Name
Abstract | ||
|---|---|---|
The paper presents a new technique for the adaptive parameter estimation in nonlinear parameterized dynamical systems. The technique proposes an uncertainty set-update approach that guarantees forward invariance of the true value of the parameters. In addition, it is shown that in the presence of sufficiently exciting state trajectories, the parameter estimates converge to the true values and the uncertainty set vanishes around the true value of the parameters. Two simulation examples are presented that demonstrate the effectiveness of the technique. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/ACC.2011.5991365 | American Control Conference |
Keywords | DocType | ISSN |
adaptive estimation,nonlinear dynamical systems,parameter estimation,adaptive parameter estimation,forward invariance,nonlinear parameterized dynamical systems,nonlinearly parameterized nonlinear dynamical systems,uncertainty set-update approach,enzyme kinetics,excited states,kinetic theory,microbial growth,nonlinear regression,nonlinear system,system dynamics,dynamic system,rate of convergence,mathematical model,neural network,kinetics,process model | Conference | 0743-1619 |
ISBN | Citations | PageRank |
978-1-4577-0080-4 | 0 | 0.34 |
References | Authors | |
6 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Adetola, V. | 1 | 0 | 0.34 |
| Devon Lehrer | 2 | 6 | 0.85 |
| Martin Guay | 3 | 99 | 11.14 |