Title :
Optimal reduced-order modeling for nonlinear distributed parameter systems
Author_Institution :
Dept. of Math., Virginia Tech, Blacksburg, VA
Abstract :
A method to develop reduced-order models for nonlinear distributed parameter systems is studied. The method is based on Galerkin projection, but the reduced-basis vectors are optimal for the dynamic model, found by minimizing the error between given full-order simulation data and the reduced-order model. This is achieved by formulating the basis selection problem as an optimal control problem with the reduced-order model as a constraint. This methodology allows a natural extension of reduced-order modeling ideas to nonlinear systems. A numerical experiment comparing the optimal reduced-order model to the popular proper orthogonal decomposition method is provided
Keywords :
Galerkin method; Karhunen-Loeve transforms; distributed parameter systems; nonlinear control systems; nonlinear dynamical systems; optimal control; reduced order systems; Galerkin projection; basis selection; dynamic model; nonlinear distributed parameter systems; optimal control problem; optimal reduced-order modeling; orthogonal decomposition; reduced-basis vectors; Contracts; Differential equations; Distributed parameter systems; Hydrogen; Large-scale systems; Mathematics; Nonlinear systems; Reduced order systems; Vectors; Weather forecasting;
Conference_Titel :
American Control Conference, 2006
Conference_Location :
Minneapolis, MN
Print_ISBN :
1-4244-0209-3
Electronic_ISBN :
1-4244-0209-3
DOI :
10.1109/ACC.2006.1656372
