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NTIS 바로가기제어·로봇·시스템학회 논문지 = Journal of institute of control, robotics and systems, v.20 no.3, 2014년, pp.277 - 288
장홍 (한국과학기술원 생명화학공학과) , 최수항 (한국과학기술원 생명화학공학과) , 이재형 (한국과학기술원 생명화학공학과)
This article reviews various state estimation methods for nonlinear systems, particularly with a perspective of a process control engineer. Nonlinear state estimation methods can be classified into the following two categories: stochastic approaches and deterministic approaches. The current review c...
핵심어 | 질문 | 논문에서 추출한 답변 |
---|---|---|
상태란 무엇인가 | 상태 또는 상태 변수(state)는 시스템(system)의 특성과 동적 (dynamic) 거동을 설명하기 위해 필요한 과거 또는 현재 정보를 말한다. 동적 시스템을 상태 변수로 해석하면 해당 공정을 정확하고 간편하게 관찰(monitoring)할 수 있고, 효과적으로 제어(control)할 수 있다. | |
상태 추정 기법 중 확률론적 접근법의 대표적인 예는 무엇이 있는가 | 상태 추정 기법은 크게 확률론적(stochastic) 접근법과 결정론적(deterministic) 접근 방법으로 나눈다. 대표적인 확률론적 접근법으로 상태 변수에 대한 조건부 확률밀도함수(conditional probability-density-function)를 회귀적(recursive)으로 계산하는 방법인 베이지안(Bayesian) 접근법이 있다. 결정론적 접근법의 대표적인 예로는 측정치와 예측치(prediction data)의 오차(error)를 최소화(minimization)하는 문제를 정의하여 푸는 최적화(optimization) 기반의 접근법이 있다. | |
물리적 모델에서 상태 변수는 무엇인가 | 동적 시스템은 상태 변수로 이루어진 모델(model)로 표현 가능하며, 이는 물리적 법칙에 따르는 모델 혹은 실험적인 모델일 수 있다. 물리적 모델에서 상태 변수는 원론적인(first- principle) 물리 현상을 나타내는 미분방정식(differential equation) 또는 차분방정식(difference equation)의 종속 변수 (dependent variable)이며, 실험적 모델에서 상태 변수는 일차 또는 이차 이상의 경험식(empirical equation)의 종속 변수이다. |
R. E. Kalman, "A new approach to linear filtering and prediction problems," Journal of Basic Engineering, vol. 82, pp. 35-45, 1960.
R. E. Kalman and R. S. Bucy, "New results in linear filtering and prediction theory," Journal of Basic Engineering, vol. 83, pp. 95-108, 1961.
D. G. Robertson and J. H. Lee, "A least squares formulation for state estimation," Journal of Process Control, vol. 5, pp. 291-299, 1995.
F. Daum, "Nonlinear filters: beyond the Kalman filter," Aerospace and Electronic Systems Magazine, IEEE, vol. 20, pp. 57-69, 2005.
J. B. Rawlings and B. R. Bakshi, "Particle filtering and moving horizon estimation," Computers & Chemical Engineering, vol. 30, pp. 1529-1541, 2006.
S. C. Patwardhan, S. Narasimhan, P. Jagadeesan, B. Gopaluni, and S. L Shah, "Nonlinear Bayesian state estimation: A review of recent developments," Control Engineering Practice, 2012.
A. Alessandri, M. Baglietto, G. Battistelli, and M. Gaggero, "Moving-horizon state estimation for nonlinear systems using neural networks," Neural Networks, IEEE Transactions on, vol. 22, pp. 768-780, 2011.
A. Alessandri, M. Baglietto, and G. Battistelli, "Moving-horizon state estimation for nonlinear discrete-time systems: New stability results and approximation schemes," Automatica, vol. 44, pp. 1753-1765, 2008.
