Feedback Error Learning Neural Network for Above-Knee Prosthesis

Kalanovic, V.D.; Skaug, Nils

doi:10.1016/s1474-6670(17)43592-0

Cited by 3 publications

(3 citation statements)

References 8 publications

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“…Position feedback is measured in some robotic arms using sensors (Mitrovic et al, 2010). In devices without sensor-based feedback of real-time localization, effective prediction-correction schemes may be needed (Kalanovic et al, 2000). Although a major humanitarian necessity, the major challenges faced when designing a low cost prosthetic devices include the economic cost for research and development, local availability of components, device functionality, prediction of time of failure, design simplicity (D'Apuzzo et al, 2012).…”

Section: Implementation Issues For Low Cost Prosthetic Devicesmentioning

confidence: 99%

Neural Control using EEG as a BCI Technique for Low Cost Prosthetic Arms

Diwakar

Bodda

Nutakki

et al. 2014

Proceedings of the International Conference on Neural Computation Theory and Applications

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There have been significant advancements in brain computer interface (BCI) techniques using EEG-like methods. EEG can serve as non-invasive BMI technique, to control devices like wheelchairs, cursors and robotic arm. In this paper, we discuss the use of EEG recordings to control low-cost robotic arms by extracting motor task patterns and indicate where such control algorithms may show promise towards the humanitarian challenge. Studies have shown robotic arm movement solutions using kinematics and machine learning methods. With iterative processes for trajectory making, EEG signals have been known to be used to control robotic arms. The paper also showcases a case-study developed towards this challenge in order to test such algorithmic approaches. Non-traditional approaches using neuro-inspired processing techniques without implicit kinematics have also shown potential applications. Use of EEG to resolve temporal information may, indeed, help understand movement coordination in robotic arm.

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Section: Implementation Issues For Low Cost Prosthetic Devicesmentioning

confidence: 99%

Neural Control using EEG as a BCI Technique for Low Cost Prosthetic Arms

Diwakar

Bodda

Nutakki

et al. 2014

Proceedings of the International Conference on Neural Computation Theory and Applications

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“…In [21] the inputs from the EMG are passed through a dynamic recurrent neural network (DRNN) to control all three sections of a virtual limb on a computer based simulation. A real control system is discussed in [22] where the authors have studied the intricacies of walking at length. They use a feed forward neural network to overcome the limitations of rule based control systems which are unable to take account of changing demands and terrain.…”

Section: Controlling a Limb Or Prosthesismentioning

confidence: 99%

Multiple sensor outputs and computational intelligence towards estimating state and speed for control of lower limb prostheses

Hardaker

Passow

Elizondo

2014

2014 14th UK Workshop on Computational Intelligence (UKCI)

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Abstract-For as long as people have been able to survive limb threatening injuries prostheses have been created. Modern lower limb prostheses are primarily controlled by adjusting the amount of damping in the knee to bend in a suitable manner for walking and running. Often the choice of walking state or running state has to be controlled manually by pressing a button. This paper examines how this control could be improved using sensors attached tofa the limbs of two volunteers. The signals from the sensors had features extracted which were passed through a computational intelligence system. The system was used to determine whether the volunteer was walking or running and their movement speed. Two new features are presented which identify the movement states of standing, walking and running and the movement speed of the volunteer. The results suggest that the control of the prosthetic limb could be improved.

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“…A simplified FEL architecture without the internal forward model has been extensively studied for robot control (Kawato et al, 1988;Tolu et al, 2012;Gomi et al, 1993;Hamavand et al, 1995;Talebi et al, 1998;Topalov et al, 1998;Teshnehlab et al, 1996;Kalanovic et al, 2000;Kurosawa et al, 2005;Neto et al, 2010;Jo et al, 2011). However, stability guarantee relies on a precondition that the controlled plant can be stabilized by linear feedback without feedforward control in Tolu et al (2012); Kawato et al (1988); Teshnehlab et al (1996); Kalanovic et (2000); Kurosawa et al (2005); Neto et al (2010); Jo et al (2011), which may not be satisfied for many control problems such as robot tracking control; in Gomi et al (1993); Hamavand et al (1995); Talebi et al (1998); Topalov et al (1998), internal inverse models are implemented in the feedback loops, which violates the original motivation of proposing FEL.…”

Section: Introductionmentioning

confidence: 99%

Bioinspired composite learning control under discontinuous friction for industrial robots

Guo¹,

Sun²,

Darouach³

2022

Preprint

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Adaptive control can be applied to robotic systems with parameter uncertainties, but improving its performance is usually difficult, especially under discontinuous friction. Inspired by the human motor learning control mechanism, an adaptive learning control approach is proposed for a broad class of robotic systems with discontinuous friction, where a composite error learning technique that exploits data memory is employed to enhance parameter estimation. Compared with the classical feedback error learning control, the proposed approach can achieve superior transient and steady-state tracking without high-gain feedback and persistent excitation at the cost of extra computational burden and memory usage. The performance improvement of the proposed approach has been verified by experiments based on a DENSO industrial robot.

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Feedback Error Learning Neural Network for Above-Knee Prosthesis

Cited by 3 publications

References 8 publications

Neural Control using EEG as a BCI Technique for Low Cost Prosthetic Arms

Neural Control using EEG as a BCI Technique for Low Cost Prosthetic Arms

Multiple sensor outputs and computational intelligence towards estimating state and speed for control of lower limb prostheses

Bioinspired composite learning control under discontinuous friction for industrial robots

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