TY - THES
AB - Multiobjective optimization plays an increasingly important role in modern applications, where several criteria are often of equal importance. The task in multiobjective optimization and multiobjective optimal control is therefore to compute
the set of optimal compromises (the Pareto set) between the conflicting objectives.
Since – in contrast to the solution of a single objective optimization problem – the
Pareto set generally consists of an infinite number of solutions, the computational
effort can quickly become challenging. This is even more the case when many problems have to be solved, when the number of objectives is high, or when the objectives
are costly to evaluate. Consequently, this thesis is devoted to the identification and
exploitation of structure both in the Pareto set and the dynamics of the underlying
model as well as to the development of efficient algorithms for solving problems with
additional parameters, with a high number of objectives or with PDE-constraints.
These three challenges are addressed in three respective parts.
In the first part, predictor-corrector methods are extended to entire Pareto sets.
When certain smoothness assumptions are satisfied, then the set of parameter dependent Pareto sets possesses additional structure, i.e. it is a manifold. The tangent
space can be approximated numerically which yields a direction for the predictor
step. In the corrector step, the predicted set converges to the Pareto set at a new
parameter value. The resulting algorithm is applied to an example from autonomous
driving.
In the second part, the hierarchical structure of Pareto sets is investigated. When
considering a subset of the objectives, the resulting solution is a subset of the Pareto
set of the original problem. Under additional smoothness assumptions, the respective subsets are located on the boundary of the Pareto set of the full problem. This
way, the “skeleton” of a Pareto set can be computed and due to the exponential
increase in computing time with the number of objectives, the computations of
these subsets are significantly faster which is demonstrated using an example from
industrial laundries.
In the third part, PDE-constrained multiobjective optimal control problems are
addressed by reduced order modeling methods. Reduced order models exploit the
structure in the system dynamics, for example by describing the dynamics of only the
most energetic modes. The model reduction introduces an error in both the function values and their gradients, which has to be taken into account in the development of
algorithms. Both scalarization and set-oriented approaches are coupled with reduced
order modeling. Convergence results are presented and the numerical benefit is
investigated. The algorithms are applied to semi-linear heat flow problems as well
as to the Navier-Stokes equations.
AU - Peitz, Sebastian
ID - 10594
TI - Exploiting structure in multiobjective optimization and optimal control
ER -
TY - JOUR
AU - Biasco, Luca
AU - Di Gregorio, Laura
ID - 16499
JF - Archive for Rational Mechanics and Analysis
SN - 0003-9527
TI - A Birkhoff–Lewis Type Theorem for the Nonlinear Wave Equation
ER -
TY - CONF
AB - In this contribution we compare two different approaches to the implementation of a Model Predictive Controller in an electric vehicle with respect to the quality of the solution and real-time applicability. The goal is to develop an intelligent cruise control in order to extend the vehicle range, i.e. to minimize energy consumption, by computing the optimal torque profile for a given track. On the one hand, a path-based linear model with strong simplifications regarding the vehicle dynamics is used. On the other hand, a nonlinear model is employed in which the dynamics of the mechanical and electrical subsystem are modeled.
AU - Eckstein, Julian
AU - Peitz, Sebastian
AU - Schäfer, Kai
AU - Friedel, Patrick
AU - Köhler, Ulrich
AU - Hessel von Molo, Mirko
AU - Ober-Blöbaum, Sina
AU - Dellnitz, Michael
ID - 8758
SN - 2212-0173
T2 - Procedia Technology, 3rd International Conference on System-Integrated Intelligence: New Challenges for Product and Production Engineering
TI - A comparison of two predictive approaches to control the longitudinal dynamics of electric vehicles
VL - 26
ER -
TY - CHAP
AU - Dellnitz, Michael
AU - Froyland, Gary
AU - Sertl, Stefan
ID - 16553
SN - 9789810243593
T2 - Equadiff 99
TI - A Conjecture on the Existence of Isolated Eigenvalues of the Perron-Frobenius Operator
ER -
TY - CONF
AB - In this article we propose a descent method for equality and inequality constrained multiobjective optimization problems (MOPs) which generalizes the steepest descent method for unconstrained MOPs by Fliege and Svaiter to constrained problems by using two active set strategies. Under some regularity assumptions on the problem, we show that accumulation points of our descent method satisfy a necessary condition for local Pareto optimality. Finally, we show the typical behavior of our method in a numerical example.
AU - Gebken, Bennet
AU - Peitz, Sebastian
AU - Dellnitz, Michael
ID - 8750
SN - 1860-949X
T2 - Numerical and Evolutionary Optimization – NEO 2017
TI - A Descent Method for Equality and Inequality Constrained Multiobjective Optimization Problems
ER -
TY - GEN
AB - The efficient optimization method for locally Lipschitz continuous multiobjective optimization problems from [1] is extended from finite-dimensional problems to general Hilbert spaces. The method iteratively computes Pareto critical points, where in each iteration, an approximation of the subdifferential is computed in an efficient manner and then used to compute a common descent direction for all objective functions. To prove convergence, we present some new optimality results for nonsmooth multiobjective optimization problems in Hilbert spaces. Using these, we can show that every accumulation point of the sequence generated by our algorithm is Pareto critical under common assumptions. Computational efficiency for finding Pareto critical points is numerically demonstrated for multiobjective optimal control of an obstacle problem.
AU - Sonntag, Konstantin
AU - Gebken, Bennet
AU - Müller, Georg
AU - Peitz, Sebastian
AU - Volkwein, Stefan
ID - 51334
T2 - arXiv:2402.06376
TI - A Descent Method for Nonsmooth Multiobjective Optimization in Hilbert Spaces
ER -
TY - CONF
AU - Schütze, Oliver
AU - Talbi, El-ghazali
AU - Pulido, Gregorio Toscano
AU - Coello, Carlos Coello
AU - Santana-Quintero, Luis Vicente
ID - 16666
SN - 1424407087
T2 - 2007 IEEE Swarm Intelligence Symposium
TI - A Memetic PSO Algorithm for Scalar Optimization Problems
ER -
TY - JOUR
AU - Dellnitz, Michael
AU - Ober-Blöbaum, Sina
AU - Post, Marcus
AU - Schütze, Oliver
AU - Thiere, Bianca
ID - 16574
JF - Celestial Mechanics and Dynamical Astronomy
SN - 0923-2958
TI - A multi-objective approach to the design of low thrust space trajectories using optimal control
ER -
TY - JOUR
AB - We present a new algorithm for model predictive control of non-linear systems with respect to multiple, conflicting objectives. The idea is to provide a possibility to change the objective in real-time, e.g. as a reaction to changes in the environment or the system state itself. The algorithm utilises elements from various well-established concepts, namely multiobjective optimal control, economic as well as explicit model predictive control and motion planning with motion primitives. In order to realise real-time applicability, we split the computation into an online and an offline phase and we utilise symmetries in the open-loop optimal control problem to reduce the number of multiobjective optimal control problems that need to be solved in the offline phase. The results are illustrated using the example of an electric vehicle where the longitudinal dynamics are controlled with respect to the concurrent objectives arrival time and energy consumption.
