From WWWwikiEn<span style="display: none;">[[Image:MFI08_WeisselSchreiter.pdf]][[Image:IFAC08_WeisselHuber.pdf]][[Image:CDC07_WeisselHuber.pdf]][[Image:IROS07_WeisselHuber.pdf]][[Image:ACC07_WeisselHuber.pdf]][[Image:ECC07_DeisenrothWeissel.pdf]][[Image:ICINCO07_WeisselHuber.pdf]][[Image:MFI06_DeisenrothWeissel.pdf]][[Image:CSIT05_Weissel.pdf]]</span> <span style="display: none;">[[Image:MFI08_WeisselSchreiter.pdf]][[Image:IFAC08_WeisselHuber.pdf]][[Image:CDC07_WeisselHuber.pdf]][[Image:IROS07_WeisselHuber.pdf]][[Image:ACC07_WeisselHuber.pdf]][[Image:ECC07_DeisenrothWeissel.pdf]][[Image:ICINCO07_WeisselHuber.pdf]][[Image:MFI06_DeisenrothWeissel.pdf]][[Image:CSIT05_Weissel.pdf]]</span>

Florian Weißel

Dipl.-Ing.

Academic Career

Research Interest

• Stochastic model predictive control
• Cooperative robotics
• Nonlinear state estimation
• Information processing in sensor-actuator-networks

Publications

Informatics in Control, Automation and Robotics -- Selected Papers from the International Conference on Informatics in Control, Automation and Robotics 2007, 24:239-252, Springer, September, 2008.

URL

Author : Florian Weissel, Marco F. Huber, Uwe D. Hanebeck
Title : Stochastic Nonlinear Model Predictive Control based on Gaussian Mixture Approximations
In : Informatics in Control, Automation and Robotics -- Selected Papers from the International Conference on Informatics in Control, Automation and Robotics 2007
Date : September 2008

Abstract

In this paper, a framework for stochastic Nonlinear
Model Predictive Control (NMPC) that explicitly incorporates the
noise influence on systems with continuous state spaces is introduced.
By the incorporation of noise, which results from uncertainties during
model identification and measurement, the quality of control can be
significantly increased. Since stochastic NMPC requires the prediction
of system states over a certain horizon, an efficient state prediction
technique for nonlinear noise-affected systems is required. This is
achieved by using transition densities approximated by axis-aligned
Gaussian mixtures together with methods to reduce the computational burden.
A versatile cost function representation also employing Gaussianmixtures
provides an increased freedom of modeling. Combining the rediction technique
with this value function representation allows closed-form calculation of
the necessary optimization problems arising from stochastic NMPC. The
capabilities of the framework and especially the benefits that can be
gained by considering the noise in the controller are illustrated by the
example of a mobile robot following a given path.

Proceedings of the 2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2008), pp. 40-46, Seoul, Republic of Korea, August, 2008.

Author : Florian Weissel, Thomas Schreiter, Marco F. Huber, Uwe D. Hanebeck
Title : Stochastic Model Predictive Control of Time-Variant Nonlinear Systems with Imperfect State Information
In : Proceedings of the 2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2008)
Date : August 2008

Abstract

In many technical systems, the system state, which
is to be controlled, is not directly accessible, but has to be
estimated from observations. Furthermore, the uncertainties
arising from this procedure are typically neglected in the
controller. To remedy this deficiency, in this paper, we present a
novel approach to stochastic nonlinear model predictive control
(NMPC) for heavily noise-affected systems with not directly
accessible, i.e., hidden states, extending the stochastic NMPCframework
presented in [1]. An important property of our novel
method is that, in contrast to classical approaches, time-variant
system and measurement equations as well as time-variant step
rewards can be considered. Extending the techniques from
[1] by introducing virtual future observations and combining
this with a novel tree search algorithm, called probabilistic
branch-and-bound search (PBAB), a solution with a feasible
computational demand of the challenging problem is possible.

Proceedings of the 17th IFAC World Congress (IFAC 2008), 17, Seoul, Republic of Korea, July, 2008.

