Access the full text.
Sign up today, get DeepDyve free for 14 days.
N. Nedialkov (2006)
Interval Tools for ODEs and DAEs12th GAMM - IMACS International Symposium on Scientific Computing, Computer Arithmetic and Validated Numerics (SCAN 2006)
Mareile Freihold, E. Hofer (2009)
Derivation of Physically Motivated Constraints for Efficient Interval Simulations Applied to the Analysis of Uncertain Dynamical Systems, 19
E. Auer, A. Rauh, E. Hofer, W. Luther (2008)
Validated Modeling of Mechanical Systems with SmartMOBILE: Improvement of Performance by ValEncIA-IVP
A. Griewank, A. Walther (2000)
Evaluating derivatives - principles and techniques of algorithmic differentiation, Second Edition, 19
R. Krawczyk (1969)
Newton-Algorithmen zur Bestimmung von Nullstellen mit FehlerschrankenComputing, 4
N. Nedialkov, J. Pryce (2008)
Solving differential algebraic equations by Taylor Series(III): the DAETS Code, 3
K. Makino, M. Berz (1997)
COSY INFINITY version 7Proceedings of the 1997 Particle Accelerator Conference (Cat. No.97CH36167), 2
A. Rauh, J. Minisini, E. Hofer (2009)
Verification Techniques for Sensitivity Analysis and Design of Controllers for Nonlinear Dynamic Systems with Uncertainties, 19
H. Aschemann, J. Minisini, A. Rauh (2010)
Interval arithmetic techniques for the design of controllers for nonlinear dynamical systems with applications in mechatronics. IIJournal of Computer and Systems Sciences International, 49
A. Rauh, J. Minisini, E. Hofer, H. Aschemann (2011)
Robust and Optimal Control of Uncertain Dynamical Systems with State-Dependent Switchings Using Interval ArithmeticReliab. Comput., 15
A. Rauh, V. Grigoryev, H. Aschemann, M. Paschen (2010)
Incremental Gain Scheduling and Sensitivity-Based Control for Underactuated ShipsIFAC Proceedings Volumes, 43
A. Rauh, J. Minisini, E. Hofer (2009)
Towards the Development of an Interval Arithmetic Environment for Validated Computer-Aided Design and Verification of Systems in Control Engineering
M. Kletting, A. Rauh, E. Hofer, H. Aschemann (2006)
Interval Observer Design Based on Taylor Models for Nonlinear Uncertain Continuous-Time Systems12th GAMM - IMACS International Symposium on Scientific Computing, Computer Arithmetic and Validated Numerics (SCAN 2006)
A. Rauh, M. Brill, Clemens Günther (2009)
A Novel Interval Arithmetic Approach for Solving Differential-Algebraic Equations with ValEncIA-IVP, 19
L. Jaulin, M. Kieffer, Olivier Didrit, Éric Walter (2001)
Applied Interval Analysis
A. Rauh, H. Aschemann (2010)
Sensitivity-based feedforward and feedback control using algorithmic differentiation2010 15th International Conference on Methods and Models in Automation and Robotics
A. Rauh, E. Hofer (2009)
Interval Methods for Optimal Control
N. Nedialkov, J. Pryce (2005)
Solving Differential-Algebraic Equations by Taylor Series (I): Computing Taylor CoefficientsBIT Numerical Mathematics, 45
M. Fliess, J. Lévine, Philippe Martin, P. Rouchon (1995)
Flatness and defect of non-linear systems: introductory theory and examplesInternational Journal of Control, 61
A. Rauh, E. Auer, Mareile Freihold, E. Hofer, H. Aschemann (2011)
Detection and Reduction of Overestimation in Guaranteed Simulations of Hamiltonian SystemsReliab. Comput., 15
Youdong Lin, M. Stadtherr (2006)
Validated Solution of Initial Value Problems for ODEs with Interval Parameters
A. Isidori (1985)
Nonlinear Control Systems
J. Rokne (1992)
Interval Arithmetic
[The task of designing feedforward control strategies for finite-dimensional systems in such a way that the output variables match predefined trajectories is a common goal in control engineering. Besides the widely used formulation of the corresponding system models as explicit sets of ordinary differential equations, differential-algebraic representations allow for a unified treatment of both system analysis and synthesis. For modeling and analysis of many real-life dynamic processes, differential-algebraic equations are a natural description to take into account interconnections between different physical components. Each component of such interconnected systems is described by a separate dynamic model, for instance the electric drive and the mechanical components in power trains.Moreover, side conditions are required to connect these component models by a description of power flow or, for example, geometric constraints imposed by links and joints. During system synthesis, control design tasks can be formulated in terms of initial value problems for sets of differential-algebraic equations. To check solvability, verified and nonverified algorithms are applicable which analyze the underlying system structures. The same holds for the reconstruction of internal variables and parameters on the basis of measured data. In this contribution, constructive approaches are discussed for solving both the control and estimator design using differential-algebraic formulations. It is demonstrated how these approaches can be used to show controllability and observability of dynamical systems. Numerical results for two applications conclude this paper.]
Published: May 13, 2011
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.