- Tytuł:
- Human-induced force reconstruction using a non-linear electrodynamic shaker applying an iterative neural network algorithm
- Autorzy:
-
Peláez-Rodríguez, César
Magdaleno, Álvaro
Salcedo-Sanz, Sancho
Lorenzana, Antolín - Powiązania:
- https://bibliotekanauki.pl/articles/27311419.pdf
- Data publikacji:
- 2023
- Wydawca:
- Polska Akademia Nauk. Czasopisma i Monografie PAN
- Tematy:
-
ground reaction forces
electrodynamic shaker
artificial neural networks
forces reconstruction
iterative neural network
siły reakcji podłoża
wstrząsarka elektrodynamiczna
sztuczne sieci neuronowe
rekonstrukcja sił
sieć neuronowa iteracyjna - Opis:
- An iterative neural network framework is proposed in this paper for the human-induced Ground Reaction Forces (GRF) replication with an inertial electrodynamic mass actuator (APS 400). This is a first approach to the systematization of dynamic load tests on structures in a purely objective, repeatable and pedestrian-independent basis. Therefore, an inversion-free offline algorithm based on Machine Learning techniques has been applied for the first time on an electrodynamic shaker, without requiring its inverse model to tackle the inverse problem of successful force reconstruction. The proposed approach aims to obtain the optimal drive signal to minimize the error between the experimental shaker output and the reference force signal, measured with a pair of instrumented insoles (Loadsol©) for human bouncing at different fre- quencies and amplitudes. The optimal performance, stability and convergence of the system are verified through experimental tests, achieving excellent results in both time and frequency domain.
- Źródło:
-
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2023, 71, 3; art. no. e144615
0239-7528 - Pojawia się w:
- Bulletin of the Polish Academy of Sciences. Technical Sciences
- Dostawca treści:
- Biblioteka Nauki
Artykuł