Moving horizon estimation
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 like the Kalman filter, 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 MHE found improved performance of MHE with the only cost of improvement being the 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.
Applications
See also
- Alpha beta filter
- Data assimilation
- Ensemble Kalman filter
- Extended Kalman filter
- Invariant extended Kalman filter
- Fast Kalman filter
- Filtering problem (stochastic processes)
- Kernel adaptive filter
- Non-linear filter
- Particle filter
- Predictor corrector
- Recursive least squares
- Schmidt–Kalman filter
- Sliding mode control
- Wiener filter
References
- ↑ 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.
- ↑ Rao, C.V.; Rawlings, J.B. and 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. 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.
- ↑ Spivey, B.; Hedengren, J. D. and Edgar, T. F. (2010). "Constrained Nonlinear Estimation for Industrial Process Fouling". Industrial & Engineering Chemistry Research 49 (17): 7824–7831. doi:10.1021/ie9018116.
- ↑ Hedengren, J.D. (2012). Kevin C. Furman, Jin-Hwa Song, Amr El-Bakry, ed. Advanced Process Monitoring. Springer’s International Series in Operations Research and Management Science.
Further reading
- Rawlings, James B. (2009). Model Predictive Control: Theory and Design. Mathematics in Science and Engineering. Madison, WI: Nob Hill Publishing, LLC. p. 576. ISBN 978-0-9759377-0-9.