TY - GEN
T1 - Fast Moving Horizon Estimation of nonlinear processes via Carleman linearization
AU - Hashemian, Negar
AU - Armaou, Antonios
N1 - Publisher Copyright:
© 2015 American Automatic Control Council.
PY - 2015/7/28
Y1 - 2015/7/28
N2 - Moving Horizon Estimation (MHE) is a general method employed in many dynamic systems to monitor unmeasurable states. MHE can handle unavoidable physical constraints on the system by a constrained nonlinear optimization problem. However, since this approach requires repeated solving of the optimization problem, it is usually limited to slow-evolving, quasi-linear, low-order systems. In this work, we propose a method that accelerates the optimization procedure. To achieve this goal, Carleman linearization technique is employed to obtain a linear representation of a generic nonlinear system. Then, the sensitivity of the estimation error, gradient vector and Hessian matrix of the objective function are analytically derived. This information about the objective function significantly reduces computational costs and errors associated with numerical approximations of derivatives. Even though the representation appears linear, it is in fact a higher order approximation. Simulation results for a crystallization process show the efficiency and performance of the designed observer.
AB - Moving Horizon Estimation (MHE) is a general method employed in many dynamic systems to monitor unmeasurable states. MHE can handle unavoidable physical constraints on the system by a constrained nonlinear optimization problem. However, since this approach requires repeated solving of the optimization problem, it is usually limited to slow-evolving, quasi-linear, low-order systems. In this work, we propose a method that accelerates the optimization procedure. To achieve this goal, Carleman linearization technique is employed to obtain a linear representation of a generic nonlinear system. Then, the sensitivity of the estimation error, gradient vector and Hessian matrix of the objective function are analytically derived. This information about the objective function significantly reduces computational costs and errors associated with numerical approximations of derivatives. Even though the representation appears linear, it is in fact a higher order approximation. Simulation results for a crystallization process show the efficiency and performance of the designed observer.
UR - http://www.scopus.com/inward/record.url?scp=84940905165&partnerID=8YFLogxK
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U2 - 10.1109/ACC.2015.7171854
DO - 10.1109/ACC.2015.7171854
M3 - Conference contribution
AN - SCOPUS:84940905165
T3 - Proceedings of the American Control Conference
SP - 3379
EP - 3385
BT - ACC 2015 - 2015 American Control Conference
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2015 American Control Conference, ACC 2015
Y2 - 1 July 2015 through 3 July 2015
ER -