AU - Peitz, Sebastian
AU - Schäfer, Kai
AU - Ober-Blöbaum, Sina
AU - Eckstein, Julian
AU - Köhler, Ulrich
AU - Dellnitz, Michael
ID - 8756
IS - 1
JF - Proceedings of the 20th World Congress of the International Federation of Automatic Control (IFAC)
SN - 2405-8963
TI - A multiobjective MPC approach for autonomously driven electric vehicles
VL - 50
ER -
TY - JOUR
AU - Peitz, Sebastian
AU - Schäfer, Kai
AU - Ober-Blöbaum, Sina
AU - Eckstein, Julian
AU - Köhler, Ulrich
AU - Dellnitz, Michael
ID - 16657
JF - IFAC-PapersOnLine
SN - 2405-8963
TI - A Multiobjective MPC Approach for Autonomously Driven Electric Vehicles * *This research was funded by the German Federal Ministry of Education and Research (BMBF) within the Leading-Edge Cluster Intelligent Technical Systems OstWestfalenLippe (it’s OWL).
ER -
TY - CONF
AU - Ober-Blöbaum, Sina
AU - Seifried, Albert
ID - 16643
SN - 9783033039629
T2 - 2013 European Control Conference (ECC)
TI - A multiobjective optimization approach for optimal control problems of mechanical systems with uncertainties
ER -
TY - JOUR
AU - Witting, Katrin
AU - Schulz, Bernd
AU - Dellnitz, Michael
AU - Böcker, Joachim
AU - Fröhleke, Norbert
ID - 16678
JF - International Journal on Software Tools for Technology Transfer
SN - 1433-2779
TI - A new approach for online multiobjective optimization of mechatronic systems
ER -
TY - CHAP
AU - Schütze, Oliver
ID - 16664
SN - 0302-9743
T2 - Lecture Notes in Computer Science
TI - A New Data Structure for the Nondominance Problem in Multi-objective Optimization
ER -
TY - JOUR
AB - AbstractApproximation of subdifferentials is one of the main tasks when computing descent directions for nonsmooth optimization problems. In this article, we propose a bisection method for weakly lower semismooth functions which is able to compute new subgradients that improve a given approximation in case a direction with insufficient descent was computed. Combined with a recently proposed deterministic gradient sampling approach, this yields a deterministic and provably convergent way to approximate subdifferentials for computing descent directions.
AU - Gebken, Bennet
ID - 51208
JF - Computational Optimization and Applications
KW - Applied Mathematics
KW - Computational Mathematics
KW - Control and Optimization
SN - 0926-6003
TI - A note on the convergence of deterministic gradient sampling in nonsmooth optimization
ER -
TY - JOUR
AU - Dellnitz, M
AU - Melbourne, I
ID - 16542
JF - Nonlinearity
SN - 0951-7715
TI - A note on the shadowing lemma and symmetric periodic points
ER -
TY - GEN
AB - We present a novel method for high-order phase reduction in networks of
weakly coupled oscillators and, more generally, perturbations of reducible
normally hyperbolic (quasi-)periodic tori. Our method works by computing an
asymptotic expansion for an embedding of the perturbed invariant torus, as well
as for the reduced phase dynamics in local coordinates. Both can be determined
to arbitrary degrees of accuracy, and we show that the phase dynamics may
directly be obtained in normal form. We apply the method to predict remote
synchronisation in a chain of coupled Stuart-Landau oscillators.
AU - von der Gracht, Sören
AU - Nijholt, Eddie
AU - Rink, Bob
ID - 45498
T2 - arXiv:2306.03320
TI - A parametrisation method for high-order phase reduction in coupled oscillator networks
ER -
TY - JOUR
AU - Day, S.
AU - Junge, O.
AU - Mischaikow, K.
ID - 16527
JF - SIAM Journal on Applied Dynamical Systems
SN - 1536-0040
TI - A Rigorous Numerical Method for the Global Analysis of Infinite-Dimensional Discrete Dynamical Systems
ER -
TY - JOUR
AU - Junge, Oliver
AU - Osinga, Hinke M.
ID - 16619
JF - ESAIM: Control, Optimisation and Calculus of Variations
SN - 1292-8119
TI - A set oriented approach to global optimal control
ER -
TY - JOUR
AU - Grüne, Lars
AU - Junge, Oliver
ID - 16613
JF - Systems & Control Letters
SN - 0167-6911
TI - A set oriented approach to optimal feedback stabilization
ER -
TY - JOUR
AU - Dellnitz, Michael
AU - Klus, Stefan
AU - Ziessler, Adrian
ID - 16581
JF - SIAM Journal on Applied Dynamical Systems
SN - 1536-0040
TI - A Set-Oriented Numerical Approach for Dynamical Systems with Parameter Uncertainty
ER -
TY - JOUR
AB - In this work we present a set-oriented path following method for the computation of relative global
attractors of parameter-dependent dynamical systems. We start with an initial approximation of the
relative global attractor for a fixed parameter λ0 computed by a set-oriented subdivision method.
By using previously obtained approximations of the parameter-dependent relative global attractor
we can track it with respect to a one-dimensional parameter λ > λ0 without restarting the whole
subdivision procedure. We illustrate the feasibility of the set-oriented path following method by
exploring the dynamics in low-dimensional models for shear flows during the transition to turbulence
and of large-scale atmospheric regime changes .
AU - Gerlach, Raphael
AU - Ziessler, Adrian
AU - Eckhardt, Bruno
AU - Dellnitz, Michael
ID - 16710
JF - SIAM Journal on Applied Dynamical Systems
SN - 1536-0040
TI - A Set-Oriented Path Following Method for the Approximation of Parameter Dependent Attractors
ER -
TY - JOUR
AU - Dellnitz, Michael
AU - Hohmann, Andreas
ID - 17015
JF - Numerische Mathematik
SN - 0029-599X
TI - A subdivision algorithm for the computation of unstable manifolds and global attractors
VL - 75
ER -
TY - JOUR
AB - The computation of global invariant manifolds has seen renewed interest in recent years. We survey different approaches for computing a global stable or unstable manifold of a vector field, where we concentrate on the case of a two-dimensional manifold. All methods are illustrated with the same example — the two-dimensional stable manifold of the origin in the Lorenz system.
AU - Krauskopf, B.
AU - Osinga, H. M.
AU - Doedel, E. J.
AU - Henderson, M. E.
AU - Guckenheimer, J.
AU - Vladimirsky, A.
AU - Dellnitz, M.
AU - Junge, O.