M. Farina, G. Ferrari-Trecate, C. Romani, and R. Scattolini, "Moving horizon estimation for distributed nonlinear systems with application to cascade river reaches," Journal of Process Control, vol. 21, pp. 767-774, 2011.
M. Farina, G. Ferrari-Trecate, and R. Scattolini, "Distributed moving horizon estimation for linear constrained systems," Automatic Control, IEEE Transactions on, vol. 55, pp. 2462-2475, 2010.
M. Farina, G. Ferrari-Trecate, and R. Scattolini, "Distributed moving horizon estimation for nonlinear constrained systems," International Journal of Robust and Nonlinear Control, vol. 22, pp. 123-143, 2012.
P. Kuhl, M. Diehl, T. Kraus, J. P. Schloder, and H. G. Bock, "A real-time algorithm for moving horizon state and parameter estimation," Computers & Chemical Engineering, vol. 35, pp. 71-83, 2011.
J. Liu, "Moving horizon state estimation for nonlinear systems with bounded uncertainties," Chemical Engineering Science, 2013.
R. Lopez-Negrete, S. C. Patwardhan, and L. T. Biegler, "Constrained particle filter approach to approximate the arrival cost in Moving Horizon Estimation," Journal of Process Control, vol. 21, pp. 909-919, 2011.
C. C. Qu and J. Hahn, "Computation of arrival cost for moving horizon estimation via unscented Kalman filtering," Journal of Process Control, vol. 19, pp. 358-363, 2009.
J. B. Rawlings and L. Ji, "Optimization-based state estimation: Current status and some new results," Journal of Process Control, vol. 22, pp. 1439-1444, 2012.
S. Ungarala, "Computing arrival cost parameters in moving horizon estimation using sampling based filters," Journal of Process Control, vol. 19, pp. 1576-1588, 2009.
V. M. Zavala, "Stability analysis of an approximate scheme for moving horizon estimation," Computers & Chemical Engineering, vol. 34, pp. 1662-1670, 2010.
V. M. Zavala, C. D. Laird, and L. T. Biegler, "A fast moving horizon estimation algorithm based on nonlinear programming sensitivity," Journal of Process Control, vol. 18, pp. 876-884, 2008.
J. Zhang and J. Liu, "Distributed moving horizon state estimation for nonlinear systems with bounded uncertainties," Journal of Process Control, vol. 23, pp. 1281-1295, 2013.
B. Nicholson, R. Lopez-Negrete, and L. T. Biegler, "On-line state estimation of nonlinear dynamic systems with gross errors," Computers & Chemical Engineering, 2013.
I. Necoara, V. Nedelcu, and I. Dumitrache, "Parallel and distributed optimization methods for estimation and control in networks," Journal of Process Control, vol. 21, pp. 756-766, 2011.
M. Farina, G. Ferrari-Trecate, and R. Scattolini, "Moving-horizon partition-based state estimation of large-scale systems," Automatica, vol. 46, pp. 910-918, 2010.
R. Huang, L. T. Biegler, and S. C. Patwardhan, "Fast offset-free nonlinear model predictive control based on moving horizon estimation," Industrial & Engineering Chemistry Research, vol. 49, pp. 7882-7890, 2010.
A. Kupper, M. Diehl, J. P. Schloder, H. G. Bock, and S. Engell, "Efficient moving horizon state and parameter estimation for SMB processes," Journal of Process Control, vol. 19, pp. 785-802, 2009.
A. Kupper, L. Wirsching, M. Diehl, J. P. Schloder, H. G. Bock, and S. Engell, "Online identification of adsorption isotherms in SMB processes via efficient moving horizon state and parameter estimation," Computers & Chemical Engineering, vol. 34, pp. 1969-1983, 2010.
V. M. Zavala and L. T. Biegler, "Optimization-based strategies for the operation of low-density polyethylene tubular reactors: Moving horizon estimation," Computers & Chemical Engineering, vol. 33, pp. 379-390, 2009.