Author : Florian Weissel, Marco F. Huber, Dietrich Brunn, Uwe D. Hanebeck
Title : Stochastic Optimal Control based on Value-Function Approximation using Sinc Interpolation
In : Proceedings of the 17th IFAC World Congress (IFAC 2008)
Date : July 2008

Abstract

An effcient approach for solving stochastic optimal control problems is to employ
dynamic programming (DP). For continuous-valued nonlinear systems, the corresponding
DP recursion generally cannot be solved in closed form. Thus, a typical approach is to
discretize the DP value functions in order to be able to carry out the calculation. Especially
for multidimensional systems, either a large number of discretization points is necessary or
the quality of approximation degrades. This problem can be alleviated by interpolating the
discretized value function. In this paper, we present an approach based on optimal low-pass
interpolation employing sinc functions (sine cardinal). For the important case of systems with
Gaussian mixture noise (including the special case of Gaussian noise), we show how the
calculations required for this approach, especially the nontrivial calculation of an expected
value of a Gaussian mixture random variable transformed by a sinc function, can be carried out
analytically. We illustrate the effectiveness of the proposed interpolation scheme by an example
from the field of Stochastic Nonlinear Model Predictive Control (SNMPC).

Proceedings of the 2007 IEEE Conference on Decision and Control (CDC 2007), pp. 3661-3666, New Orleans, Louisiana, USA, December, 2007.

Author : Florian Weissel, Marco F. Huber, Uwe D. Hanebeck
Title : A Nonlinear Model Predictive Control Framework Approximating Noise Corrupted Systems with Hybrid Transition Densities
In : Proceedings of the 2007 IEEE Conference on Decision and Control (CDC 2007)
Date : December 2007

Abstract

In this paper, a framework for Nonlinear Model Predictive Control
(NMPC) for heavily noise-affected systems is presented. Within this
framework, the noise influence, which originates from uncertainties
during model identification or measurement, is explicitly considered.
This leads to a significant increase in the control quality. One
part of the proposed framework is the efficient state prediction,
which is necessary for NMPC. It is based on transition density approximation
by hybrid transition densities, which allows efficient closed-form
state prediction of time-variant nonlinear systems with continuous
state spaces in discrete time. Another part of the framework is a
versatile value function representation using Gaussian mixtures,
Dirac mixtures, and even a combination of both. Based on these methods,
an efficient closed-form algorithm for calculating an approximate
value function of the NMPC optimal control problem employing dynamic
programming is presented. Thus, also very long optimization horizons
can be used and furthermore it is possible to calculate the value
function off-line, which reduces the on-line computational burden
significantly. The capabilities of the framework and especially the
benefits that can be gained by incorporating the noise in the controller
are illustrated by the example of a miniature walking robot following
a given path.

Proceedings of the 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2007), pp. 2474-2479, San Diego, California, USA, November, 2007.

Author : Florian Weissel, Marco F. Huber, Uwe D. Hanebeck
Title : Test-Environment based on a Team of Miniature Walking Robots for Evaluation of Collaborative Control Methods
In : Proceedings of the 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2007)
Date : November 2007

Abstract

For the collaborative control of a team of robots, a set of well-suited
high-level control algorithms, especially for path planning and measurement
scheduling, is essential. The quality of these control algorithms
can be significantly increased by considering uncertainties that
arise, e.g. from noisy measurements or system model abstraction,
by incorporating stochastic filters into the control. To develop
these kinds of algorithms and to prove their effectiveness, obviously
real-world experiments with real world uncertainties are mandatory.
Therefore, a test-environment for evaluating algorithms for collaborative
control of a team of robots is presented. This test-environment is
founded on miniature walking robots with six degrees of freedom.
Their novel locomotion concept not only allows them to move in a
wide variety of different motion patterns far beyond the possibilities
of traditionally employed wheel-based robots, but also to handle
real-world conditions like uneven ground or small obstacles. These
robots are embedded in a modular test-environment, comprising infrastructure
and simulation modules as well as a high-level control module with
submodules for pose estimation, path planning, and measurement scheduling.
The interaction of the individual modules of the introduced test-environment
is illustrated by an experiment from the field of cooperative localization
with focus on measurement scheduling, where the robots that perform
distance measurements are selected based on a novel criterion, the
normalized mutual Mahalanobis distance.

