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

Identyfikatory

DOI

10.24425/bpasts.2023.144615

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

ground reaction forces; electrodynamic shaker; artificial neural networks; forces reconstruction; iterative neural network

PL

siły reakcji podłoża; wstrząsarka elektrodynamiczna; sztuczne sieci neuronowe; rekonstrukcja sił; sieć neuronowa iteracyjna

• Wydawca: Polska Akademia Nauk, Wydział IV Nauk Technicznych
• Czasopismo: Bulletin of the Polish Academy of Sciences. Technical Sciences
• Rocznik: 2023
• Tom: Vol. 71, nr 3
• Strony: art. no. e144615
• Opis fizyczny: Bibliogr. 34 poz., rys., tab.

Twórcy

autor

Peláez-Rodríguez César

• Department of Signal Processing and Communications, Universidad de Alcalá, Alcalá de Henares, 28805, Spain
• ITAP. Escuela de Ingenierías Industriales. Universidad de Valladolid. P.º del Cauce, 59, 47011 Valladolid, Spain

• https://orcid.org/0000-0003-1260-8112

autor

Magdaleno Álvaro

• ITAP. Escuela de Ingenierías Industriales. Universidad de Valladolid. P.º del Cauce, 59, 47011 Valladolid, Spain

autor

Salcedo-Sanz Sancho

• Department of Signal Processing and Communications, Universidad de Alcalá, Alcalá de Henares, 28805, Spain

autor

Lorenzana Antolín

• ITAP. Escuela de Ingenierías Industriales. Universidad de Valladolid. P.º del Cauce, 59, 47011 Valladolid, Spain

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).