152 research outputs found
Asymptotic Stability of POD based Model Predictive Control for a semilinear parabolic PDE
In this article a stabilizing feedback control is computed for a semilinear
parabolic partial differential equation utilizing a nonlinear model predictive
(NMPC) method. In each level of the NMPC algorithm the finite time horizon open
loop problem is solved by a reduced-order strategy based on proper orthogonal
decomposition (POD). A stability analysis is derived for the combined POD-NMPC
algorithm so that the lengths of the finite time horizons are chosen in order
to ensure the asymptotic stability of the computed feedback controls. The
proposed method is successfully tested by numerical examples
Reduced Order Optimal Control of the Convective FitzHugh-Nagumo Equation
In this paper, we compare three model order reduction methods: the proper
orthogonal decomposition (POD), discrete empirical interpolation method (DEIM)
and dynamic mode decomposition (DMD) for the optimal control of the convective
FitzHugh-Nagumo (FHN) equations. The convective FHN equations consists of the
semi-linear activator and the linear inhibitor equations, modeling blood
coagulation in moving excitable media. The semilinear activator equation leads
to a non-convex optimal control problem (OCP). The most commonly used method in
reduced optimal control is POD. We use DEIM and DMD to approximate efficiently
the nonlinear terms in reduced order models. We compare the accuracy and
computational times of three reduced-order optimal control solutions with the
full order discontinuous Galerkin finite element solution of the convection
dominated FHN equations with terminal controls. Numerical results show that POD
is the most accurate whereas POD-DMD is the fastest
Optimal Control of Convective FitzHugh-Nagumo Equation
We investigate smooth and sparse optimal control problems for convective
FitzHugh-Nagumo equation with travelling wave solutions in moving excitable
media. The cost function includes distributed space-time and terminal
observations or targets. The state and adjoint equations are discretized in
space by symmetric interior point Galerkin (SIPG) method and by backward Euler
method in time. Several numerical results are presented for the control of the
travelling waves. We also show numerically the validity of the second order
optimality conditions for the local solutions of the sparse optimal control
problem for vanishing Tikhonov regularization parameter. Further, we estimate
the distance between the discrete control and associated local optima
numerically by the help of the perturbation method and the smallest eigenvalue
of the reduced Hessian
Nonlinear Model Reduction Based On The Finite Element Method With Interpolated Coefficients: Semilinear Parabolic Equations
For nonlinear reduced-order models, especially for those with non-polynomial
nonlinearities, the computational complexity still depends on the dimension of
the original dynamical system. As a result, the reduced-order model loses its
computational efficiency, which, however, is its the most significant
advantage. Nonlinear dimensional reduction methods, such as the discrete
empirical interpolation method, have been widely used to evaluate the nonlinear
terms at a low cost. But when the finite element method is utilized for the
spatial discretization, nonlinear snapshot generation requires inner products
to be fulfilled, which costs lots of off-line time. Numerical integrations are
also needed over elements sharing the selected interpolation points during the
simulation, which keeps on-line time high. In this paper, we extend the finite
element method with interpolated coefficients to nonlinear reduced-order
models. It approximates the nonlinear function in the reduced-order model by
its finite element interpolation, which makes coefficient matrices of the
nonlinear terms pre-computable and, thus, leads to great savings in the
computational efforts. Due to the separation of spatial and temporal variables
in the finite element interpolation, the discrete empirical interpolation
method can be directly applied on the nonlinear functions. Therefore, the main
computational hurdles when applying the discrete empirical interpolation method
in the finite element context are conquered. We also establish a rigorous
asymptotic error estimation, which shows that the proposed approach achieves
the same accuracy as that of the standard finite element method under certain
smoothness assumptions of the nonlinear functions. Several numerical tests are
presented to validate the proposed method and verify the theoretical results.Comment: 26 pages, 8 figure
Model Order Reduction by Proper Orthogonal Decomposition
We provide an introduction to POD-MOR with focus on (nonlinear) parametric
PDEs and (nonlinear) time-dependent PDEs, and PDE constrained optimization with
POD surrogate models as application. We cover the relation of POD and SVD, POD
from the infinite-dimensional perspective, reduction of nonlinearities,
certification with a priori and a posteriori error estimates, spatial and
temporal adaptivity, input dependency of the POD surrogate model, POD basis
update strategies in optimal control with surrogate models, and sketch related
algorithmic frameworks. The perspective of the method is demonstrated with
several numerical examples.Comment: arXiv admin note: substantial text overlap with arXiv:1701.0505
A residual based snapshot location strategy for POD in distributed optimal control of linear parabolic equations
In this paper we study the approximation of a distributed optimal control
problem for linear para\-bolic PDEs with model order reduction based on Proper
Orthogonal Decomposition (POD-MOR). POD-MOR is a Galerkin approach where the
basis functions are obtained upon information contained in time snapshots of
the parabolic PDE related to given input data. In the present work we show that
for POD-MOR in optimal control of parabolic equations it is important to have
knowledge about the controlled system at the right time instances. For the
determination of the time instances (snapshot locations) we propose an
a-posteriori error control concept which is based on a reformulation of the
optimality system of the underlying optimal control problem as a second order
in time and fourth order in space elliptic system which is approximated by a
space-time finite element method. Finally, we present numerical tests to
illustrate our approach and to show the effectiveness of the method in
comparison to existing approaches
Polynomial approximation of high-dimensional Hamilton–Jacobi–Bellman equations and applications to feedback control of semilinear parabolic PDES
© 2018 Society for Industrial and Applied Mathematics. A procedure for the numerical approximation of high-dimensional Hamilton–Jacobi–Bellman (HJB) equations associated to optimal feedback control problems for semilinear parabolic equations is proposed. Its main ingredients are a pseudospectral collocation approximation of the PDE dynamics and an iterative method for the nonlinear HJB equation associated to the feedback synthesis. The latter is known as the successive Galerkin approximation. It can also be interpreted as Newton iteration for the HJB equation. At every step, the associated linear generalized HJB equation is approximated via a separable polynomial approximation ansatz. Stabilizing feedback controls are obtained from solutions to the HJB equations for systems of dimension up to fourteen
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