K. R. Muske and T. F. Edgar, "Nonlinear state estimation," in Nonlinear Process Control, 1997, pp. 311-370.
S. J. Julier and J. K. Uhlmann, "Unscented filtering and nonlinear estimation," Proc. of the IEEE, vol. 92, pp. 401-422, 2004.
R. Van Der Merwe, "Sigma-point Kalman filters for probabilistic inference in dynamic state-space models," University of Stellenbosch, 2004.
M. S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp, "A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking," Signal Processing, IEEE Transactions on, vol. 50, pp. 174-188, 2002.
R. Van Der Merwe, A. Doucet, N. De Freitas, and E. Wan, "The unscented particle filter," in NIPS, pp. 584-590, 2000.
G. Evensen, "The ensemble Kalman filter: Theoretical formulation and practical implementation," Ocean Dynamics, vol. 53, pp. 343-367, 2003.
G. Burgers, P. Jan van Leeuwen, and G. Evensen, "Analysis scheme in the ensemble Kalman filter," Monthly Weather Review, vol. 126, pp. 1719-1724, 1998.
T. C. Hsia, System Identification: Least-Squares Methods: Lexington books Lexington, 1977.
D. G. Robertson, J. H. Lee, and J. B. Rawlings, "A moving horizon-based approach for least-squares estimation," AIChE Journal, vol. 42, pp. 2209-2224, 1996.
K. R. Muske, J. B. Rawlings, and J. H. Lee, "Receding horizon recursive state estimation," in American Control Conference, pp. 900-904, 1993.
C. V. Rao, J. B. Rawlings, and J. H. Lee, "Constrained linear state estimation-a moving horizon approach," Automatica, vol. 37, pp. 1619-1628, 2001.
C. V. Rao and J. B. Rawlings, "Constrained process monitoring: Moving-horizon approach," AIChE Journal, vol. 48, pp. 97-109, 2002.
M. Diehl, H. G. Bock, and J. P. Schloder, "A real-time iteration scheme for nonlinear optimization in optimal feedback control," SIAM Journal on Control and Optimization, vol. 43, pp. 1714-1736, 2005.
M. Diehl, H. G. Bock, J. P. Schloder, R. Findeisen, Z. Nagy, and F. Allgower, "Real-time optimization and nonlinear model predictive control of processes governed by differentialalgebraic equations," Journal of Process Control, vol. 12, pp. 577-585, 2002.
V. M. Zavala and L. T. Biegler, "Optimization-based strategies for the operation of low-density polyethylene tubular reactors: nonlinear model predictive control," Computers & Chemical Engineering, vol. 33, pp. 1735-1746, 2009.
V. M. Zavala and L. T. Biegler, "The advanced-step NMPC controller: Optimality, stability and robustness," Automatica, vol. 45, pp. 86-93, 2009.
M. Diehl, H. J. Ferreau, and N. Haverbeke, "Efficient numerical methods for nonlinear MPC and moving horizon estimation," Nonlinear Model Predictive Control, Springer, pp. 391-417, 2009.
D. B. Leineweber, I. Bauer, H. G. Bock, and J. P. Schloder, "An efficient multiple shooting based reduced SQP strategy for large-scale dynamic process optimization. Part 1: theoretical aspects," Computers & Chemical Engineering, vol. 27, pp. 157-166, 2003.
J. B. Rawlings and B. T. Stewart, "Coordinating multiple optimization-based controllers: New opportunities and challenges," Journal of Process Control, vol. 18, pp. 839-845, 2008.
R. Scattolini, "Architectures for distributed and hierarchical model predictive control-a review," Journal of Process Control, vol. 19, pp. 723-731, 2009.
H. Michalska and D. Q. Mayne, "Moving horizon observers and observer-based control," Automatic Control, IEEE Transactions on, vol. 40, pp. 995-1006, 1995.
