• Proceedings of the KIEE Conference
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• 2006.07d
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• pp.1868-1870
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• 2006

Neural network is used in many fields of control systems, but input-output patterns of a control system are not easy to be obtained and by using as single feedback neural network controller. And also it is difficult to get a satisfied performance when the changes of rapid load and disturbance are applied. To resolve those problems, this paper proposes a new algorithm which is the neural network controller. The new algorithm uses the neural network instead of activation function to control object at the output node. Therefore, control object is composed of neural network controller unifying activation function, and it supplies the error back propagation path to calculate the error at the output node. As a result, the input-output pattern problem of the controller which is resigned by the simple structure of neural network is solved, and real-time learning can be possible in general back propagation algorithm. Application of the new algorithm of neural network controller gives excellent performance for initial and tracking response and it shows the robust performance for rapid load change and disturbance. The proposed control algorithm is implemented on a high speed DSP, TMS320C32, for the speed of 3-phase induction motor. Enhanced performance is shown in the test of the speed control.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.7
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• pp.1298-1301
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• 2007

The problem of designing dynamic output feedback sliding mode controllers for uncertain multivariable linear systems is considered. Using linear matrix inequalities(LMIs), a feasibility condition for the design problem is derived. Explicit fomulas of the gain matrices of a full order output feedback sliding mode controller in terms of the solution matrices of the LMI condition is given. A simple LMI-based algorithm for designing output feedback sliding mode controllers is also given. Finally, numerical design examples are given to show the effectiveness of the proposed method.

• The Transactions of the Korean Institute of Power Electronics
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• v.27 no.6
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• pp.498-506
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• 2022

This study proposes a design methodology for bidirectional, series-resonant, dual-active bridge (SRDAB) converters. The circuit parameters of the SRDAB converters are designed by considering the output power and efficiency of the converter. The proposed method can be used to design a high-power, high-efficiency SRDAB converter. A voltage controller is employed to manipulate the output voltage of the converter, and the controller gains are selected using the transfer function and frequency response of the controller. Simulation results show that the output power of the designed SRDAB converter is 2 kW per converter module as designed. In addition, the performance of the voltage controller is evaluated using the simulation and experimental results. The output voltage follows the reference voltage within 10 ms under the step change of the reference command. The output voltage also follows the reference voltage under the step load change. The efficiency of the designed SRDAB converter is 95.6%.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.4
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• pp.154-157
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• 2006

This paper is proposed hybrid artificial intelligent(HAI) controller for high performance of induction motor drive. The design..of this algorithm based on fuzzy-neural network(FNN) controller that is implemented using fuzzy control and neural network. This controller uses fuzzy rule as training patterns of a neural network. Also, this controller uses the back-propagation method to adjust the weights between the neurons of neural network in order to minimize the error between the command output and actual output. A model reference adaptive scheme is proposed in which the adaptation mechanism is executed by fuzzy logic based on the error and change of error measured between the motor speed and output of a reference model. The control performance of the adaptive FNN controller is evaluated by analysis for various operating conditions. The results of experiment prove that the proposed control system has strong high performance and robustness to parameter variation, and steady-state accuracy and transient response.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.41 no.3
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• pp.39-46
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• 2004

This paper is proposed adaptive fuzzy-neural network(FNN) controller for the speed control of interior permanent magnet synchronous motor(IPMSM) drive. The design of this algorithm based on FNN controller that is implemented by using fuzzy control and neural network. This controller uses fuzzy rule as training patterns of a neural network. Also, this controller uses the back-propagation method to adjust the weights among the neurons of neural network in order to minimize the error between the command output and actual output. A model reference adaptive scheme is proposed in which the adaptation mechanism is executed by fuzzy logic based on the error and change of error measured between the motor speed and output of a reference model. The control performance of the adaptive FNN controller is evaluated by analysis for various operating conditions. The results of analysis prove that the proposed control system has strongly high performance and robustness in parameter variation, steady-state accuracy and transient response.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.19 no.1
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• pp.109-116
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• 2005