ID - 16627
JF - International Journal of Bifurcation and Chaos
SN - 0218-1274
TI - A Survey of Methods for Computing (un)stable Manifolds of Vector Fields
ER -
TY - JOUR
AB - Multiobjective optimization plays an increasingly important role in modern applications, where several criteria are often of equal importance. The task in multiobjective optimization and multiobjective optimal control is therefore to compute the set of optimal compromises (the Pareto set) between the conflicting objectives. The advances in algorithms and the increasing interest in Pareto-optimal solutions have led to a wide range of new applications related to optimal and feedback control, which results in new challenges such as expensive models or real-time applicability. Since the Pareto set generally consists of an infinite number of solutions, the computational effort can quickly become challenging, which is particularly problematic when the objectives are costly to evaluate or when a solution has to be presented very quickly. This article gives an overview of recent developments in accelerating multiobjective optimal control for complex problems where either PDE constraints are present or where a feedback behavior has to be achieved. In the first case, surrogate models yield significant speed-ups. Besides classical meta-modeling techniques for multiobjective optimization, a promising alternative for control problems is to introduce a surrogate model for the system dynamics. In the case of real-time requirements, various promising model predictive control approaches have been proposed, using either fast online solvers or offline-online decomposition. We also briefly comment on dimension reduction in many-objective optimization problems as another technique for reducing the numerical effort.
AU - Peitz, Sebastian
AU - Dellnitz, Michael
ID - 8751
IS - 2
JF - Mathematical and Computational Applications
SN - 2297-8747
TI - A Survey of Recent Trends in Multiobjective Optimal Control—Surrogate Models, Feedback Control and Objective Reduction
VL - 23
ER -
TY - JOUR
AU - Witting, Katrin
AU - Ober-Blöbaum, Sina
AU - Dellnitz, Michael
ID - 16677
JF - Journal of Global Optimization
SN - 0925-5001
TI - A variational approach to define robustness for parametric multiobjective optimization problems
ER -
TY - JOUR
AU - Noé, Frank
AU - Nüske, Feliks
ID - 21935
JF - Multiscale Modeling & Simulation
SN - 1540-3459
TI - A Variational Approach to Modeling Slow Processes in Stochastic Dynamical Systems
ER -
TY - JOUR
AU - Dellnitz, M
AU - Heinrich, C
ID - 16532
JF - Nonlinearity
SN - 0951-7715
TI - Admissible symmetry increasing bifurcations
ER -
TY - JOUR
AU - Vieluf, Solveig
AU - Mora, Karin
AU - Gölz, Christian
AU - Reuter, Eva-Maria
AU - Godde, Ben
AU - Dellnitz, Michael
AU - Reinsberger, Claus
AU - Voelcker-Rehage, Claudia
ID - 16714
JF - Neuroscience
SN - 0306-4522
TI - Age- and Expertise-Related Differences of Sensorimotor Network Dynamics during Force Control
ER -
TY - JOUR
AB - Recently multilevel subdivision techniques have been introduced in the numerical investigation of complicated dynamical behavior. We illustrate the applicability and efficiency of these methods by a detailed numerical study of Chua's circuit. In particular we will show that there exist two regions in phase space which are almost invariant in the sense that typical trajectories stay inside each of these sets on average for quite a long time.
AU - Dellnitz, Michael
AU - Junge, Oliver
ID - 16535
JF - International Journal of Bifurcation and Chaos
SN - 0218-1274
TI - Almost Invariant Sets in Chua's Circuit
ER -
TY - JOUR
AU - Guder, Rabbijah
AU - Dellnitz, Michael
AU - Kreuzer, Edwin
ID - 16614
JF - Chaos, Solitons & Fractals
SN - 0960-0779
TI - An adaptive method for the approximation of the generalized cell mapping
ER -
TY - JOUR
AU - Dellnitz, Michael
AU - Junge, Oliver
ID - 16536
JF - Computing and Visualization in Science
SN - 1432-9360
TI - An adaptive subdivision technique for the approximation of attractors and invariant measures
ER -
TY - JOUR
AU - Junge, Oliver
ID - 16617
JF - Dynamical Systems
SN - 1468-9367
TI - An adaptive subdivision technique for the approximation of attractors and invariant measures: proof of convergence
ER -
TY - JOUR
AU - Klus, Stefan
AU - Sahai, Tuhin
AU - Liu, Cong
AU - Dellnitz, Michael
ID - 16624
JF - Journal of Computational and Applied Mathematics
SN - 0377-0427
TI - An efficient algorithm for the parallel solution of high-dimensional differential equations
ER -
TY - JOUR
AB - In this article, we present an efficient descent method for locally Lipschitz
continuous multiobjective optimization problems (MOPs). The method is realized
by combining a theoretical result regarding the computation of descent
directions for nonsmooth MOPs with a practical method to approximate the
subdifferentials of the objective functions. We show convergence to points
which satisfy a necessary condition for Pareto optimality. Using a set of test
problems, we compare our method to the multiobjective proximal bundle method by
M\"akel\"a. The results indicate that our method is competitive while being
easier to implement. While the number of objective function evaluations is
larger, the overall number of subgradient evaluations is lower. Finally, we
show that our method can be combined with a subdivision algorithm to compute
entire Pareto sets of nonsmooth MOPs.
AU - Gebken, Bennet
AU - Peitz, Sebastian
ID - 16867
JF - Journal of Optimization Theory and Applications
TI - An efficient descent method for locally Lipschitz multiobjective optimization problems
VL - 188
ER -
TY - CONF
AU - Timmermann, Robert
AU - Dellnitz, Michael
ID - 17048
T2 - Performance Analysis of Sport IX, Part 8, Routledge
TI - Analysis of team and player performance using recorded trajectory data
ER -
TY - GEN
AB - Kernel transfer operators, which can be regarded as approximations of
transfer operators such as the Perron-Frobenius or Koopman operator in
reproducing kernel Hilbert spaces, are defined in terms of covariance and
cross-covariance operators and have been shown to be closely related to the
conditional mean embedding framework developed by the machine learning
community. The goal of this paper is to show how the dominant eigenfunctions of
these operators in combination with gradient-based optimization techniques can
be used to detect long-lived coherent patterns in high-dimensional time-series
data. The results will be illustrated using video data and a fluid flow
example.
AU - Klus, Stefan
AU - Peitz, Sebastian
AU - Schuster, Ingmar
ID - 16293
T2 - arXiv:1805.10118
TI - Analyzing high-dimensional time-series data using kernel transfer operator eigenfunctions
ER -
TY - CHAP
AU - Preis, Robert
AU - Monien, Burkhard
AU - Schamberger, Stefan
ID - 16658
SN - 2154-4573
T2 - Handbook of Approximation Algorithms and Metaheuristics
TI - Approximation Algorithms for Multilevel Graph Partitioning
ER -
TY - JOUR
AU - Demoures, Francois
AU - Gay-Balmaz, Francois
AU - Leitz, Thomas
AU - Leyendecker, Sigrid
AU - Ober-Blöbaum, Sina
AU - Ratiu, Tudor S.
