• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2495-2497
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• 2000

Level control in the steam generator of a nuclear power plant is important process. But, the low power operation of nuclear power plant causes nonlinear characteristics and non minimum phase characteristics (swell and shrink), change of delay. So, we can't lead good results with conventional PID controller. Particularly, the design of controller with constraints is necessary. This paper introduces MPC(Model Predictive Control) with constraints and designs a good performance MPC controller in spite of the input constraints and nonlinear characteristics, non-minimum phase characteristics

• Journal of Electrical Engineering and Technology
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• v.12 no.5
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• pp.2043-2050
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• 2017

This paper presents two MPC (Model Predictive Control) based charging and discharging algorithms of BESS (Battery Energy Storage System) for capacity firming of wind generation. To deal with the intermittency of the output of wind generation, a single BESS is employed. The proposed algorithms not only make the output of combined systems of wind generation and BESS track the predefined reference, but also keep the SoC (State of Charge) of BESS within its physical limitation. Since the proposed algorithms are both presented in simple if-then statements which are the optimal solutions of related optimization problems, they are both easy to implement in a real-time system. Finally, simulations of the two strategies are done using a realistic wind farm library and a BESS model. The results on both simulations show that the proposed algorithms effectively achieve capacity firming while fulfilling all physical constraints.

• Industrial Engineering and Management Systems
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• v.1 no.1
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• pp.1-9
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• 2002

Among the state-space description of discrete vent systems, the max-plus algebra is known as one of the effective approach. This paper proposes a model predictive control (MPC) design method based on the max-plus algebra. Several studies related to these topics have been done so far under the constraints that system parameters are constant. However, in practical systems such as production systems, it is common and sometimes inevitable that system parameters vary by each event. Therefore, it is of worth to design a new MPC controller taking account of adjustable system parameters. In this paper, we formulate system parameters as adjustable ones, and they are solved by a linear programing method. Since MPC determines optimal control input considering future reference signals, the controller can be more robust and the operation cost can be reduced. Finally, the proposed method is applied to a production system with three machines, and the effectiveness of the proposed method is verified through a numerical simulation.

• Journal of Power Electronics
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• v.17 no.3
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• pp.695-703
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• 2017

In order to satisfy the urgent requirements for the overheating protection of induction motors, an approach that can be used to identify motor temperature has been proposed based on the rotor slots harmonic (RSH) in this paper. One method to accomplish this is to improve the calculation efficiency of the RSH by predicting the stator winding distribution harmonic order by analyzing the harmonics spectrum. Another approach is to increase the identification accuracy of the RSH by suppressing the influence of voltage flashes or current surges during temperature estimation based on model predictive control (MPC). First, an analytical expression of the stator inductance is extracted from a steady-state positive sequence motor equivalent circuit model developed from the rotor flux field orientation. Then a procedure that applies MPC for reducing the identification error of the rotor temperature caused by voltage sag or swell of the power system is given. Due to this work, the efficiency and accuracy of the RSH have been significantly improved and validated our experiments. This work can serves as a reference for the on-line temperature monitoring and overheating protection of an induction motor.

• Journal of Drive and Control
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• v.14 no.2
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• pp.9-15
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• 2017

The steering systems of all-terrain cranes have been developed with various control strategies for the stability and drivability. To optimally control the input steering angle, an accurate mathematical model that represents the actual crane dynamics is required. The derivation of an accurate mathematical model to optimally control the steering angle, however, is difficult since the steering-control strategy generally varies with the magnitude of the crane's longitudinal velocity, and the postures of the crane's working parts vary while it is being driven. To address this problem, this paper proposes an automatic steering-control algorithm that is based on the MPC (model predictive control) with a disturbance observer for all-terrain cranes. The designed disturbance observer of this study was used to estimate the error between the base steering model and the actual crane. A model predictive controller was used for the computation of the optimal steering angle, along with the use of the base steering model with an estimated uncertainty. Performance evaluations of the designed control algorithms were conducted based on a curved-path scenario in the Matlab/Simulink environment. The performance-evaluation results show a sound reference-path-tracking performance despite the large uncertainties.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.7
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• pp.846-853
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• 2018

