• Journal of Institute of Control, Robotics and Systems
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• v.3 no.2
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• pp.115-123
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• 1997

이산기간 H/sub .inf./ 제어에 의한 최적 예견제어기를 제안한다. 기존의 H/sub .inf./ 제어기는 미지의 외란만 고려한 것이고, LQ 에 의한 예견제어기는 예견 가능한 외란과 미지의 외란이 동시에 가해지는 동적 시스템의 전달함수 행렬의 infinity 놈의 최소화하는 피드백제어기가 동시에 설계된다. 제어기의 설계는 full-information H/sub .inf./ 제어 이론을 따르나, 그 유도 과정은 LQ 에 기초한 예견제어기와 유사하게 이루어진다. 설계된 H/sub .inf./ 예견 게인 행렬은 LQ 예견 게인 행렬과 유사한 구조를 갖는다. 전달함수 행렬의 infinity 놈이 .inf.로 갈수록 H/sub .inf./ 예견 게인 행렬은 LQ에 의한 것에 접근한다. LQ 예견 게인 행렬은 H/sub .inf./ 예견 게인 행렬의 부분 집합임이 입증한다.

• 제어로봇시스템학회:학술대회논문집
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• 1999.10a
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• pp.88-91
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• 1999

The purpose of this paper is to propose an approach to design a robust servo controller based on the Mixed H$_2$/H$\sub$$\infty$/ theory. In order to do this, we first modify the generalized plant for the usual H$\sub$$\infty$/ servo problem to a structure of the Mixed H$_2$/H$\sub$$\infty$/ minimization problem by virtue of the internal model principle. By doing this, we can divide specifications adopted for robust servo system design into H$_2$and H$\sub$$\infty$/ performance criteria, respectively. Then, the mixed H$_2$/H$\sub$$\infty$/ problem is solved in order to find the best solution, by which we can minimize H$_2$-norm of the transfer function under the condition of H$\sub$$\infty$/-norm value, through Linear Matrix Equality (LMI).

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.39 no.2
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• pp.95-103
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• 2002

This paper deals with the H$_{\infty}$ control problems for discrete-time linear systems with time-varying delays in states. The existence condition and the design method of the H$_{\infty}$ state feedback controller are given. In this paper, the H$_{\infty}$ control law is assumed to be a memoryless state feedback, and the upper-bound of time-varying delay and S-procedure are used. Through some changes of variables and Schur complement, the obtained sufficient condition can be rewritten as an LMI(linear matrix inequality) form in terms of all variables.

• Journal of the Korean Society of Industry Convergence
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• v.23 no.4_1
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• pp.559-567
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• 2020

As the renewable energy market grows, grid-connected inverters have been improving and expanding in several fields in recent years because energy conversion devices are the main components of solar systems. In this paper, a high-precision new grid-connected three-phase inverter system is proposed. The proposed system consists of a main inverter, a sub inverter and a transformer. The main inverter operates at a low switching frequency and high power and transmits power to the grid. A sub-inverter connected in series with the transmission line through a matching transformer operates at lower power than the main inverter to provide input values to the transformer. The transformer acts as a power supply according to the voltage compensation value. This study is based on the principle of operation of the UPFC(Unified Power Flow Controller) structure used to regulate power flow in AC transmission lines. The grid-connected inverter system proposed in this paper is implemented with high precision and high resolution. The proposed system was verified through its ability to enhance and ensure the safety of the proposed system through simulation and experiment.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.23 no.8
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• pp.33-40
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• 2009

This paper presents the high performance control of interior permanent magnet synchronous motor(IPMSM) using space vector(SV) PWM method based on hybrid artificial intelligent(HAI) controller. The HAI controller combines the advantages between adaptive fuzzy control and neural network The SV PWM method is applied to a speed control system of motor in the industry field until now and is feasible to improve harmonic rate of output current, switching frequency and response characteristics. This HAI controller is used instead of conventional PI controller in order to solve problems happening when calculating a reference voltage. The HAI controller improves speed performance by hybrid combination of reference model-based adaptive mechanism method, fuzzy control and neural network. This paper analyzes response characteristics of parameter variation, steady-state and transient-state using proposed HAI controller and this controller compares with conventional fuzzy neural network(FNN) and PI controller. Also, this paper proves validity of HAI 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 the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.20 no.7
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• pp.65-73
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• 2006

This paper presents speed control of IPMSM drive using neural network(NN) PI controller. 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, NNPI controller proposes a new method based neural network. NNPI 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 Power Electronics
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• v.18 no.2
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• pp.558-572
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• 2018

In addition to its modular nature, a Hybrid Modular Multilevel Converter (HMMC) assembled from half-bridge and full-bridge sub-modules, is able to block DC faults with a minimum number of switching devices, which makes it attractive for high power applications. This paper introduces a control strategy based on the Root-Least Square (RLS) algorithm to estimate the capacitor voltages instead of using direct measurements. This action eliminates the need for voltage transducers in the HMMC sub-modules and the associated communication link with the central controller. In addition to capacitor voltage balancing and suppression of circulating currents, a fault tolerant control unit (FTCU) is integrated into the proposed strategy to modify the parameters of the HMMC controller. On advantage of the proposed FTCU is that it does not need extra components. Furthermore, a fault detection unit is adapted by utilizing a hybrid estimation scheme to detect sub-module faults. The behavior of the suggested technique is assessed using PSCAD offline simulations. In addition, it is validated using a real-time digital simulator connected to a real time controller under various normal and fault conditions. The proposed strategy shows robust performance in terms of accuracy and time response since it succeeds in stabilizing the HMMC under faults.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.23 no.8
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• pp.57-66
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• 2009

The conventional fixed gain PI controller is very sensitive to step change of command speed, parameter variation and load disturbances. The precise speed control of interior permanent magnet synchronous motor(IPMSM) drive becomes a complex issue due to nonlinear coupling among its winding currents and the rotor speed as well as the nonlinear electromagnetic developed torque. Therefore, there exists a need to tune the PI controller parameters on-line to ensure optimum drive performance over a wide range of operating conditions. This paper proposes hybrid intelligent-PI(HIPI) controller of IPMSM drive using adaptive learning mechanism(ALM) and fuzzy neural network(FNN). The proposed controller is developed to ensure accurate speed control of IPMSM drive under system disturbances and estimation of speed using artificial neural network(ANN) controller. The PI controller parameters are optimized by ALM-FNN at all possible operating condition in a closed loop vector control scheme, The validity of the proposed controller is verified by results at different dynamic operating conditions.

• The Transactions of the Korean Institute of Power Electronics
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• v.11 no.3
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• pp.224-232
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• 2006

The paper proposes the artificial neural network(ANN) sensorless control of induction motor drive with adaptive fuzzy logic controller(AFLC). Also, this paper proposes the speed control of induction motor using AFC and estimation of speed using ANN controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The error between the desired state variable and the actual one is back-propagated to adjust the rotor speed, so that the actual state variable will coincide with the desired one. The proposed control algorithm is applied to induction motor drive system controlled AFLC and him controller. And this paper is proposed the results to verify the effectiveness of the AFLC and ANN controller.