ID - 16583
JF - PAMM
SN - 1617-7061
TI - Asynchronous variational Lie group integration for geometrically exact beam dynamics
ER -
TY - JOUR
AU - Nüske, Feliks
AU - Boninsegna, Lorenzo
AU - Clementi, Cecilia
ID - 21944
JF - The Journal of Chemical Physics
SN - 0021-9606
TI - Coarse-graining molecular systems by spectral matching
ER -
TY - JOUR
AU - Dellnitz, Michael
ID - 17014
JF - Schlaglichter der Forschung: Zum 75. Jahrestag der Universität Hamburg
TI - Collisions of chaotic attractors
ER -
TY - THES
AB - Mehrzieloptimierung behandelt Probleme, bei denen mehrere skalare Zielfunktionen simultan optimiert werden sollen. Ein Punkt ist in diesem Fall optimal, wenn es keinen anderen Punkt gibt, der mindestens genauso gut ist in allen Zielfunktionen und besser in mindestens einer Zielfunktion. Ein notwendiges Optimalitätskriterium lässt sich über Ableitungsinformationen erster Ordnung der Zielfunktionen herleiten. Die Menge der Punkte, die dieses notwendige Kriterium erfüllen, wird als Pareto-kritische Menge bezeichnet. Diese Arbeit enthält neue Resultate über Pareto-kritische Mengen für glatte und nicht-glatte Mehrzieloptimierungsprobleme, sowohl was deren Berechnung betrifft als auch deren Struktur. Im glatten Fall erfolgt die Berechnung über ein Fortsetzungsverfahren, im nichtglatten Fall über ein Abstiegsverfahren. Anschließend wird die Struktur des Randes der Pareto-kritischen Menge analysiert, welcher aus Pareto-kritischen Mengen kleinerer Subprobleme besteht. Schlussendlich werden inverse Probleme betrachtet, bei denen zu einer gegebenen Datenmenge ein Zielfunktionsvektor gefunden werden soll, für den die Datenpunkte kritisch sind.
AU - Gebken, Bennet
ID - 31556
TI - Computation and analysis of Pareto critical sets in smooth and nonsmooth multiobjective optimization
ER -
TY - CHAP
AU - Deuflhard, Peter
AU - Dellnitz, Michael
AU - Junge, Oliver
AU - Schütte, Christof
ID - 16584
SN - 1439-7358
T2 - Computational Molecular Dynamics: Challenges, Methods, Ideas
TI - Computation of Essential Molecular Dynamics by Subdivision Techniques
ER -
TY - JOUR
AU - Dellnitz, M.
AU - Witting, K.
ID - 16545
JF - International Journal of Computing Science and Mathematics
SN - 1752-5055
TI - Computation of robust Pareto points
ER -
TY - JOUR
AU - Aston, P. J.
AU - Dellnitz, M.
ID - 16498
JF - Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences
SN - 1364-5021
TI - Computation of the dominant Lyapunov exponent via spatial integration using matrix norms
ER -
TY - JOUR
AU - Dellnitz, Michael
ID - 17012
IS - 3
JF - IMA Journal of Numerical Analysis
TI - Computational bifurcation of periodic solutions in systems with symmetry
VL - 12
ER -
TY - JOUR
AU - Dellnitz, Michael
AU - Werner, Bodo
ID - 16682
JF - Journal of Computational and Applied Mathematics
SN - 0377-0427
TI - Computational methods for bifurcation problems with symmetries—with special attention to steady state and Hopf bifurcation points
ER -
TY - JOUR
AU - Gail, Tobias
AU - Leyendecker, Sigrid
AU - Ober-Blöbaum, Sina
ID - 16608
JF - PAMM
SN - 1617-7061
TI - Computing time investigations for variational multirate integration
ER -
TY - CHAP
AU - Dellnitz, Michael
AU - Preis, Robert
ID - 16543
SN - 0302-9743
T2 - Lecture Notes in Computer Science
TI - Congestion and Almost Invariant Sets in Dynamical Systems
ER -
TY - CHAP
AU - Dellnitz, Michael
AU - Padberg, Kathrin
AU - Preis, Robert
AU - Thiere, Bianca
ID - 16575
SN - 9789048198832
T2 - Nonlinear Science and Complexity
TI - Continuous and Discrete Concepts for Detecting Transport Barriers in the Planar Circular Restricted Three Body Problem
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 - JOUR
AU - Flaßkamp, Kathrin
AU - Timmermann, Julia
AU - Ober-Blöbaum, Sina
AU - Trächtler, Ansgar
ID - 16597
JF - International Journal of Control
SN - 0020-7179
TI - Control strategies on stable manifolds for energy-efficient swing-ups of double pendula
ER -
TY - GEN
AB - In a recent article, we presented a framework to control nonlinear partial
differential equations (PDEs) by means of Koopman operator based reduced models
and concepts from switched systems. The main idea was to transform a control
system into a set of autonomous systems for which the optimal switching
sequence has to be computed. These individual systems can be approximated very
efficiently by reduced order models obtained from data, and one can guarantee
equality of the full and the reduced objective function under certain
assumptions. In this article, we extend these results to continuous control
inputs using convex combinations of multiple Koopman operators corresponding to
constant controls, which results in a bilinear control system. Although
equality of the objectives can be carried over when the PDE depends linearly on
the control, we show that this approach is also valid in other scenarios using
several flow control examples of varying complexity.
AU - Peitz, Sebastian
ID - 16292
T2 - arXiv:1801.06419
TI - Controlling nonlinear PDEs using low-dimensional bilinear approximations obtained from data
ER -
TY - JOUR
AU - Schütze, Oliver
AU - Laumanns, Marco
AU - Coello Coello, Carlos A.
AU - Dellnitz, Michael
AU - Talbi, El-Ghazali
ID - 16668
JF - Journal of Global Optimization
SN - 0925-5001
TI - Convergence of stochastic search algorithms to finite size pareto set approximations
ER -
TY - CHAP
AU - Schütze, Oliver
AU - Mostaghim, Sanaz
AU - Dellnitz, Michael
AU - Teich, Jürgen
ID - 16665
SN - 0302-9743
T2 - Lecture Notes in Computer Science
TI - Covering Pareto Sets by Multilevel Evolutionary Subdivision Techniques
ER -
TY - JOUR
AU - Dellnitz, M.
AU - Sch�tze, O.
AU - Hestermeyer, T.