In this paper, an improved model predictive control (MPC) method is proposed, which reduces the amount of calculations caused by the increased number of candidate voltage vectors with the increased voltage level in multi-level inverters. When the conventional MPC method is used for multi-level inverters, all candidate voltage vectors are considered to predict the next-step current value. However, in the case that the sampling time is short, increased voltage level makes it difficult to consider the all candidate voltage vectors. In this paper, the improved MPC method which can get a fast transient response is proposed with a small amount of the computation by adding new candidate voltage vectors that are set to find the optimal vector. As a result, the proposed method shows faster transient response than the method that considers the adjacent vectors and reduces the computational burden compared to the method that considers the whole voltage vector. the performance of the proposed method is verified through simulations and experiments.

• The Transactions of the Korean Institute of Power Electronics
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• v.20 no.3
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• pp.273-279
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• 2015

A zero vector selection method to reduce switching losses for model predictive control (MPC) of voltage source inverter is proposed. A conventional MPC of voltage source inverter has not been proposed, and a method to select the redundancy of the zero vector is required for this study. In this paper, the redundancy of the zero vectors is selected with generating a zero sequence voltage to reduce switching losses. The zero vector of 2-level inverter is determined by determining sign of the zero sequence voltage. In the proposed method, the quality of the current is retained and switching loss can be reduced compared with the conventional method. This result was verified by P-sim simulation and experiments.

• Journal of Power Electronics
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• v.17 no.1
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• pp.136-148
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• 2017

In recent years, voltage source multilevel converters are very popular in medium/high-voltage industrial applications, among which the NPC/H-Bridge converter is a popular solution to the medium/high-voltage drive systems. The conventional finite control set model predictive control (FCS-MPC) strategy is not practical for multilevel converters due to their substantial calculation requirements, especially under high number of voltage levels. To solve this problem, a hierarchical model predictive voltage control (HMPVC) strategy with referring to the implementation of g-h coordinate space vector modulation (SVM) is proposed. By the hierarchical structure of different cost functions, load currents can be controlled well and common mode voltage can be maintained at low values. The proposed strategy could be easily expanded to the systems with high number of voltage levels while the amount of required calculation is significantly reduced and the advantages of the conventional FCS-MPC strategy are reserved. In addition, a HMPVC-based field oriented control scheme is applied to a drive system with the NPC/H-Bridge converter. Both steady-state and transient performances are evaluated by simulations and experiments with a down-scaled NPC/H-Bridge converter prototype under various conditions, which validate the proposed HMPVC strategy.

• Transactions on Control, Automation and Systems Engineering
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• v.4 no.4
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• pp.289-295
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• 2002

In this paper, a linear matrix inequality(LMI)-based robust control method, which combines model predictive control(MPC) with the feedback linearization(FL), is presented for constrained nonlinear systems with parameter uncertainty. The design procedures consist of the following 3 steps: Polytopic description of nonlinear system with a parameter uncertainty via FL, Mapping of actual input constraint by FL into constraint on new input of linearized system, Optimization of the constrained MPC problem based on LMI. To verify the performance and usefulness of the control method proposed in this paper, some simulations with application to a flexible single link manipulator are performed.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2612-2616
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• 2003

Method for performance monitoring and diagnosis of a MIMO control system has been studied aiming at application to model predictive control (MPC) for industrial processes. The performance monitoring part is designed on the basis of the traditional SPC/SQC method. To meet the underlying premise of Schwart chart observation that the observed variable should be univariate and independent, the process variables are decorrelated temporally as well as spatially before monitoring. The diagnosis part was designed to identify the root of performance degradation among the controller, process, and disturbance. For this, a method to estimate the model-error and disturbance signal has been devised. The proposed methods were evaluated through numerical examples.