Proceedings of the 2007 American Control Conference (ACC 2007), pp. 3751-3756, New York, New York, USA, July, 2007.

Author : Florian Weissel, Marco F. Huber, Uwe D. Hanebeck
Title : Efficient Control of Nonlinear Noise-Corrupted Systems Using a Novel Model Predictive Control Framework
In : Proceedings of the 2007 American Control Conference (ACC 2007)
Date : July 2007

Abstract

Model identification and measurement acquisition is always to some
degree uncertain. Therefore, a framework for Nonlinear Model Predictive
Control (NMPC) is proposed that explicitly considers the noise influence
on nonlinear dynamic systems with continuous state spaces and a finite
set of control inputs in order to significantly increase the control
quality. Integral parts of NMPC are the prediction of system states
over a finite horizon as well as the problem specific modeling of
reward functions. For achieving an efficient and also accurate state
prediction, the introduced framework uses transition densities approximated
by means of axis-aligned Gaussian mixtures. The representation power
of Gaussian mixtures is also used to model versatile reward functions.
Thus, together with the prediction technique a closed-form calculation
of the optimization problems arising from NMPC is possible. Additionally,
an efficient algorithm for calculating an approximate value function
of the corresponding optimal control problem employing dynamic programming
is presented. Thus, the value function can be calculated off-line,
which reduces the on-line computational burden significantly and
also permits the use of long optimization horizons. The capabilities
of the framework and especially the benefits that can be gained by
incorporating the noise in the controller are illustrated by the
example of a two-wheeled differential-drive mobile robot following
a given path.

Proceedings of the 2007 European Control Conference (ECC 2007), Kos, Greece, July, 2007.

Author : Marc P. Deisenroth, Florian Weissel, Toshiyuki Ohtsuka, Uwe D. Hanebeck
Title : Online-Computation Approach to Optimal Control of Noise-Affected Nonlinear Systems with Continuous State and Control Spaces
In : Proceedings of the 2007 European Control Conference (ECC 2007)
Date : July 2007

Abstract

A novel online-computation approach to optimal control of nonlinear,
noise-affected systems with continuous state and control spaces is
presented. In the proposed algorithm, system noise is explicitly
incorporated into the control decision. This leads to superior results
compared to state-of-the-art nonlinear controllers that neglect this
influence. The solution of an optimal nonlinear controller for a
corresponding deterministic system is employed to find a meaningful
state space restriction. This restriction is obtained by means of
approximate state prediction using the noisy system equation. Within
this constrained state space, an optimal closed-loop solution for
a finite decisionmaking horizon (prediction horizon) is determined
within an adaptively restricted optimization space. Interleaving
stochastic dynamic programming and value function approximation yields
a solution to the considered optimal control problem. The enhanced
performance of the proposed discrete-time controller is illustrated
by means of a scalar example system. Nonlinear model predictive control
is applied to address approximate treatment of infinite-horizon problems
by the finite-horizon controller.

Proceedings of the 4th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2007), SPSMC:62-69, Angers, France, May, 2007.

Author : Florian Weissel, Marco F. Huber, Uwe D. Hanebeck
Title : A Closed-Form Model Predictive Control Framework for Nonlinear Noise-Corrupted Systems
In : Proceedings of the 4th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2007)
Date : May 2007

Abstract

In this paper, a framework for Nonlinear Model Predictive Control
(NMPC) that explicitly incorporates the noise influence on systems
with continuous state spaces is introduced. By the incorporation
of noise, which results from uncertainties during model identification
and the measurement process, the quality of control can be significantly
increased. Since NMPC requires the prediction of system states over
a certain horizon, an efficient state prediction technique for nonlinear
noise-affected systems is required. This is achieved by using transition
densities approximated by axis-aligned Gaussian mixtures together
with methods to reduce the computational burden. A versatile cost
function representation also employing Gaussian mixtures provides
an increased freedom of modeling. Combining the prediction technique
with this value function representation allows closed-form calculation
of the necessary optimization problems arising from NMPC. The capabilities
of the framework and especially the benefits that can be gained by
considering the noise in the controller are illustrated by the example
of a mobile robot following a given path.