ID - 16684
JF - Journal of Optimization Theory and Applications
SN - 0022-3239
TI - Covering Pareto Sets by Multilevel Subdivision Techniques
ER -
TY - JOUR
AU - Dellnitz, Michael
AU - Field, Michael
AU - Golubitsky, Martin
AU - Ma, Jun
AU - Hohmann, Andreas
ID - 16550
JF - International Journal of Bifurcation and Chaos
SN - 0218-1274
TI - Cycling Chaos
ER -
TY - JOUR
AB - We derive a data-driven method for the approximation of the Koopman generator called gEDMD, which can be regarded as a straightforward extension of EDMD (extended dynamic mode decomposition). This approach is applicable to deterministic and stochastic dynamical systems. It can be used for computing eigenvalues, eigenfunctions, and modes of the generator and for system identification. In addition to learning the governing equations of deterministic systems, which then reduces to SINDy (sparse identification of nonlinear dynamics), it is possible to identify the drift and diffusion terms of stochastic differential equations from data. Moreover, we apply gEDMD to derive coarse-grained models of high-dimensional systems, and also to determine efficient model predictive control strategies. We highlight relationships with other methods and demonstrate the efficacy of the proposed methods using several guiding examples and prototypical molecular dynamics problems.
AU - Klus, Stefan
AU - Nüske, Feliks
AU - Peitz, Sebastian
AU - Niemann, Jan-Hendrik
AU - Clementi, Cecilia
AU - Schütte, Christof
ID - 16288
JF - Physica D: Nonlinear Phenomena
SN - 0167-2789
TI - Data-driven approximation of the Koopman generator: Model reduction, system identification, and control
VL - 406
ER -
TY - JOUR
AB - In recent years, the success of the Koopman operator in dynamical systems
analysis has also fueled the development of Koopman operator-based control
frameworks. In order to preserve the relatively low data requirements for an
approximation via Dynamic Mode Decomposition, a quantization approach was
recently proposed in [Peitz & Klus, Automatica 106, 2019]. This way, control
of nonlinear dynamical systems can be realized by means of switched systems
techniques, using only a finite set of autonomous Koopman operator-based
reduced models. These individual systems can be approximated very efficiently
from data. The main idea is to transform a control system into a set of
autonomous systems for which the optimal switching sequence has to be computed.
In this article, we extend these results to continuous control inputs using
relaxation. This way, we combine the advantages of the data efficiency of
approximating a finite set of autonomous systems with continuous controls. We
show that when using the Koopman generator, this relaxation --- realized by
linear interpolation between two operators --- does not introduce any error for
control affine systems. This allows us to control high-dimensional nonlinear
systems using bilinear, low-dimensional surrogate models. The efficiency of the
proposed approach is demonstrated using several examples with increasing
complexity, from the Duffing oscillator to the chaotic fluidic pinball.
AU - Peitz, Sebastian
AU - Otto, Samuel E.
AU - Rowley, Clarence W.
ID - 16309
IS - 3
JF - SIAM Journal on Applied Dynamical Systems
TI - Data-Driven Model Predictive Control using Interpolated Koopman Generators
VL - 19
ER -
TY - JOUR
AU - Klus, Stefan
AU - Nüske, Feliks
AU - Koltai, Péter
AU - Wu, Hao
AU - Kevrekidis, Ioannis
AU - Schütte, Christof
AU - Noé, Frank
ID - 21941
JF - Journal of Nonlinear Science
SN - 0938-8974
TI - Data-Driven Model Reduction and Transfer Operator Approximation
ER -
TY - JOUR
AB - The control of complex systems is of critical importance in many branches of science, engineering, and industry, many of which are governed by nonlinear partial differential equations. Controlling an unsteady fluid flow is particularly important, as flow control is a key enabler for technologies in energy (e.g., wind, tidal, and combustion), transportation (e.g., planes, trains, and automobiles), security (e.g., tracking airborne contamination), and health (e.g., artificial hearts and artificial respiration). However, the high-dimensional, nonlinear, and multi-scale dynamics make real-time feedback control infeasible. Fortunately, these high- dimensional systems exhibit dominant, low-dimensional patterns of activity that can be exploited for effective control in the sense that knowledge of the entire state of a system is not required. Advances in machine learning have the potential to revolutionize flow control given its ability to extract principled, low-rank feature spaces characterizing such complex systems.We present a novel deep learning modelpredictive control framework that exploits low-rank features of the flow in order to achieve considerable improvements to control performance. Instead of predicting the entire fluid state, we use a recurrent neural network (RNN) to accurately predict the control relevant quantities of the system, which are then embedded into an MPC framework to construct a feedback loop. In order to lower the data requirements and to improve the prediction accuracy and thus the control performance, incoming sensor data are used to update the RNN online. The results are validated using varying fluid flow examples of increasing complexity.
AU - Bieker, Katharina
AU - Peitz, Sebastian
AU - Brunton, Steven L.
AU - Kutz, J. Nathan
AU - Dellnitz, Michael
ID - 16290
JF - Theoretical and Computational Fluid Dynamics
SN - 0935-4964
TI - Deep model predictive flow control with limited sensor data and online learning
VL - 34
ER -
TY - JOUR
AB - We present a flexible trust region descend algorithm for unconstrained and
convexly constrained multiobjective optimization problems. It is targeted at
heterogeneous and expensive problems, i.e., problems that have at least one
objective function that is computationally expensive. The method is
derivative-free in the sense that neither need derivative information be
available for the expensive objectives nor are gradients approximated using
repeated function evaluations as is the case in finite-difference methods.
Instead, a multiobjective trust region approach is used that works similarly to
its well-known scalar pendants. Local surrogate models constructed from
evaluation data of the true objective functions are employed to compute
possible descent directions. In contrast to existing multiobjective trust
region algorithms, these surrogates are not polynomial but carefully
constructed radial basis function networks. This has the important advantage
that the number of data points scales linearly with the parameter space
dimension. The local models qualify as fully linear and the corresponding
general scalar framework is adapted for problems with multiple objectives.
Convergence to Pareto critical points is proven and numerical examples
illustrate our findings.
AU - Berkemeier, Manuel Bastian
AU - Peitz, Sebastian
ID - 21337
IS - 2
JF - Mathematical and Computational Applications
TI - Derivative-Free Multiobjective Trust Region Descent Method Using Radial Basis Function Surrogate Models
VL - 26
ER -
TY - CONF
AU - Li, R.
AU - Pottharst, A.
AU - Frohieke, N.
AU - Becker, J.
AU - Witting, K.
AU - Dellnitz, M.
AU - Znamenshchykov, O.
AU - Feldmann, R.
ID - 16631
SN - 0780389751
T2 - Twentieth Annual IEEE Applied Power Electronics Conference and Exposition, 2005. APEC 2005.