C. V. Rao, J. B. Rawlings, and D. Q. Mayne, "Constrained state estimation for nonlinear discrete-time systems: Stability and moving horizon approximations," Automatic Control, IEEE Transactions on, vol. 48, pp. 246-258, 2003.
D. R. Yang and K. S. Lee, "Monitoring of a distillation column using modified extended Kalman filter and a reduced order model," Computers & Chemical Engineering, vol. 21, pp. S565- S570, 1997.
R. M. Oisiovici and S. L. Cruz, "State estimation of batch distillation columns using an extended Kalman filter," Chemical Engineering Science, vol. 55, pp. 4667-4680, 2000.
R. M. Oisiovici and S. L. Cruz, "Inferential control of high-purity multicomponent batch distillation columns using an extended Kalman filter," Industrial & Engineering Chemistry Research, vol. 40, pp. 2628-2639, 2001.
M. J. Olanrewaju and M. A. Al-Arfaj, "Estimator-based control of reactive distillation system: Application of an extended Kalman filtering," Chemical Engineering Science, vol. 61, pp. 3386-3399, 2006.
D. J. Kozub and J. F. MacGregor, "State estimation for semi-batch polymerization reactors," Chemical Engineering Science, vol. 47, pp. 1047-1062, 1992.
N. Tudoroiu and K. Khorasani, "State estimation of the vinyl acetate reactor using unscented Kalman filters (UKF)," in Industrial Electronics and Control Applications, 2005. ICIECA 2005. International Conference on, 2007, pp. 4 pp.-4.
T. Chen, J. Morris, and E. Martin, "Particle filters for state and parameter estimation in batch processes," Journal of Process Control, vol. 15, pp. 665-673, 2005.
A. Romanenko and J. A. Castro, "The unscented filter as an alternative to the EKF for nonlinear state estimation: a simulation case study," Computers & Chemical Engineering, vol. 28, pp. 347-355, 2004.
A. Romanenko, L. O. Santos, and P. A. Afonso, "Unscented Kalman filtering of a simulated pH system," Industrial & Engineering Chemistry Research, vol. 43, pp. 7531-7538, 2004.
S. B. Chitralekha, J. Prakash, H. Raghavan, R. Gopaluni, and S. L. Shah, "A comparison of simultaneous state and parameter estimation schemes for a continuous fermentor reactor," Journal of Process Control, vol. 20, pp. 934-943, 2010.
A. Shenoy, J. Prakash, V. Prasad, S. Shah, and K. McAuley, "Practical issues in state estimation using particle filters: Case studies with polymer reactors," Journal of Process Control, 2012.
S. Kolas, B. Foss, and T. Schei, "Constrained nonlinear state estimation based on the UKF approach," Computers & Chemical Engineering, vol. 33, pp. 1386-1401, 2009.
J. Prakash, S. C. Patwardhan, and S. L. Shah, "On the choice of importance distributions for unconstrained and constrained state estimation using particle filter," Journal of Process Control, vol. 21, pp. 3-16, 2011.
X. Shao, B. Huang, and J. M. Lee, "Constrained Bayesian state estimation-A comparative study and a new particle filter based approach," Journal of Process Control, vol. 20, pp. 143-157, 2010.
J. Prakash, S. C. Patwardhan, and S. L. Shah, "Constrained nonlinear state estimation using ensemble Kalman filters," Industrial & Engineering Chemistry Research, vol. 49, pp. 2242-2253, 2010.
B. J. Spivey, J. D. Hedengren, and T. F. Edgar, "Constrained nonlinear estimation for industrial process fouling," Industrial & Engineering Chemistry Research, vol. 49, pp. 7824-7831, 2010.
E. L. Haseltine and J. B. Rawlings, "Critical evaluation of extended Kalman filtering and moving-horizon estimation," Industrial & Engineering Chemistry Research, vol. 44, pp. 2451-2460, 2005.
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