can be polluted with harmonics mainly due to i) dead time in switches, ii) non-linearity of switches, iii) grid harmonics, and iv) DC link fluctuation. Therefore, it is essential to design the robust Harmonic Compensation (HC) technique for the improvement of output current quality and fulfill the IEEE 1547 Total harmonics Distortion (THD) limit i.e. <5%. The conventional harmonic techniques often are complex in implementation due to their i) additional hardware needs, ii) complex structure, iii) difficulty in tuning of parameters, iv) current controller compatibility issues, and v) higher computational burden. In this paper, to eliminate the harmonics from the GCI output current, a novel Digital Lock-In Amplifier (DLA) based harmonic detection is proposed. The advantage of DLA is that it extracts the harmonic information accurately, which is further compensated by means of PI controller in feed forward manner. Moreover, the proposed HC method does not require additional hardware and it works with any current controller reference frame. To show the effectiveness of the proposed HC method a 5kW GCI prototype built in laboratory. The output current THD is achieved less than 5% even with 10% load, which is verified by simulation and experiment.