TI - Design and implementation of a hybrid energy supply system for railway vehicles
ER -
TY - JOUR
AU - Schütze, Oliver
AU - Vasile, Massimiliano
AU - Junge, Oliver
AU - Dellnitz, Michael
AU - Izzo, Dario
ID - 16669
JF - Engineering Optimization
SN - 0305-215X
TI - Designing optimal low-thrust gravity-assist trajectories using space pruning and a multi-objective approach
ER -
TY - JOUR
AU - Froyland, Gary
AU - Dellnitz, Michael
ID - 16600
JF - SIAM Journal on Scientific Computing
SN - 1064-8275
TI - Detecting and Locating Near-Optimal Almost-Invariant Sets and Cycles
ER -
TY - CONF
AU - Thiere, Bianca
AU - Ober-Blöbaum, Sina
AU - Pergola, Pierpaolo
ID - 16675
SN - 9781624101502
T2 - AIAA/AAS Astrodynamics Specialist Conference
TI - Detecting Initial Guesses for Trajectories in the (P)CRTBP
ER -
TY - JOUR
AU - Barany, Ernest
AU - Dellnitz, Michael
AU - Golubitsky, Martin
ID - 16518
JF - Physica D: Nonlinear Phenomena
SN - 0167-2789
TI - Detecting the symmetry of attractors
ER -
TY - JOUR
AU - Froyland, Gary
AU - Padberg, Kathrin
AU - England, Matthew H.
AU - Treguier, Anne Marie
ID - 16602
JF - Physical Review Letters
SN - 0031-9007
TI - Detection of Coherent Oceanic Structures via Transfer Operators
ER -
TY - JOUR
AU - Elsässer, Robert
AU - Monien, Burkhard
AU - Preis, Robert
ID - 16586
JF - Theory of Computing Systems
SN - 1432-4350
TI - Diffusion Schemes for Load Balancing on Heterogeneous Networks
ER -
TY - JOUR
AU - Meyer, A.
ID - 16635
JF - IFAC Proceedings Volumes
SN - 1474-6670
TI - Discontinuity Induced Bifurcations in Timed Continuous Petri Nets
ER -
TY - CONF
AB - This paper formulates the dynamical equations of mechanics subject to holonomic constraints in terms of the states and controls using a constrained version of the Lagrange-d’Alembert principle. Based on a discrete version of this principle, a structure preserving time-stepping scheme is derived. It is shown that this respect for the mechanical structure (such as a reliable computation of the energy and momentum budget, without numerical dissipation) is retained when the system is reduced to its minimal dimension by the discrete null space method. Together with initial and final conditions on the configuration and conjugate momentum, the reduced time-stepping equations serve as nonlinear equality constraints for the minimisation of a given cost functional. The algorithm yields a sequence of discrete configurations together with a sequence of actuating forces, optimally guiding the system from the initial to the desired final state. The resulting discrete optimal control algorithm is shown to have excellent energy and momentum properties, which are illustrated by two specific examples, namely reorientation and repositioning of a rigid body subject to external forces and the reorientation of a rigid body with internal momentum wheels.
AU - Leyendecker, Sigrid
AU - Ober-Blöbaum, Sina
AU - Marsden, Jerrold E.
AU - Ortiz, Michael
ID - 16630
SN - 079184806X
T2 - Volume 5: 6th International Conference on Multibody Systems, Nonlinear Dynamics, and Control, Parts A, B, and C
TI - Discrete Mechanics and Optimal Control for Constrained Multibody Dynamics
ER -
TY - JOUR
AU - Demoures, F.
AU - Gay-Balmaz, F.
AU - Leyendecker, S.
AU - Ober-Blöbaum, S.
AU - Ratiu, T. S.
AU - Weinand, Y.
ID - 16582
JF - Numerische Mathematik
SN - 0029-599X
TI - Discrete variational Lie group formulation of geometrically exact beam dynamics
ER -
TY - CONF
AU - Specht, Andreas
AU - Ober-Blobaum, Sina
AU - Wallscheid, Oliver
AU - Romaus, Christoph
AU - Bocker, Joachim
ID - 16672
SN - 9781467349741
T2 - 2013 International Electric Machines & Drives Conference
TI - Discrete-time model of an IPMSM based on variational integrators
ER -
TY - CONF
AU - Flasskamp, Kathrin
AU - Murphey, Todd
AU - Ober-Blobaum, Sina
ID - 16594
SN - 9783033039629
T2 - 2013 European Control Conference (ECC)
TI - Discretized switching time optimization problems
ER -
TY - CONF
AU - Klöpper, Benjamin
AU - Podlogar, Herbert
AU - Gausemeier, Jürgen
AU - Witting, Katrin
ID - 16623
SN - 9780769532998
T2 - 2008 19th International Conference on Database and Expert Systems Applications
TI - Domain Spanning Search for the Identification of Solution Patterns for the Conceptual Design of Self-Optimizing Systems
ER -
TY - JOUR
AB - Many problems in science and engineering require an efficient numerical approximation of integrals or solutions to differential equations. For systems with rapidly changing dynamics, an equidistant discretization is often inadvisable as it results in prohibitively large errors or computational effort. To this end, adaptive schemes, such as solvers based on Runge–Kutta pairs, have been developed which adapt the step size based on local error estimations at each step. While the classical schemes apply very generally and are highly efficient on regular systems, they can behave suboptimally when an inefficient step rejection mechanism is triggered by structurally complex systems such as chaotic systems. To overcome these issues, we propose a method to tailor numerical schemes to the problem class at hand. This is achieved by combining simple, classical quadrature rules or ODE solvers with data-driven time-stepping controllers. Compared with learning solution operators to ODEs directly, it generalizes better to unseen initial data as our approach employs classical numerical schemes as base methods. At the same time it can make use of identified structures of a problem class and, therefore, outperforms state-of-the-art adaptive schemes. Several examples demonstrate superior efficiency. Source code is available at https://github.com/lueckem/quadrature-ML.
AU - Dellnitz, Michael
AU - Hüllermeier, Eyke
AU - Lücke, Marvin
AU - Ober-Blöbaum, Sina
AU - Offen, Christian
AU - Peitz, Sebastian
AU - Pfannschmidt, Karlson
ID - 21600
IS - 2
JF - SIAM Journal on Scientific Computing
TI - Efficient time stepping for numerical integration using reinforcement learning
VL - 45
ER -
TY - CHAP
AB - With the ever increasing capabilities of sensors and controllers, autonomous driving is quickly becoming a reality. This disruptive change in the automotive industry poses major challenges for manufacturers as well as suppliers as entirely new design and testing strategies have to be developed to remain competitive. Most importantly, the complexity of autonomously driving vehicles in a complex, uncertain, and safety-critical environment requires new testing procedures to cover the almost infinite range of potential scenarios.
AU - Peitz, Sebastian
AU - Dellnitz, Michael
AU - Bannenberg, Sebastian
ED - Bock, H. G.
ED - Küfer, K.-H.
ED - Maas, P.
