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Adaptive Local Model Networks with Higher Degree Polynomials
Oliver Banfer,Marlon Franke,Oliver Nelles 제어로봇시스템학회 2010 제어로봇시스템학회 국제학술대회 논문집 Vol.2010 No.10
A new adaptation method for local model networks with higher degree polynomials which are trained by the polynomial model tree (POLYMOT) algorithm is presented in this paper. Usually the local models are linearly parameterized and those parameters are typically adapted by a recursive least squares approach. For the utilization of higher degree polynomials a subset selection method, which is a part of the POLYMOT algorithm, selects and estimates the most significant parameters from a huge parameter matrix. This matrix contains one parameter wi,nx for each input u<SUP>l</SUP>p up to the maximal polynomial degree and for all the combinations of the inputs. It is created during the training procedure of the local model network. For the online adaptation of the trained local model network only the selected parameters should be used. Otherwise the local model network would be too flexible and the idea of subset selection would be lost. Therefore the presented adaptation method generates at first a new parameter matrix with the selected and most significant parameters which are unequal to zero. Afterwards the local model parameters can be adapted with the aid of a standard recursive least squares method.
A comparison of DAE integrators in the context of benchmark problems for flexible multibody dynamics
Peter Betsch,Christian Becker,Marlon Franke,Yinping Yang,Alexander Janz 대한기계학회 2015 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.29 No.7
In the present work a uniform framework for general flexible multibody dynamics is used to compare state-of-the-art DAE integratorsin the context of benchmark problems. The multibody systems considered herein are comprised of rigid bodies, nonlinear beams andshells. The constitutive laws applied in the benchmark problems belong to the class of hyperelastic materials. To numerically integratethe uniform set of DAEs three alternative time-stepping schemes are applied: (i) an energy-momentum consistent method, (ii) a specificvariational integrator and (iii) a generalized-α scheme.