TY - CONF
AB - In comparison to classical control approaches in the field of electrical drives like the field-oriented control (FOC), model predictive control (MPC) approaches are able to provide a higher control performance. This refers to shorter settling times, lower overshoots, and a better decoupling of control variables in case of multi-variable controls. However, this can only be achieved if the used prediction model covers the actual behavior of the plant sufficiently well. In case of model deviations, the performance utilizing MPC remains below its potential. This results in effects like increased current ripple or steady state setpoint deviations. In order to achieve a high control performance, it is therefore necessary to adapt the model to the real plant behavior. When using an online system identification, a less accurate model is sufficient for commissioning of the drive system. In this paper, the combination of a finite-control-set MPC (FCS-MPC) with a system identification is proposed. The method does not require high-frequency signal injection, but uses the measured values already required for the FCS-MPC. An evaluation of the least squares-based identification on a laboratory test bench showed that the model accuracy and thus the control performance could be improved by an online update of the prediction models.
AU - Hanke, Soren
AU - Peitz, Sebastian
AU - Wallscheid, Oliver
AU - Böcker, Joachim
AU - Dellnitz, Michael
ID - 10597
SN - 9781538694145
T2 - 2019 IEEE International Symposium on Predictive Control of Electrical Drives and Power Electronics (PRECEDE)
TI - Finite-Control-Set Model Predictive Control for a Permanent Magnet Synchronous Motor Application with Online Least Squares System Identification
ER -
TY - JOUR
AU - Sahai, Tuhin
AU - Ziessler, Adrian
AU - Klus, Stefan
AU - Dellnitz, Michael
ID - 16709
JF - Nonlinear Dynamics
SN - 0924-090X
TI - Continuous relaxations for the traveling salesman problem
ER -
TY - GEN
AB - Embedding techniques allow the approximations of finite dimensional
attractors and manifolds of infinite dimensional dynamical systems via
subdivision and continuation methods. These approximations give a topological
one-to-one image of the original set. In order to additionally reveal their
geometry we use diffusion mapst o find intrinsic coordinates. We illustrate our
results on the unstable manifold of the one-dimensional Kuramoto--Sivashinsky
equation, as well as for the attractor of the Mackey-Glass delay differential
equation.
AU - Gerlach, Raphael
AU - Koltai, Péter
AU - Dellnitz, Michael
ID - 16711
T2 - arXiv:1902.08824
TI - Revealing the intrinsic geometry of finite dimensional invariant sets of infinite dimensional dynamical systems
ER -
TY - JOUR
AB - In a wide range of applications it is desirable to optimally control a dynamical system with respect to concurrent, potentially competing goals. This gives rise to a multiobjective optimal control problem where, instead of computing a single optimal solution, the set of optimal compromises, the so-called Pareto set, has to be approximated. When the problem under consideration is described by a partial differential equation (PDE), as is the case for fluid flow, the computational cost rapidly increases and makes its direct treatment infeasible. Reduced order modeling is a very popular method to reduce the computational cost, in particular in a multi query context such as uncertainty quantification, parameter estimation or optimization. In this article, we show how to combine reduced order modeling and multiobjective optimal control techniques in order to efficiently solve multiobjective optimal control problems constrained by PDEs. We consider a global, derivative free optimization method as well as a local, gradient-based approach for which the optimality system is derived in two different ways. The methods are compared with regard to the solution quality as well as the computational effort and they are illustrated using the example of the flow around a cylinder and a backward-facing-step channel flow.
AU - Peitz, Sebastian
AU - Ober-Blöbaum, Sina
AU - Dellnitz, Michael
ID - 8753
IS - 1
JF - Acta Applicandae Mathematicae
SN - 0167-8019
TI - Multiobjective Optimal Control Methods for the Navier-Stokes Equations Using Reduced Order Modeling
VL - 161
ER -
TY - JOUR
AU - Peitz, Sebastian
AU - Ober-Blöbaum, Sina
AU - Dellnitz, Michael
ID - 16691
JF - Acta Applicandae Mathematicae
SN - 0167-8019
TI - Multiobjective Optimal Control Methods for the Navier-Stokes Equations Using Reduced Order Modeling
ER -
TY - JOUR
AB - We present a new framework for optimal and feedback control of PDEs using Koopman operator-based reduced order models (K-ROMs). The Koopman operator is a linear but infinite-dimensional operator which describes the dynamics of observables. A numerical approximation of the Koopman operator therefore yields a linear system for the observation of an autonomous dynamical system. In our approach, by introducing a finite number of constant controls, the dynamic control system is transformed into a set of autonomous systems and the corresponding optimal control problem into a switching time optimization problem. This allows us to replace each of these systems by a K-ROM which can be solved orders of magnitude faster. By this approach, a nonlinear infinite-dimensional control problem is transformed into a low-dimensional linear problem. Using a recent convergence result for the numerical approximation via Extended Dynamic Mode Decomposition (EDMD), we show that the value of the K-ROM based objective function converges in measure to the value of the full objective function. To illustrate the results, we consider the 1D Burgers equation and the 2D Navier–Stokes equations. The numerical experiments show remarkable performance concerning both solution times and accuracy.
AU - Peitz, Sebastian
AU - Klus, Stefan
ID - 10593
JF - Automatica
SN - 0005-1098
TI - Koopman operator-based model reduction for switched-system control of PDEs
VL - 106
ER -
TY - JOUR
AB - We address the general mathematical problem of computing the inverse p-th
root of a given matrix in an efficient way. A new method to construct iteration
functions that allow calculating arbitrary p-th roots and their inverses of
symmetric positive definite matrices is presented. We show that the order of
convergence is at least quadratic and that adaptively adjusting a parameter q
always leads to an even faster convergence. In this way, a better performance
than with previously known iteration schemes is achieved. The efficiency of the
iterative functions is demonstrated for various matrices with different
densities, condition numbers and spectral radii.
AU - Richters, Dorothee
AU - Lass, Michael
AU - Walther, Andrea
AU - Plessl, Christian
AU - Kühne, Thomas
ID - 21
IS - 2
JF - Communications in Computational Physics
TI - A General Algorithm to Calculate the Inverse Principal p-th Root of Symmetric Positive Definite Matrices
VL - 25
ER -
TY - JOUR
AU - Dellnitz, Michael
AU - Gebken, Bennet
AU - Gerlach, Raphael
AU - Klus, Stefan
ID - 16712
JF - Dynamical Systems
SN - 1468-9367
TI - On the equivariance properties of self-adjoint matrices
ER -
TY - GEN
AU - Jurgelucks, Benjamin
AU - Schulze, Veronika
AU - Feldmann, Nadine
AU - Claes, Leander
ID - 8482
T2 - GAMM 2019
TI - Arbitrary sensitivity for inverse problems in piezoelectricity
ER -
TY - CONF
AU - Schumacher, Jan
ID - 13106
T2 - Beiträge zum Mathematikunterricht 2019
TI - Rekonstruktion diagrammatischen Schließens am Beispiel der Subtraktion negativer Zahlen
ER -