ED - Milde, A.
ED - Schulz, V.
ID - 30294
SN - 1612-3956
T2 - German Success Stories in Industrial Mathematics
TI - Efficient Virtual Design and Testing of Autonomous Vehicles
VL - 35
ER -
TY - CHAP
AU - Dellnitz, Michael
AU - Scheurle, Jürgen
ID - 16544
SN - 9789401044134
T2 - Dynamics, Bifurcation and Symmetry
TI - Eigenvalue Movement for a Class of Reversible Hamiltonian Systems with Three Degrees of Freedom
ER -
TY - JOUR
AU - Goelz, Christian
AU - Mora, Karin
AU - Stroehlein, Julia Kristin
AU - Haase, Franziska Katharina
AU - Dellnitz, Michael
AU - Reinsberger, Claus
AU - Vieluf, Solveig
ID - 21195
JF - Cognitive Neurodynamics
TI - Electrophysiological signatures of dedifferentiation differ between fit and less fit older adults
ER -
TY - CONF
AU - Flaskamp, K.
AU - Ober-Blobaum, S.
ID - 16589
SN - 9781457710964
T2 - 2012 American Control Conference (ACC)
TI - Energy efficient control for mechanical systems based on inherent dynamical structures
ER -
TY - CONF
AU - Knoke, Tobias
AU - Romaus, Christoph
AU - Bocker, Joachim
AU - Dell'Aere, Alessandro
AU - Witting, Katrin
ID - 16626
SN - 1553-572X
T2 - IECON 2006 - 32nd Annual Conference on IEEE Industrial Electronics
TI - Energy Management for an Onboard Storage System Based on Multi-Objective Optimization
ER -
TY - CONF
AU - Schneider, T.
AU - Schulz, B.
AU - Henke, C.
AU - Witting, K.
AU - Steenken, D.
AU - Bocker, J.
ID - 16663
SN - 9781424442515
T2 - 2009 IEEE International Electric Machines and Drives Conference
TI - Energy transfer via linear doubly-fed motor in different operating modes
ER -
TY - JOUR
AB - In real-world problems, uncertainties (e.g., errors in the measurement,
precision errors) often lead to poor performance of numerical algorithms when
not explicitly taken into account. This is also the case for control problems,
where optimal solutions can degrade in quality or even become infeasible. Thus,
there is the need to design methods that can handle uncertainty. In this work,
we consider nonlinear multi-objective optimal control problems with uncertainty
on the initial conditions, and in particular their incorporation into a
feedback loop via model predictive control (MPC). In multi-objective optimal
control, an optimal compromise between multiple conflicting criteria has to be
found. For such problems, not much has been reported in terms of uncertainties.
To address this problem class, we design an offline/online framework to compute
an approximation of efficient control strategies. This approach is closely
related to explicit MPC for nonlinear systems, where the potentially expensive
optimization problem is solved in an offline phase in order to enable fast
solutions in the online phase. In order to reduce the numerical cost of the
offline phase, we exploit symmetries in the control problems. Furthermore, in
order to ensure optimality of the solutions, we include an additional online
optimization step, which is considerably cheaper than the original
multi-objective optimization problem. We test our framework on a car
maneuvering problem where safety and speed are the objectives. The
multi-objective framework allows for online adaptations of the desired
objective. Alternatively, an automatic scalarizing procedure yields very
efficient feedback controls. Our results show that the method is capable of
designing driving strategies that deal better with uncertainties in the initial
conditions, which translates into potentially safer and faster driving
strategies.
AU - Hernández Castellanos, Carlos Ignacio
AU - Ober-Blöbaum, Sina
AU - Peitz, Sebastian
ID - 16297
JF - International Journal of Robust and Nonlinear Control
TI - Explicit Multi-objective Model Predictive Control for Nonlinear Systems Under Uncertainty
VL - 30(17)
ER -
TY - JOUR
AB - Model predictive control is a prominent approach to construct a feedback
control loop for dynamical systems. Due to real-time constraints, the major
challenge in MPC is to solve model-based optimal control problems in a very
short amount of time. For linear-quadratic problems, Bemporad et al. have
proposed an explicit formulation where the underlying optimization problems are
solved a priori in an offline phase. In this article, we present an extension
of this concept in two significant ways. We consider nonlinear problems and -
more importantly - problems with multiple conflicting objective functions. In
the offline phase, we build a library of Pareto optimal solutions from which we
then obtain a valid compromise solution in the online phase according to a
decision maker's preference. Since the standard multi-parametric programming
approach is no longer valid in this situation, we instead use interpolation
between different entries of the library. To reduce the number of problems that
have to be solved in the offline phase, we exploit symmetries in the dynamical
system and the corresponding multiobjective optimal control problem. The
results are verified using two different examples from autonomous driving.
AU - Ober-Blöbaum, Sina
AU - Peitz, Sebastian
ID - 16294
JF - International Journal of Robust and Nonlinear Control
TI - Explicit multiobjective model predictive control for nonlinear systems with symmetries
VL - 31(2)
ER -
TY - JOUR
AU - Dellnitz, Michael
AU - Hohmann, Andreas
AU - Junge, Oliver
AU - Rumpf, Martin
ID - 16552
JF - Chaos: An Interdisciplinary Journal of Nonlinear Science
SN - 1054-1500
TI - Exploring invariant sets and invariant measures
ER -
TY - CHAP
AU - Froyland, Gary
ID - 16598
SN - 9781461266488
T2 - Nonlinear Dynamics and Statistics
TI - Extracting Dynamical Behavior via Markov Models
ER -
TY - GEN
AB - We present a new gradient-like dynamical system related to unconstrained convex smooth multiobjective optimization which involves inertial effects and asymptotic vanishing damping. To the best of our knowledge, this system is the first inertial gradient-like system for multiobjective optimization problems including asymptotic vanishing damping, expanding the ideas laid out in [H. Attouch and G. Garrigos, Multiobjective optimization: an inertial approach to Pareto optima, preprint, arXiv:1506.02823, 201]. We prove existence of solutions to this system in finite dimensions and further prove that its bounded solutions converge weakly to weakly Pareto optimal points. In addition, we obtain a convergence rate of order O(t−2) for the function values measured with a merit function. This approach presents a good basis for the development of fast gradient methods for multiobjective optimization.
