User:Hekler
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Achim Hekler
 | Dipl.-Inform. Research Assistant, Ph.D. Student |
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| Address: | Karlsruher Institut für Technologie
Institut für Anthropomatik Gebäude 07.21 Raum A 1.6 Haid-und-Neu-Str. 7 D-76131 Karlsruhe |
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| Walk-in hours: | on arrangement | |
| Phone: | +49-721-608-46296 | |
| E-mail: | Achim.Hekler@kit.edu | |
Academic Career
| since 01/09 | Research Assistant at the Intelligent Sensor-Actuator-Systems Lab, Department of Computer Science, University of Karlsruhe (TH). |
| 10/08-12/08 | Research Assistant at the Institute for Biomedical Engineering, Department of Electrical Engineering and Information Technology, University of Karlsruhe (TH). |
Research Interests
Publications
Jörg Fischer, Achim Hekler, Uwe D. Hanebeck,
State Estimation in Packet-Based Networked Control Systems (preliminary title),
- Proceedings of the 15th International Conference on Information Fusion (Fusion 2012) (to appear), Singapore, July, 2012.
Author : Jörg Fischer, Achim Hekler, Uwe D. Hanebeck
Title : State Estimation in Packet-Based Networked Control Systems (preliminary title)
In : Proceedings of the 15th International Conference on Information Fusion (Fusion 2012) (to appear)
Date : July 2012
Optimal Stochastic Open-Loop Feedback Control over Unreliable Networks (preliminary title),
- Proceedings of the 15th International Conference on Information Fusion (Fusion 2012) (to appear), Singapore, July, 2012.
- Author : Achim Hekler, Jörg Fischer, Uwe D. Hanebeck
Title : Optimal Stochastic Open-Loop Feedback Control over Unreliable Networks (preliminary title)
In : Proceedings of the 15th International Conference on Information Fusion (Fusion 2012) (to appear)
Date : July 2012
Efficient Open-Loop Feedback Control of Nonlinear Stochastic Systems Based on Deterministic Dirac Mixture Densities,
- Proceedings of the 2012 American Control Conference (ACC 2012) (to appear), Montréal, Canada, June, 2012.
- Author : Achim Hekler, Christof Chlebek, Uwe D. Hanebeck
Title : Efficient Open-Loop Feedback Control of Nonlinear Stochastic Systems Based on Deterministic Dirac Mixture Densities
In : Proceedings of the 2012 American Control Conference (ACC 2012) (to appear)
Date : June 2012
Stochastic Nonlinear Model Predictive Control with Guaranteed Error Bounds Using Compactly Supported Wavelets,
- Proceedings of the 50th IEEE Conference on Decision and Control (CDC 2011), Orlando, Florida, USA, December, 2011.
Author : Achim Hekler, Martin Kiefel, Uwe D. HanebeckAbstract
Title : Stochastic Nonlinear Model Predictive Control with Guaranteed Error Bounds Using Compactly Supported Wavelets
In : Proceedings of the 50th IEEE Conference on Decision and Control (CDC 2011)
Date : December 2011In model predictive control, a high quality of control can only be achieved,
if the model of the system reflects the real-world process as precisely as possible.
Therefore, the controller should be capable of both handling a nonlinear system description and
systematically incorporating uncertainties affecting the system. Since stochastic nonlinear model predictive control (SNMPC) problems
in general cannot be solved in closed form, either the system model or the occurring densities have to be approximated.
In this paper, we present an SNMPC framework, which approximates the densities and the reward function by their wavelet expansions.
Due to the few requirements on the shape and family of the densities or reward function, the presented technique can be applied to a large class
of SNMPC problems. For accelerating the optimization, we additionally present a novel thresholding technique, the so-called dynamic thresholding,
which neglects coefficients that are insignificant, while at the same time guaranteeing that the optimal control input is still chosen.
The capabilities of the proposed approach are demonstrated by simulations with a path planning scenario.
Nonlinear Bayesian Estimation with Compactly Supported Wavelets,
- Proceedings of the 2010 IEEE Conference on Decision and Control (CDC 2010), Atlanta, Georgia, USA, December, 2010.
- Author : Achim Hekler, Martin Kiefel, Uwe D. HanebeckAbstract
Title : Nonlinear Bayesian Estimation with Compactly Supported Wavelets
In : Proceedings of the 2010 IEEE Conference on Decision and Control (CDC 2010)
Date : December 2010Bayesian estimation for nonlinear systems is still a challenging problem, as
in general the type of the true probability density changes and the
complexity increases over time. Hence, approximations of the occurring
equations and/or of the underlying probability density functions are
inevitable. In this paper, we propose an approximation of the conditional
densities by wavelet expansions. This kind of representation allows a sparse
set of characterizing coefficients, especially for smooth or piecewise
smooth density functions. Besides its good approximation properties, fast
algorithms operating on sparse vectors are applicable and thus, a good
trade-off between approximation quality and run-time can be achieved.
