Computer Simulation Tests of Feedback Error Learning Controller with IDM and ISM for Functional Electrical Stimulation in Wrist Joint Control

Abstract

Feedforward controller would be useful for hybrid Functional Electrical Stimulation (FES) system using powered orthotic devices. In this paper, Feedback Error Learning (FEL) controller for FES (FEL-FES controller) was examined using an inverse statics model (ISM) with an inverse dynamics model (IDM) to realize a feedforward FES controller. For FES application, the ISM was tested in learning off line using training data obtained by PID control of very slow movements. Computer simulation tests in controlling wrist joint movements showed that the ISM performed properly in positioning task and that IDM learning was improved by using the ISM showing increase of output power ratio of the feedforward controller. The simple ISM learning method and the FEL-FES controller using the ISM would be useful in controlling the musculoskeletal system that has nonlinear characteristics to electrical stimulation and therefore is expected to be useful in applying to hybrid FES system using powered orthotic device.

Bridging the sim2real gap. Investigating deviations between experimental motion measurements and musculoskeletal simulation results—a systematic review

“…Certain methods primarily target a different goal than generating simulation results without residuals. The goal can be the estimation of most accurate muscle forces or torques ( Chao and Rim, 1973 ; Koh and Jennings, 2003 ; Seth and Pandy, 2007 ; Morrow et al, 2014 ; Lv et al, 2016 ; Bélaise et al, 2018a ; Bélaise et al, 2018b ; Stanev and Moustakas, 2018 ; Moissenet et al, 2019 ; Bailly et al, 2021 ; Ceglia et al, 2023 ), the determination of joint torques or muscle excitations needed to generate desired movement ( Kaplan and Heegaard, 2001 ; Thelen et al, 2003 ; Thelen and Anderson, 2006 ; Da Silva et al, 2008 ; Ghafari et al, 2009 ; Demircan et al, 2010 ; Watanabe and Sugi, 2010 ; Lin et al, 2018 ; Mouzo et al, 2018 ; Dorschky et al, 2019 ; Haralabidis et al, 2021 ; Wang et al, 2021 ; Wang et al, 2022 ) or the development of controllers to determine muscle activity that track desired movement ( Yamaguchi and Zajac, 1990 ; Blana et al, 2009 ; Arash Haghpanah et al, 2022 ). Handling the sim2real gap is then a secondary achievement.…”

Robust Hybrid Controller Design to Stabilise an Underactuated Robot Vehicle under Various Input

“…Te PID-PD control parameters are tuned progressively until the system stabilise [36]. Te FLC with two inputs includes error and change of error with one output used to describe a fuzzy inference system and then create the fuzzy rules [37,38].…”

DataSheet1.PDF

“…Certain methods primarily target a different goal than generating simulation results without residuals. The goal can be the estimation of most accurate muscle forces or torques (Chao and Rim, 1973;Koh and Jennings, 2003;Seth and Pandy, 2007;Morrow et al, 2014;Lv et al, 2016;Bélaise et al, 2018a;Bélaise et al, 2018b;Stanev and Moustakas, 2018;Moissenet et al, 2019;Bailly et al, 2021;Ceglia et al, 2023), the determination of joint torques or muscle excitations needed to generate desired movement (Kaplan and Heegaard, 2001;Thelen et al, 2003;Thelen and Anderson, 2006;Da Silva et al, 2008;Ghafari et al, 2009;Demircan et al, 2010;Watanabe and Sugi, 2010;Lin et al, 2018;Mouzo et al, 2018;Dorschky et al, 2019;Haralabidis et al, 2021;Wang et al, 2021;Wang et al, 2022) or the development of controllers to determine muscle activity that track desired movement (Yamaguchi and Zajac, 1990;Blana et al, 2009;Arash Haghpanah et al, 2022). Handling the sim2real gap is then a secondary achievement.…”