AU - Sonntag, Konstantin
AU - Peitz, Sebastian
ID - 32447
T2 - arXiv:2307.00975
TI - Fast Convergence of Inertial Multiobjective Gradient-like Systems with Asymptotic Vanishing Damping
ER -
TY - JOUR
AB - We derive efficient algorithms to compute weakly Pareto optimal solutions for smooth, convex and unconstrained multiobjective optimization problems in general Hilbert spaces. To this end, we define a novel inertial gradient-like dynamical system in the multiobjective setting, which trajectories converge weakly to Pareto optimal solutions. Discretization of this system yields an inertial multiobjective algorithm which generates sequences that converge weakly to Pareto optimal solutions. We employ Nesterov acceleration to define an algorithm with an improved convergence rate compared to the plain multiobjective steepest descent method (Algorithm 1). A further improvement in terms of efficiency is achieved by avoiding the solution of a quadratic subproblem to compute a common step direction for all objective functions, which is usually required in first-order methods. Using a different discretization of our inertial gradient-like dynamical system, we obtain an accelerated multiobjective gradient method that does not require the solution of a subproblem in each step (Algorithm 2). While this algorithm does not converge in general, it yields good results on test problems while being faster than standard steepest descent.
AU - Sonntag, Konstantin
AU - Peitz, Sebastian
ID - 46019
JF - Journal of Optimization Theory and Applications
TI - Fast Multiobjective Gradient Methods with Nesterov Acceleration via Inertial Gradient-Like Systems
ER -
TY - CHAP
AB - In the development of model predictive controllers for PDE-constrained problems, the use of reduced order models is essential to enable real-time applicability. Besides local linearization approaches, proper orthogonal decomposition (POD) has been most widely used in the past in order to derive such models. Due to the huge advances concerning both theory as well as the numerical approximation, a very promising alternative based on the Koopman operator has recently emerged. In this chapter, we present two control strategies for model predictive control of nonlinear PDEs using data-efficient approximations of the Koopman operator. In the first one, the dynamic control system is replaced by a small number of autonomous systems with different yet constant inputs. The control problem is consequently transformed into a switching problem. In the second approach, a bilinear surrogate model is obtained via a convex combination of these autonomous systems. Using a recent convergence result for extended dynamic mode decomposition (EDMD), convergence of the reduced objective function can be shown. We study the properties of these two strategies with respect to solution quality, data requirements, and complexity of the resulting optimization problem using the 1-dimensional Burgers equation and the 2-dimensional Navier–Stokes equations as examples. Finally, an extension for online adaptivity is presented.
AU - Peitz, Sebastian
AU - Klus, Stefan
ID - 16289
SN - 0170-8643
T2 - Lecture Notes in Control and Information Sciences
TI - Feedback Control of Nonlinear PDEs Using Data-Efficient Reduced Order Models Based on the Koopman Operator
VL - 484
ER -
TY - JOUR
AU - Dellnitz, M.
ID - 16556
JF - IMA Journal of Numerical Analysis
SN - 0272-4979
TI - Finding zeros by multilevel subdivision techniques
ER -
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
AB - The Koopman operator has become an essential tool for data-driven approximation of dynamical (control) systems in recent years, e.g., via extended dynamic mode decomposition. Despite its popularity, convergence results and, in particular, error bounds are still quite scarce. In this paper, we derive probabilistic bounds for the approximation error and the prediction error depending on the number of training data points; for both ordinary and stochastic differential equations. Moreover, we extend our analysis to nonlinear control-affine systems using either ergodic trajectories or i.i.d.
samples. Here, we exploit the linearity of the Koopman generator to obtain a bilinear system and, thus, circumvent the curse of dimensionality since we do not autonomize the system by augmenting the state by the control inputs. To the
best of our knowledge, this is the first finite-data error analysis in the stochastic and/or control setting. Finally, we demonstrate the effectiveness of the proposed approach by comparing it with state-of-the-art techniques showing its superiority whenever state and control are coupled.
AU - Nüske, Feliks
AU - Peitz, Sebastian
AU - Philipp, Friedrich
AU - Schaller, Manuel
AU - Worthmann, Karl
ID - 23428
JF - Journal of Nonlinear Science
TI - Finite-data error bounds for Koopman-based prediction and control
VL - 33
ER -
TY - JOUR
AU - Dellnitz, M
AU - Melbourne, I
AU - Marsden, J E
ID - 16548
JF - Nonlinearity
SN - 0951-7715
TI - Generic bifurcation of Hamiltonian vector fields with symmetry
ER -
TY - CHAP
AU - Dellnitz, Michael
AU - Marsden, Jerrold E.
AU - Melbourne, Ian
AU - Scheurle, Jürgen
ID - 16547
SN - 9783034875387
T2 - Bifurcation and Symmetry
TI - Generic Bifurcations of Pendula
ER -
TY - JOUR
AU - Dellnitz, Michael
AU - Melbourne, Ian
ID - 16541
JF - Journal of Computational and Applied Mathematics
SN - 0377-0427
TI - Generic movement of eigenvalues for equivariant self-adjoint matrices
ER -
TY - JOUR
AU - Grüne, L.
AU - Junge, O.
ID - 16612
JF - Journal of Optimization Theory and Applications
SN - 0022-3239
TI - Global Optimal Control of Perturbed Systems
ER -
TY - JOUR
AU - Chaudhuri, I.
AU - Sertl, S.
AU - Hajnal, Z.
AU - Dellnitz, M.
AU - Frauenheim, Th.
ID - 16500
JF - Applied Surface Science
SN - 0169-4332
TI - Global optimization of silicon nanoclusters
ER -
TY - JOUR
AU - Sertl, Stefan
AU - Dellnitz, Michael
ID - 16671
JF - Journal of Global Optimization
SN - 0925-5001
TI - Global Optimization using a Dynamical Systems Approach
ER -
TY - CONF
AB - In this article we develop a gradient-based algorithm for the solution of multiobjective optimization problems with uncertainties. To this end, an additional condition is derived for the descent direction in order to account for inaccuracies in the gradients and then incorporated into a subdivision algorithm for the computation of global solutions to multiobjective optimization problems. Convergence to a superset of the Pareto set is proved and an upper bound for the maximal distance to the set of substationary points is given. Besides the applicability to problems with uncertainties, the algorithm is developed with the intention to use it in combination with model order reduction techniques in order to efficiently solve PDE-constrained multiobjective optimization problems.
AU - Peitz, Sebastian
AU - Dellnitz, Michael
ID - 8752
SN - 1860-949X
T2 - NEO 2016
TI - Gradient-Based Multiobjective Optimization with Uncertainties
ER -
TY - CHAP
AU - Dellnitz, Michael
AU - Molo, Mirko Hessel-von
AU - Metzner, Philipp
AU - Preis, Robert
AU - Schütte, Christof
ID - 16559
SN - 9783540356561
T2 - Analysis, Modeling and Simulation of Multiscale Problems
TI - Graph Algorithms for Dynamical Systems
ER -
TY - JOUR
AU - Ringkamp, Maik
AU - Ober-Blöbaum, Sina
AU - Dellnitz, Michael
AU - Schütze, Oliver
ID - 16659
JF - Engineering Optimization
SN - 0305-215X
TI - Handling high-dimensional problems with multi-objective continuation methods via successive approximation of the tangent space
ER -