볼테라 시리즈 입력을 이용한 냉연 산세 라인 산농도 모델 추정

다국어 초록 (Multilingual Abstract)

This paper deals with estimating the acid concentration of pickling process using the Volterra inputs. To estimate the acid concentration, the whole pickling process is represented by the grey box model consists of the white box dealing with known system and the black box dealing with unknown system. Because there is a possibility of nonlinear term in the unknown system, the Volterra series are used to estimate the acid concentration. For the white box modeling, the acid tank solution level and concentration equations are used, and for the black box modeling, the acid concentration is estimated using the Volterra Least Mean Squares (LMS) algorithm and Least Squares (LS) algorithm. The LMS algorithm has the advantage of the simple structure and the low computation, and the LS algorithm has the advantage of lowest error. The simulation results compared to the measured data are included.

목차 (Table of Contents)

• Abstract
• I. 서론
• II. 산세과정의 그레이 박스 모델링
• III. 볼테라 LMS 필터를 이용한 산 소모량 추정
• IV. 볼테라 LS를 이용한 산 소모량 추정
• V. 산 소모량 추정 시뮬레이션
• VI. 결론
• REFERENCES

참고문헌 (Reference)

1 박형국, "산세라인 자동화를 위한 농도 측정 시스템 개발" 제어·로봇·시스템학회 19 (19): 891-895, 2013

2 이재형, "비선형 시스템의 상태변수 추정기법 동향" 제어·로봇·시스템학회 20 (20): 277-288, 2014

3 R. D. Nowak, "Volterra filter identification using penalized least squares" 5 : 2813-2816, 1996

4 R. D. Nowak, "Penalized least squares estimation of Volterra filters and higher order statistics" 46 (46): 419-428, 1998

5 P. Kittisupakorn, "Neural network based model predictive control for a steel pickling process" 19 (19): 579-590, 2009

6 B. Sohlberg, "Hybrid grey box modelling of a pickling process" 13 (13): 1093-1102, 2005

7 C. E. Park, "Cooling and Estimation model for acid concentration in cooling pickling process" 316-317, 2015

8 G. Budura, "Communications Department Faculty of Electronics and Telecommunications Timisoara, Bd. V. Parvan, no. 2" 2005