Name
Abstract | ||
|---|---|---|
We present a method for robust input design for nonlinear state-space models. The method optimizes a scalar cost function of the Fisher information matrix over a set of marginal distributions of stationary processes. By using elements from graph theory we characterize such a set. Since the true system is unknown, the resulting optimization problem takes the uncertainty on the true value of the parameters into account. In addition, the required estimates of the information matrix are computed using particle methods, and the resulting problem is convex in the decision variables. Numerical examples illustrate the proposed technique by identifying models using the expectation–maximization algorithm. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.automatica.2016.11.030 | Automatica |
Keywords | Field | DocType |
System identification,Input design,Particle filter,Nonlinear systems | Mathematical optimization,Nonlinear system,Control theory,Computer science,Scalar (physics),Particle filter,Fisher information,Input design,System identification,Marginal distribution | Journal |
Volume | Issue | ISSN |
77 | 1 | 0005-1098 |
Citations | PageRank | References |
0 | 0.34 | 19 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Patricio E. Valenzuela | 1 | 7 | 4.00 |
| Johan Dahlin | 2 | 33 | 5.24 |
| Cristian R. Rojas | 3 | 252 | 43.97 |
| Thomas B. Schön | 4 | 744 | 72.66 |