Moving horizon estimation (MHE) is an optimization approach that uses a series of measurements observed over time, containing noise (random variations) and other inaccuracies, and produces estimates of unknown variables or parameters. Unlike deterministic approaches, MHE requires an iterative approach that relies on linear programming or nonlinear programming solvers to find a solution.[1]
MHE reduces to the Kalman filter under certain simplifying conditions.[2] A critical evaluation of the extended Kalman filter and the MHE found that the MHE improved performance at the cost of increased computational expense.[3] Because of the computational expense, MHE has generally been applied to systems where there are greater computational resources and moderate to slow system dynamics. However, in the literature there are some methods to accelerate this method.[4][5]
- ^ J.D. Hedengren; R. Asgharzadeh Shishavan; K.M. Powell; T.F. Edgar (2014). "Nonlinear modeling, estimation and predictive control in APMonitor". Computers & Chemical Engineering. 70 (5): 133–148. doi:10.1016/j.compchemeng.2014.04.013. S2CID 5793446.
- ^ Rao, C.V.; Rawlings, J.B.; Maynes, D.Q (2003). "Constrained State Estimation for Nonlinear Discrete-Time Systems: Stability and Moving Horizon Approximations". IEEE Transactions on Automatic Control. 48 (2): 246–258. CiteSeerX 10.1.1.131.1613. doi:10.1109/tac.2002.808470.
- ^ Haseltine, E.J.; Rawlings, J.B. (2005). "Critical Evaluation of Extended Kalman Filtering and Moving-Horizon Estimation". Ind. Eng. Chem. Res. 44 (8): 2451–2460. doi:10.1021/ie034308l.
- ^ Hashemian, N.; Armaou, A. (2015). "Fast Moving Horizon Estimation of nonlinear processes via Carleman linearization". 2015 American Control Conference (ACC). pp. 3379–3385. doi:10.1109/ACC.2015.7171854. ISBN 978-1-4799-8684-2. S2CID 13251259.
- ^ Hashemian, N.; Armaou, A. (2016). "Simulation, model-reduction and state estimation of a two-component coagulation process". AIChE Journal. 62 (5): 1557–1567. doi:10.1002/aic.15146.