Model order reduction approaches for infinite horizon optimal control problems via the HJB equation. (English) Zbl 06861108
Benner, Peter (ed.) et al., Model reduction of parametrized systems. Selected contributions based on the presentations at the MoRePaS conference, SISSA, Trieste, Italy, October 13–16, 2015. Cham: Springer. MS&A, Model. Simul. Appl. 17, 333-347 (2017).
Summary: We investigate feedback control for infinite horizon optimal control problems for partial differential equations. The method is based on the coupling between Hamilton-Jacobi-Bellman (HJB) equations and model reduction techniques. It is well-known that HJB equations suffer the so called curse of dimensionality and, therefore, a reduction of the dimension of the system is mandatory. In this report we focus on the infinite horizon optimal control problem with quadratic cost functionals. We compare several model reduction methods such as Proper Orthogonal Decomposition, Balanced Truncation and a new algebraic Riccati equation based approach. Finally, we present numerical examples and discuss several features of the different methods analyzing advantages and disadvantages of the reduction methods.
For the entire collection see [Zbl 1381.65001].
For the entire collection see [Zbl 1381.65001].
MSC:
| 65K10 | Numerical optimization and variational techniques |
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