Moreover, due to its highly generic nature, it can be applied to a large
class of nonlinear systems with a high modeling accuracy. In particular, the
noise acting upon the system can be modeled by an arbitrary probability
distribution and can influence the system in any way.
Robust Model Predictive Control Incorporating Least Favorable Measurements,
- Proceedings of the 2010 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2010), Salt Lake City, Utah, USA, September, 2010.
- Author : Daniel Lyons, Achim Hekler, Markus Kuderer, Uwe D. HanebeckAbstract
Title : Robust Model Predictive Control Incorporating Least Favorable Measurements
In : Proceedings of the 2010 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2010)
Date : September 2010Closed-loop model predictive control of nonlinear systems,Nominee Best Paper Award
whose internal states are not completely accessible, incorporates
the impact of possible future measurements into the planning
process. When planning ahead in time, those measurements
are not known, so the closed-loop controller accounts for
the expected impact of all potential measurements. We propose a novel
conservative closed-loop control approach that does not calculate the
expected impact of all measurements, but solely considers the single
future measurement that has the worst impact on the control objective.
In doing so, the model predictive controller guarantees robustness
even in the face of high disturbances acting upon the system. Moreover,
by considering only a single dedicated measurement, the complexity of
closed-loop control is reduced significantly. The capabilities of our
approach are evaluated by means of a path planning problem for a mobile robot.
Nonlinear Model Predictive Control Considering Stochastic and Systematic Uncertainties with Sets of Densities,
- Proceedings of the IEEE Multi-Conference on Systems and Control (MSC 2010), Yokohama, Japan, September, 2010.
- Author : Achim Hekler, Daniel Lyons, Benjamin Noack, Uwe D. HanebeckAbstract
Title : Nonlinear Model Predictive Control Considering Stochastic and Systematic Uncertainties with Sets of Densities
In : Proceedings of the IEEE Multi-Conference on Systems and Control (MSC 2010)
Date : September 2010In Model Predictive Control, the quality of control
is highly dependent upon the model of the system under control.
Therefore, a precise deterministic model is desirable. However,
in real-world applications, modeling accuracy is typically limited
and systems are generally affected by disturbances. Hence,
it is important to systematically consider these uncertainties
and to model them correctly. In this paper, we present a
novel Nonlinear Model Predictive Control method for systems
affected by two different types of perturbations that are
modeled as being either stochastic or unknown but bounded
quantities. We derive a formal generalization of the Nonlinear
Model Predictive Control principle for considering both types
of uncertainties simultaneously, which is achieved by using
sets of probability densities. In doing so, a more robust and
reliable control is obtained. The capabilities and benefits of
our approach are demonstrated in real-world experiments with
miniature walking robots.
Maße für Wahrscheinlichkeitsdichten in der informationstheoretischen Sensoreinsatzplanung,
- Verteilte Messsysteme, pp. 121-132, KIT Scientific Publishing, March, 2010.
- URL Author : Daniel Lyons, Achim Hekler, Benjamin Noack, Uwe D. HanebeckAbstract
Title : Maße für Wahrscheinlichkeitsdichten in der informationstheoretischen Sensoreinsatzplanung
In : Verteilte Messsysteme
Date : March 2010Bei der Beobachtung eines räumlich verteilten Phänomens mit einer
Vielzahl von Sensoren ist die intelligente Auswahl von Messkonfigurationen aufgrund von
beschränkten Rechen- und Kommunikationskapazitäten entscheidend für die
Lebensdauer des Sensornetzes. Mit der Sensoreinsatzplanung kann die im nächsten
Zeitschritt anzusteuernde Messkonfiguration dynamisch mittels einer stochastischen
modell-prädiktiven Planung über einen endlichen Zeithorizont bestimmt werden.
Dabei wird als Gütekriterium die Maximierung des zu erwartenden Informationsgewinns
durch zukünftige Messungen unter sparsamer Verwendung der Energieressourcen gewählt.
In diesem Artikel wird ein neues Maß für kontinuierliche Wahrscheinlichkeitsdichten
vorgestellt, das sich kanonisch aus der Konstruktion eines Vektorraums für
Wahrscheinlichkeitsdichten ergibt. Dieses Maß wird als Gütefunktion in der
vorausschauenden Sensoreinsatzplanung zur Bewertung des informationstheoretischen Einfluß
von Messungen auf die aktuelle Zustandsschätzung verwendet.
Achim Hekler, Nicole Kikillus, and Armin Bolz
Detection of ectopic beats in single channel electrocardiograms
Proceedings of the 4th European Congress for Medical and Biological Engineering (MBEC 2008), 2008, Antwerpen, Belgium