Wind power and photovoltaic(PV) systems are getting into the spotlight as substitute energy. But problem is happened stability by speed change of wind and the power output of the sun's ray. The power output amount according to velocity of wind power system. System breakdown can happen at change of sudden velocity, typhoon and night. This paper shows a automatic tail safety brake controller based on feedback control using wind velocity. The operation of automatic tail safety controller verified by manual test. PV system is a energy change system by temperature of sun's ray and cell. Maximum power point tracking(MPPT) is used in PV systems to maximize the photovoltaic array output power. In existed PV system is low output and changeable DC voltage for boost and filtering the buck-boost converter use. But, this paper established deformed DC-DC converter. Changed Buck-boost converter's unlined output current to line output current using DC-DC converter. This is effect that reduce ripple of output current. Proved through an output waveform comparison experiment. Finally, tail safety brake controller is established to wind turbine system for stability elevation and DC-DC converter is established on PV system for stability output.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.06a
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• pp.732-735
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• 2007

Recently the control of induction motor for position control has been extensively studied. The representative method is PIDA controller proposed by Jung&Dorf. By designed PIDA controller' parameter had large value. Moreover, this method is very analyze, so that, not adapted controller parameter in disturbance. Besides using generalize fuzzy controller. Because input and output membership function is linguistic type, therefore system response is very slow. So, in this paper we used optimized fuzzy controller. Optimized fuzzy controller is output membership function is unity value. The controller performance was estimated applied to induction motor' position control.

• Proceedings of the KIPE Conference
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• 2003.07b
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• pp.965-968
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• 2003

This paper describes the PWM-VSI controller of three-phase UPS system using stationary reference frame. This controller meets the specification the UPS inverter output voltage even under the unbalanced or nonlinear load. This controller is also constructed with duble control loop of the outer voltage control loop and the inner current control loop. For the fast response of the output voltage control, yhr inner current control loop of the capacitor current os used. To get the good property against overshoot, the If controller us used. The outer voltage controller is designed with P controller and the high gain transfer function is used for the zero steady state error. All control gains of both controller is designed base on the CDM method.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2002.03a
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• pp.219-227
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• 2002

In this paper, we designed neural network predictive PID controller to control sway happened in transfer of trolley for automatic travel control system. We include dynamic character of nonlinear system, and mathematical expression veny simple used neural network. When various establishment location and surrounding disturbance were approved based on mathematical modelling of crane, controller designed to become effective control location error and vibration angle of two control variables that simultaneously can predictive control. Neural network predictive PID controller produced parameter of PID controller using neural network self-tuner. Neural network self-tuner's input used crane's output and neural network predictive output. Neural network self-tuner using error back propagation algorithm. We analyzed control performance comparison through computer simulation when applied disturbance about sway of location and angle in transfer of crane. The results show that the proposed neural network predictive PID controller has better performances than general PID controller, neural network PID controller.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2005.05a
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• pp.124-127
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• 2005

As computer capability and test skill become more and more advanced, finite element method and modal test are being widely applied in engineering design. In order to correlate and reconcile the inevitable discrepancies between the analytical and experimental models, many techniques have been developed. Among these methods, multiple-system methods are known as the effective tools in that they can supply the rich modal data available which are experimentally obtained. These abundant modal data can help structural system parameters estimated well. Multiple-system methods can be classified into the structural modification methods and feedback controller methods. The structural modification methods need the physical attachment of structures and their concept may limit the application of them. To overcome this drawback, the feedback controller methods are addressed which enable us to get more modal data without the structural change. Mode decoupling controller(MDC), one of them, is to use acceleration out)ut feedback to perturb an open-loop system. The output feedback controller generally cannot guarantee the stability of a closed-loop system. However, MDC can solve this problem under the certain constraints. So far, MDC utilizes accelerations as the sensor signals. In this research, strain sensors are going to be picked up to apply to the MDC. Strain output is recently used for structural system identification due to the drastically improved and miniaturized strain sensor. In this paper, we show that the MDC using strain output has differences compared with acceleration output in estimating the structural system parameters. The associated simulation is performed to demonstrate the above mentioned characteristics.