• 전기학회논문지
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• 제61권7호
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• pp.1019-1023
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• 2012

This paper considers a model predictive control problem of discrete-time constrained systems with time-varying delay. For this problem, a delay dependent state feedback control approach is used to achieve asymptotic stabilization of systems with input constraints. Based on Lyapunov stability theory, a new stability condition is obtained via linear matrix inequality formulation to find cost monotonicity condition of the model predictive control algorithm which guarantee the closed loop stability. Finally, the proposed method is applied to a numerical example in order to show the effectiveness of our results.

• Journal of Power Electronics
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• 제17권4호
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• pp.983-990
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• 2017

This paper proposes a model predictive control based on the discrete Lyapunov function to improve the performance of power electronic converters. The proposed control technique, based on the finite control set model predictive control (FCS-MPC), defines a cost function for the control law which is determined under the Lyapunov stability theorem with a control error compensation. The steady state and dynamic performance of the proposed control strategy has been tested under a single phase AC/DC voltage source rectifier (S-VSR). Experimental results demonstrate that the proposed control strategy not only offers global stability and good robustness but also leads to a high quality sinusoidal current with a reasonably low total harmonic distortion (THD) and a fast dynamic response under linear loads.

• 대한임베디드공학회논문지
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• 제17권2호
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• pp.115-121
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• 2022

This paper proposes a distributed model predictive formation control algorithm for a group of unmanned ground vehicles (UGVs) with guaranteeing collision avoidance between UGVs. Generally, the model predictive control based formation control has a disadvantage in that it takes a long time to compute control inputs when considering collision avoidance between UGVs. In this paper, in order to overcome this problem, the formation control algorithm is implemented in a distributed manner so that it could be individually controlled. Also, a collision-avoidance method considering real-time is proposed. The proposed formation control algorithm is implemented based on robot operating system (ROS), open source-based middleware. Through the various simulation tests, it is confirmed that the formation control of five UGVs is successfully performed while avoiding collisions between UGVs.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1999년도 제14차 학술회의논문집
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• pp.45-48
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• 1999

Model predictive control algorithm requires a relevant model of the system to be controlled. Unfortunately, the first principle model describing a polymerization reaction system has a large number of parameters to be estimated. Thus there is a need for the identification and control of a polymerization reactor system by using available input-output data. In this work, the polynomial auto-regressive moving average (ARMA) models are employed as the input-output model and combined into the nonlinear model predictive control algorithm based on the successive linearization method. Simulations are conducted to identify the continuous styrene polymerization reactor system. The input variables are the jacket inlet temperature and the feed flow rate whereas the output variables are the monomer conversion and the weight-average molecular weight. The polynomial ARMA models obtained by the system identification are used to control the monomer conversion and the weight-average molecular weight in a continuous styrene polymerization reactor It is demonstrated that the nonlinear model predictive controller based on the polynomial ARMA model tracks the step changes in the setpoint satisfactorily. In conclusion, the polynomial ARMA model is proven effective in controlling the continuous styrene polymerization reactor.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.90-95
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• 2003

In this paper, an Adaptive Nonlinear Constrained Model Predictive Control (ANCMPC) is presented for a pH control in a fed-batch bio-reactor. The pH model is represented with Hammerstein Model. The static nonlinear part of Hammerstein model is described with the static pH model, and the dynamic linear part of the Hammerstein model is described with the CARIMA model. The parameters of the CARIMA model is estimated on-line with the input and output measurements of the system using a recursive least squares type of identi�cation algorithm. The e�ectiveness of the proposed controller is shown through simulations.

• 제어로봇시스템학회논문지
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• 제21권7호
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• pp.662-668
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• 2015

This paper presents an improved predictive control of an induction machine fed by a matrix converter using N-switching vectors as the control action during a complete sampling period of the controller. The conventional model predictive control scheme based matrix converter uses a single switching vector over the same period which introduces high torque ripple. The proposed switching scheme for a matrix converter based model predictive control of an induction machine drive selects the appropriate switching vectors for control of electromagnetic torque with small variations of the stator flux. The proposed method can reduce the ripple of the electrical variables by selecting the switching state as well as the method used in the space vector modulation techniques. Simulation results are presented to verify the effectiveness of the improved predictive control strategy for induction machine fed by a matrix converter.

