KLUEDO RSS FeedKLUEDO Dokumente/documents
https://kluedo.ub.uni-kl.de/index/index/
Mon, 24 Jan 2011 08:52:09 +0100Mon, 24 Jan 2011 08:52:09 +0100Optimal control methods for the calculation of invariant excitation signals for multibody systems
https://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/2273
Input signals are needed for the numerical simulation of vehicle multibody systems. With these input data, the equations of motion can be integrated numerically and some output quantities can be calculated from the simulation results. In this work we consider the corresponding inverse problem: We assume that some reference output signals are available, typically gained by measurement and focus on the task to derive the input signals that produce the desired reference output in a suitable sense. If the input data is invariant, i.e., independent of the specific system, it can be transferred and used to excite other system variants. This problem can be formulated as optimal control problem. We discuss solution approaches from optimal control theory, their applicability to this special problem class and give some simulation results.M. Burger; M. Speckert; K. Dreßlerreporthttps://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/2273Mon, 24 Jan 2011 08:52:09 +0100Calculating invariant loads for system simulation in vehicle engineering
https://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/2094
For the numerical simulation of a mechanical multibody system (MBS), dynamical loads are needed as input data, such as a road profile. With given input quantities, the equations of motion of the system can be integrated. Output quantities for further investigations are calculated from the integration results. In this paper, we consider the corresponding inverse problem: We assume, that a dynamical system and some reference output signals are given. The general task is to derive an input signal, such that the system simulation produces the desired reference output. We present the state-of-the-art method in industrial applications, the iterative learning control method (ILC) and give an application example from automotive industry. Then, we discuss three alternative methods based on optimal control theory for differential algebraic equations (DAEs) and give an overview of their general scheme.M. Burger; K. Dreßler; A. Marquardt; M. Speckertreporthttps://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/2094Wed, 20 May 2009 14:51:44 +0200