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볼테라 시리즈 입력을 이용한 냉연 산세 라인 산농도 모델 추정
박찬은(Chan Eun Park),송주만(Ju-man Song),박태수(Tae Su Park),노일환(Il-Hwan Noh),박형국(Hyoung-Kuk Park),최승갑(Seung Gab Choi),박부견(PooGyeon Park) 제어로봇시스템학회 2015 제어·로봇·시스템학회 논문지 Vol.21 No.12
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.
박부견(PooGyeon Park),이원일(Won Il Lee),이석영(Seok Young Lee) 제어로봇시스템학회 2014 제어·로봇·시스템학회 논문지 Vol.20 No.3
This article surveys the control theoretic study on time delay systems. Since time delay systems are infinite dimensional, there are not analytic but numerical solutions on almost analysis and synthesis problems, which implies that there are a tremendous number of approximated solutions. To show how to find such solutions, several results are summarized in terms of two different axes: 1) theoretic tools like integral inequality associated with the derivative of delay terms, Jensen inequality, lower bound lemma for reciprocal convexity, and Wirtinger-based inequality and 2) various candidates for Laypunov-Krasovskii functionals.
In Seok Park(박인석),Nam Kyu Kwon(권남규),PooGyeon Park(박부견) 대한전기학회 2017 정보 및 제어 심포지엄 논문집 Vol.2017 No.4
This paper considers the problem of H∞ control for Markovian jump systems with partly unknown transition probabilities and input saturation. Using the convex property of normalized mode transition probabilities, less conservative H∞ stochastic stabilization conditions for discrete-time Markovian jump systems with partly unknown transition probabilities and input saturation are derived. Then, the derived conditions are represented as linear matrix inequalities (LMIs) conditions. The numerical examples will show that the proposed theorem exhibited better performance in view of the minimum cost √.