Proceedings of the 2006 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2006), pp. 371-376, Heidelberg, Germany, September, 2006.

Author : Marc P. Deisenroth, Toshiyuki Ohtsuka, Florian Weissel, Dietrich Brunn, Uwe D. Hanebeck
Title : Finite-Horizon Optimal State-Feedback Control of Nonlinear Stochastic Systems Based on a Minimum Principle
In : Proceedings of the 2006 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2006)
Date : September 2006

Abstract

In this paper, an approach to the finite-horizon
optimal state-feedback control problem of nonlinear, stochastic,
discrete-time systems is presented. Starting from the dynamic
equation, the value function will be approximated
by means of Taylor series expansion up to second-order
derivatives. Moreover, the problem will be reformulated, such
that a minimum principle can be applied to the stochastic
problem. Employing this minimum principle, the optimal control
problem can be rewritten as a two-point boundary-value
problem to be solved at each time step of a shrinking horizon.
To avoid numerical problems, the two-point boundary-value
problem will be solved by means of a continuation method.
Thus, the curse of dimensionality of dynamic programming
is avoided, and good candidates for the optimal state-feedback
controls are obtained. The proposed approach will be evaluated
by means of a scalar example system.

Proceedings of the 7th International Workshop on Computer Science and Information Technologies (CSIT 2005), 1, Ufa, Russian Federation, September, 2005.

Author : Florian Weissel, Uwe D. Hanebeck
Title : A Test-Environment for Control Schemes in the Field of Collaborative Robots and Swarm Intelligence
In : Proceedings of the 7th International Workshop on Computer Science and Information Technologies (CSIT 2005)
Date : September 2005

Abstract

This paper presents an architecture for a test-environment for algorithms
and control schems in the filed of collaborative robotics and swarm
intelligence. As the foundation of the test-environment, small bionically
inspired robots are presented. The robots are small (20 cm x 5 cm
x 5 cm) and lightweight (< 200g). Their design is inspired by the
movement of caterpillars. Threee cubical segments are connected via
special joints, where each of these joints has three independent
degrees of translatory freedom. Thus, the robots are able to handle
rough terrain with small obstacle. The robots are driven by innovative
piezoelectric motors that allow a gearless design without any rotary
parts. Each robot is equipped with on-board processing and radio
communication. The software of the robots is written using TinyOS,
an event-driven operating system for large-scale distributed sensor-actuator-networks.

Proceedings of the 18th International Parallel and Distributed Processing Symposium (IPDPS'04), pp. 138-143, Santa Fé, New Mexico, USA, April, 2004.

Author : Michael Huebner, Michael Ullmann, Florian Weissel, Juergen Becker
Title : Real-time Configuration Code Decompression for Dynamic FPGA Self-Reconfiguration
In : Proceedings of the 18th International Parallel and Distributed Processing Symposium (IPDPS'04)
Date : April 2004

Abstract

Xilinx Virtex FPGAs have the possibility of dynamical partial run-time
reconfiguration. If a system uses this feature with many different
configuration bitstreams for substitution of parts in reconfiguration
memory, the amout of neccesary memory increases. The sum of memory
amout which has to be provided for the configuration data is not
negligible. This fact suggests the investigation of compressing data
before they are stored in memory modules of a system.The compressed
bitstream data has to be decrompressed before transferring it to
the FPGA. This paper shows an approach of compressing configuration
data at design time and decompressing them with a hardware module
implemented on FPGA while run-time.