Name
Abstract | ||
|---|---|---|
In the last decade, moving horizon estimation (MHE) has emerged as a powerful technique for tackling the problem of estimating the state of a dynamic system in the presence of nonlinearities and disturbances. MHE is based on the idea of minimizing an estimation cost function defined on a sliding window composed of a finite number of time stages. The cost function is usually made up of two contributions: a prediction error computed on a recent batch of inputs and outputs; an arrival cost that serves the purpose of summarizing the past data. However, the diffusion of such techniques has been hampered by: i) the difficulty in choosing the arrival cost so as to ensure stability of the overall estimation scheme; ii) the request of an adequate computational effort on line. In this paper, both problems are addressed and possible solutions are proposed. First, by means of a novel stability analysis, it is constructively shown that under very general observability conditions a quadratic arrival cost is sufficient to ensure the stability of the estimation error provided that the weight matrix is adequately chosen. Second, a novel approximate MHE algorithm is proposed that is based on nonlinear programming sensitivity calculations. The approximate MHE algorithm has the same stability properties of the optimal one which make the overall approach suitable to be applied in real settings. Preliminary simulation results confirm the effectiveness of proposed method. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/CDC.2010.5718126 | CDC |
Keywords | Field | DocType |
observability,observability conditions,sliding window,weight matrix,nonlinear programming,dynamic system,moving horizon estimation,nonlinear systems,nonlinear programming sensitivity calculations,stability analysis,estimation cost function minimization,minimization,cost function,sensitivity,prediction error,nonlinear system,upper bound,asymptotic stability | Mathematical optimization,Observability,Nonlinear system,Sliding window protocol,Upper and lower bounds,Computer science,Control theory,Nonlinear programming,Quadratic equation,Minification,Exponential stability | Conference |
ISSN | ISBN | Citations |
0743-1546 | 978-1-4244-7745-6 | 14 |
PageRank | References | Authors |
1.09 | 19 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Angelo Alessandri | 1 | 323 | 30.46 |
| Marco Baglietto | 2 | 215 | 16.91 |
| Giorgio Battistelli | 3 | 623 | 46.03 |
| Victor M. Zavala | 4 | 221 | 28.04 |