• The Transactions of the Korean Institute of Power Electronics
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• v.15 no.2
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• pp.159-166
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• 2010

For a grid-connected inverter in distributed generation systems, the current control is essential, and recently, the predictive current control based on a high performance digital signal processors (DSP) to satisfy a fast dynamic response has been widely investigated. However, the performance of predictive current control is degraded by the time delay due to digital implementation, the parameter and measured value errors and the interference of noise, and also theses make system even unstable. Therefore, this paper proposes the predictive current control using grid voltage observer for grid-connected inverter applications. To determine the relevant voltage observer gain, the low-order harmonics of grid voltage are considered, and the effect of filter parameter errors is analyzed. The proposed method has a fast current response capability, the robustness to noise and simple implementation due to voltage sensorless control and the robust current control performance to low-order grid harmonics. The feasibility of the proposed method is verified by simulation and experimental results.

• Journal of Advanced Navigation Technology
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• v.23 no.5
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• pp.424-429
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• 2019

The evolution of networking technology such as commercialization of 5G systems provides foundation for information exchange and control of systems over the network. In addition, importance of controlling a system with delay is increasing significantly, since various phenomena in the network are associated with delay. In this paper, with a predictive control which has been studied for designing a controller with low complexity, we propose a novel predictive control for a system with multi-inputs such that it can keeps the complexity almost the same regardless of the number of inputs and degree of delay. The asymptotic stability of the proposed control with a static output feedback is also proved. The numerical simulation shows that the proposed method is superior in complexity and the performance of finding feasible controllers to the existing predictive control and a conventional method based on augmented states.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.4
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• pp.227-233
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• 2021

Predictive maintenance has been one of important applications of data science technology that creates a predictive model by collecting numerous data related to management targeted equipment. It does not predict equipment failure with just one or two signs, but quantifies and models numerous symptoms and historical data of actual failure. Statistical methods were used a lot in the past as this predictive maintenance method, but recently, many machine learning-based methods have been proposed. Such proposed machine learning-based methods are preferable in that they show more accurate prediction performance. However, with the exception of some learning models such as decision tree-based models, it is very difficult to explicitly know the structure of learning models (Black-Box Model) and to explain to what extent certain attributes (features or variables) of the learning model affected the prediction results. To overcome this problem, a recently proposed study is an explainable artificial intelligence (AI). It is a methodology that makes it easy for users to understand and trust the results of machine learning-based learning models. In this paper, we propose an explainable AI method to further enhance the explanatory power of the existing learning model by targeting the previously proposedpredictive model [5] that learned data from a core facility (Hyper Compressor) of a domestic chemical plant that produces polyethylene. The ensemble prediction model, which is a black box model, wasconverted to a white box model using the Explainable AI. The proposed methodology explains the direction of control for the major features in the failure prediction results through the Explainable AI. Through this methodology, it is possible to flexibly replace the timing of maintenance of the machine and supply and demand of parts, and to improve the efficiency of the facility operation through proper pre-control.

• Structural Engineering and Mechanics
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• v.47 no.5
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• pp.679-700
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• 2013

Two new predictive design methods are presented in this study. The first is a hybrid method, called neuro-fuzzy, based on neural networks with fuzzy learning. A total of 280 experimental datasets obtained from the literature concerning concentric punching shear tests of reinforced concrete slab-column connections without shear reinforcement were used to test the model (194 for experimentation and 86 for validation) and were endorsed by statistical validation criteria. The punching shear strength predicted by the neuro-fuzzy model was compared with those predicted by current models of punching shear, widely used in the design practice, such as ACI 318-08, SIA262 and CBA93. The neuro-fuzzy model showed high predictive accuracy of resistance to punching according to all of the relevant codes. A second, more user-friendly design method is presented based on a predictive linear regression model that supports all the geometric and material parameters involved in predicting punching shear. Despite its simplicity, this formulation showed accuracy equivalent to that of the neuro-fuzzy model.

• Journal of Power Electronics
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• v.18 no.2
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• pp.445-455
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• 2018

A novel algorithm is proposed to regulate the neutral point potential in neutral point clamped three-level inverters. Oscillations of the neutral point potential and an unbalanced dc-link voltage cause distortions of the output voltage. Large capacitors, which make the application costly and bulky, are needed to eliminate oscillations. Thus, the algorithm proposed in this paper utilizes the finite-control-set model predictive control and the multistage medium vector to solve these issues. The proposed strategy consists of a two-step prediction and a cost function to evaluate the selected multistage medium vector. Unlike the virtual vector method, the multistage medium vector is a mixture of the virtual vector and the original vector. In addition, its amplitude is variable. The neutral point current generated by it can be used to adjust the neutral point potential. When compared with the virtual vector method, the multistage medium vector contributes to decreasing the regulation time when the modulation index is high. The vectors are rearranged to cope with the variable switching frequency of the model predictive control. Simulation and experimental results verify the validity of the proposed strategy.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.38 no.2
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• pp.20-30
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• 2001

