Improved Humanoid Gait Using Learning-Based Analysis of a New Wearable 3D Force System: Work Programme

Almeida, Luís; Santos, Vítor; Ferreira, José Ribeiro

doi:10.23919/cisti.2019.8760604

Cited by 1 publication

(1 citation statement)

References 22 publications

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“…* e-mail: cesar.pelaez@uah.es First of all, adaptive control techniques have been extensively applied in earthquake simulations by means of shaking tables [7,8,9,10]. These adaptive techniques have also been explored in the field of humanoid robots [11,12,13,14,15]. Another classical alternative consists of using an iterative learning control system (ILC) [16,17].…”

Section: Introductionmentioning

confidence: 99%

Human-induced force reconstruction using a non-linear electrodynamic shaker applying an iterative neural network algorithm

Peláez-Rodríguez,

Magdaleno,

Salcedo-Sanz

et al. 2023

Bulletin of the Polish Academy of Sciences Technical Sciences

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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 frequencies 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.

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