Abstract
A solution is proposed to estimate the states in the nonlinear discrete time system. Moving Horizon Estimation (MHE) is used to obtain the approximated states by minimizing a criterion that is the Euclidean form of the difference between the estimated outputs and the measured ones over a finite time horizon. The differential evolution (DE) algorithm is incorporated into the implementation of MHE in order to solve the optimization problem which is presented as a nonlinear programming problem due to the constraints. The effectiveness of the approach is illustrated in simulated systems that have appeared in the moving horizon estimation literature.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Rao, C.V., Rawlings, J.B.: Nonlinear Moving Horizon Estimation. In: AllgoK wer, A.Z.F. (ed.): Nonlinear model predictive control, Progress in systems and control theory, pp. 45–69 (2000)
Price, K., Storn, R.: Differential Evolution - A Simple Evolution Strategy for Fast Optimization. Dr. Dobb’s Journal 22(4), 18–24 (1997)
Storn, R., Price, K.: Minimizing the Real Functions of the ICEC’96 Contest by Differential Evolution, pp. 842–844 (1996)
Wang, L(eds.): Intelligent Optimization Algorithms with Application,Tsinghua University & Springer Press (2001)
Huang, F.Z., Wang, L., He, Q.: An Effective Co-evolutionary Differential Evolution for Constrained Optimization. In Applied Mathematics and Computation, vol. doi:10.1016/ j.amc.(2006)07-105
Christopher, V.R., James, B.R., Jay, H.L.: Constrained Linear State Estimation-a Moving Horizon Approach. Automatica 37, 1619–1628 (2001)
Rao, C.V.: Moving Horizon Strategies for the Constrained Monitoring and Control of Nonlinear Discrete-time Systems. In: Ph.D. thesis, University of Wisconsin-Madison (2000)
Rao, C.V., Rawlings, J.B.: Constrained Process Monitoring: Moving-horizon Approach. Aiche Journal 48(1), 97–109 (2002)
Rao, C.V., Rawlings, J.B., Mayne, D.Q.: Constrained State Estimation for Nonlinear Discrete-time Systems: Stability and Moving Horizon Approximations. Ieee Transactions on Automatic Control 48(2), 246–258 (2003)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, Y., Wang, J., Liu, B. (2007). Constrained Nonlinear State Estimation – A Differential Evolution Based Moving Horizon Approach. In: Huang, DS., Heutte, L., Loog, M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2007. Lecture Notes in Computer Science(), vol 4682. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74205-0_122
Download citation
DOI: https://doi.org/10.1007/978-3-540-74205-0_122
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74201-2
Online ISBN: 978-3-540-74205-0
eBook Packages: Computer ScienceComputer Science (R0)