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By Fabio Casciati, Roberto Rossi (auth.), Prof. Dr. Jan Holnicki-Szulc, Prof. Dr. Carlos Mota Soares (eds.)

This ebook collects invited lectures offered on the AMAS & ECCOMAS Workshop/Thematic convention SMART’03 on clever fabrics and buildings. The convention was once held in Jadwisin, Poland close to Warsaw, 2-5 September 2003. It was once geared up by way of the complicated fabrics and constructions (AMAS) Centre of Excellence on the Institute of primary Technological examine (IFTR) in Warsaw and ECCOMAS – the eu group on Computational equipment in technologies and SMART-TECH Centre at IFTR. The aim of the workshop used to be to collect and consolidate the group of clever fabrics and buildings in Europe. The workshop software was once grouped into the themes Structural keep an eye on, Vibration keep an eye on and Dynamics, harm id, and clever Materials.

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Consequently, distinct and sometimes completely independent systems must be developed for specific collision scenarios. In contrast to the standard passive systems, the proposed approach focuses on active adaptation of energy absorbing structures (equipped with a sensor J. Holnicki-Szulc et al. ), Advances in Smart Technologies in Structural Engineering © Springer-Verlag Berlin Heidelberg 2004 46 J. Holnicki-Szulc, P. Pawlowski and M. Wiklo a) Fig. 1. Testing example of truss-like cantilever. a) the initial configuration, b) the stiffest substructure, c) the most compliable substructure, d) t he stiffest substructure designed for the second load case VDM Based Dyna mic Analysis of Adaptive Structure system detecting impact in advance and controllable semi-active dissipaters, so called structural fuses) with a high ability of adaptation to extreme overloading.

To efficiently solve the optimization problem in (3), the identification is carried out in two steps. First, the parameters p 1 are idenitified from responses measured during micro-slip regime. The remaining parameters p 2 are obtained from measurements where the joint is operated in stick-slip behaviour. For both optimizations, data from several measurements with different excitations and normal forces are used. Because of the nonlinear objective function (3) a gradient-based optimization algorithm will only give local minima.

Math. 43:9-44 14. Lane JS, Ferri AA, Heck BS (1992) Vibration control using semi-active friction damping. In: Friction-induced vibration, chatter, squeal, and chaos. ASME 49:165-171 15. Hu R, Muller PC (1997) Position control of robots by nonlinearity estimation and compensation: Theory and experiements. J. Intelligent and Robotic Systems 20:195-209 16. Gaul L, Nitsche R (2000) Friction control for vibration suppression. Mechanical Systems and Signal Processing 14(2):139-150 17. Gaul L, Nitsche R (2003) Lyapunov design of damping controllers.

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