• 펄프종이기술
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• 제35권4호
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• pp.48-56
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• 2003

In this work an integrated model for paper plants combining wet-end and dry section is developed and a model predictive control scheme based on the plant model is proposed. Closed-loop process identification method is employed to produce a state-space model. Thick stock, filler flow, machine speed and steam pressure are selected as input variables and basis weight, ash content and moisture content are considered as output variables. The desired output trajectory is constructed in the form of 1st-order dynamics. Results of simulations for control of grade change operations are compared with plant operation data collected during the grade change operations under the same conditions as in simulations. From the comparison, we can see that the proposed model predictive control scheme reduces the grade change time and achieves stable steady-state.

• 디지털융복합연구
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• 제10권9호
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• pp.397-407
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• 2012

본 연구에서는 대전지역 일개 건강검진센터의 디지털화된 국민건강보험 건강검진 수검 자료를 이용하여 국민건강보험 검진환자를 효율적으로 관리할 수 있는 방안을 제시하고자 하였다. 이를 위해 대전지역 일개 건강검진센터의 2002년부터 2011년까지 10년간의 국민건강보험 건강검진 수검 자료를 수집하였으며, 수집된 자료를 이용하여 국민건강보험 검진환자의 재검진 예측모형, 민간검진 예측모형, 위암검진 예측모형을 개발하였다. 예측모형 개발 결과 연령, 거주지, 단체 또는 개인 검진 여부, 이전 국민건강보험 건강검진 횟수 등이 국민건강보험 재검진, 민간검진, 위암검진에 유의한 영향을 미치는 것으로 나타났다. 개발된 예측모형을 토대로 재검진, 민간검진, 위암검진 확률 로직을 산출하여 대전지역 일개 건강검진센터의 시스템에 적용할 수 있는 방안을 마련하였다. 국민건강보험 건강검진 환자관리 시스템을 토대로 맞춤형 서비스를 제공한다면 건강검진센터 운영의 효율화에 크게 이바지 할 수 있을 것이다.

• Journal of Electrical Engineering and Technology
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• 제11권2호
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• pp.405-415
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• 2016

This paper presents a model predictive control for shunt active power filters in synchronous reference frame using space vector pulse-width modulation (SVPWM). The three phase load currents are transformed into synchronous rotating reference frame in order to reduce the order of the control system. The proposed current controller calculates reference current command for harmonic current components in synchronous frame. The fundamental load current components are transformed into dc components revealing only the harmonics. The predictive current controller will add robustness and fast compensation to generate commands to the SVPWM which minimizes switching frequency while maintaining fast harmonic compensation. By using the model predictive control, the optimal switching state to be applied to the next sampling time is selected. The filter current contains only the harmonic components, which are the reference compensating currents. In this method the supply current will be equal to the fundamental component of load current and a part of the current at fundamental frequency for losses of the inverter. Mathematical analysis and the feasibility of the suggested approach are verified through simulation results under steady state and transient conditions for non-linear load. The effectiveness of the proposed controller is confirmed through experimental validation.

• 제어로봇시스템학회논문지
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• 제12권7호
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• pp.706-712
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• 2006

Recursive Partial Least Squares(RPLS) method has been used for processing the on-line available multivariate chemical process data and modeling adaptive prediction model for process changes. However, RPLS method is unstable in PLS model updating because RPLS method updates PLS model by merging past PLS model and new data. In this study, Adaptive Predictive Partial Least Squres(APPLS) method is suggested for more sensitive adaptation to process changes. By expanding APPLS method, block-wise Adaptive Predictive Partial Least Squares(block-wise APPLS) method is suggested for a lager scale data of chemical processes. APPLS method has been applied to predict the reactor properties and the product quality of a direct esterification reactor for polyethylene terephthalate(PTT), and block-wise APPLS method has been applied to predict the cetane number using NIR Diesel Spectra data. APPLS and block-wise APPLS methods show better prediction and updating performance than RPLS method.