2,465 research outputs found
Online-Computation Approach to Optimal Control of Noise-Affected Nonlinear Systems with Continuous State and Control Spaces
© 2007 EUCA.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 decision-making 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
Bibliographic Review on Distributed Kalman Filtering
In recent years, a compelling need has arisen to understand the effects of distributed information structures on estimation and filtering. In this paper, a bibliographical review on distributed Kalman filtering (DKF) is provided.\ud
The paper contains a classification of different approaches and methods involved to DKF. The applications of DKF are also discussed and explained separately. A comparison of different approaches is briefly carried out. Focuses on the contemporary research are also addressed with emphasis on the practical applications of the techniques. An exhaustive list of publications, linked directly or indirectly to DKF in the open literature, is compiled to provide an overall picture of different developing aspects of this area
Antilope - A Lagrangian Relaxation Approach to the de novo Peptide Sequencing Problem
Peptide sequencing from mass spectrometry data is a key step in proteome
research. Especially de novo sequencing, the identification of a peptide from
its spectrum alone, is still a challenge even for state-of-the-art algorithmic
approaches. In this paper we present Antilope, a new fast and flexible approach
based on mathematical programming. It builds on the spectrum graph model and
works with a variety of scoring schemes. Antilope combines Lagrangian
relaxation for solving an integer linear programming formulation with an
adaptation of Yen's k shortest paths algorithm. It shows a significant
improvement in running time compared to mixed integer optimization and performs
at the same speed like other state-of-the-art tools. We also implemented a
generic probabilistic scoring scheme that can be trained automatically for a
dataset of annotated spectra and is independent of the mass spectrometer type.
Evaluations on benchmark data show that Antilope is competitive to the popular
state-of-the-art programs PepNovo and NovoHMM both in terms of run time and
accuracy. Furthermore, it offers increased flexibility in the number of
considered ion types. Antilope will be freely available as part of the open
source proteomics library OpenMS
Status of research at the Institute for Computer Applications in Science and Engineering (ICASE)
Research conducted at the Institute for Computer Applications in Science and Engineering in applied mathematics, numerical analysis and computer science is summarized
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