In the paper, we propose a predictive control scheme using multiple neural network-based prediction models. To construct the multiple models, we select several specific values of a parameter whose variation affects serious control performance in the plant. Among the multiple prediction models, we choose one that shows the best predictions for future outputs of the plant by a switching technique. Based on a nonlinear programming method, we calculate the current process input in the nonlinear predictive control system with multiple prediction models. The proposed control method is shown to be very effective when a parameter of the plant changes or the time delay, if it exists, varies. It is also shown that the proposed method is successfully applied for the control of suspension in a electro-magnetic levitation system.

• Nuclear Engineering and Technology
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• v.49 no.1
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• pp.134-140
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• 2017

A well-performed core power control to track load changes is crucial in pressurized water reactor (PWR) nuclear power stations. It is challenging to keep the core power stable at the desired value within acceptable error bands for the safety demands of the PWR due to the sensitivity of nuclear reactors. In this paper, a state-space model predictive control (MPC) method was applied to the control of the core power. The model for core power control was based on mathematical models of the reactor core, the MPC model, and quadratic programming (QP). The mathematical models of the reactor core were based on neutron dynamic models, thermal hydraulic models, and reactivity models. The MPC model was presented in state-space model form, and QP was introduced for optimization solution under system constraints. Simulations of the proposed state-space MPC control system in PWR were designed for control performance analysis, and the simulation results manifest the effectiveness and the good performance of the proposed control method for core power control.

• Journal of Power Electronics
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• v.19 no.6
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• pp.1536-1543
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• 2019

This paper presents an improved torque predictive control (TPC) for permanent magnet synchronous motors (PMSMs) using an indirect matrix converter (IMC). The IMC has characteristics such as a high power density and sinusoidal waveforms of the input-output currents. Additionally, this configuration does not have any DC-link capacitors. Due to these advantages of the IMC, it is used in various application field such as electric vehicles and railway cars. Recently, research on various torque control methods for PMSM drives using an IMC is being actively pursued. In this paper, an improved TPC method for PMSM drives using an IMC is proposed. In the improved TPC method, the magnitudes of the voltage vectors applied to control the torque and flux of the PMSM are adjusted depending on the PMSM torque control such as the steady state and transient response. Therefore, it is able to reduce the ripples of the output current and torque in the low-speed and high-speed load ranges. Additionally, the improved TPC can improve the dynamic torque response when compared with the conventional TPC. The effectiveness of the improved TPC method is verified by experimental results.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.1-4
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• 1995

We propose a new design method for a generalized predictive control (GPC) system based on the parametrization of two-degree-of freedom control systems. The objective is to design the GPC system which guarantees the stability of the control system for a perturbed plant. The design procedure of our proposed method consists of three steps. First, we design a basic controller for a nominal plant using the LQG method and parametrize a whole control system. Next, we identify the deviation between the perturbed plant and the nominal one using a closed-loop identification method and design a free parameter of parametrization to stabilize the closed-loop system. Finally, we design a feedforward controller so as to incorporate GPC technique into our controller structure. A numerical example is presented to show the effectiveness of our proposed method.

• Korean Journal of Clinical Laboratory Science
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• v.39 no.3
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• pp.271-276
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• 2007

Helicobacter pylori (H. pylori) infection is common in korea and high incidence at gastric ulcer and duodenal ulcer. $^{14}C-urea$ breath test ($^{14}C-UBT$) is regarded as a highly reliable and non-invasive method for the diagnosis of H. pylori infection. The purpose of this study was to evaluate the diagnositc performance of a new and rapid $^{14}C-UBT$, which was equipped with Geiger-Muller counter and compared the results with those obtained by gastroduodenoscopic biopsies (GBx). One hundred sixty-eight patients (M : F = 118 : 50) underwent $^{14}C-UBT$, rapid urease test (CLO test), and GBx. The results of $^{14}C-UBT$ were classified as positive (>50 cpm), borderline (25$^{14}C-UBT$ or CLO test results with GBx as a glod standard. In the assessment of the presence of H. pylori infection, the $^{14}C-UBT$ global performance yielded positive predictive value, negative predictive value and accuracy of 93.3% and 83.3%, respectively. However, the CLO test had performance yielded positive predictive value, negative predictive value and accuracy of 76.9%, 50.0%, respectively. In this study $^{14}C-UBT$ is a highly accurate, simple and non-invasive method or the diagnosis of follow up H. pylori infection.