Data-Based and Opportunistic Integral Concurrent Learning for Adaptive Trajectory Tracking During Switched FES-Induced Biceps Curls

Abstract

Hybrid exoskeletons, which combine functional electrical stimulation (FES) with a motorized testbed, can potentially improve the rehabilitation of people with movement disorders. However, hybrid exoskeletons have inherently nonlinear and uncertain dynamics, including combinations of discrete modes that switch between different continuous dynamic subsystems, which complicate closed-loop control. A particular complication is the uncertain muscle control effectiveness associated with FES. In this work, adaptive integral concurrent learning (ICL) motor and FES controllers are developed for a hybrid biceps curl exoskeleton, which are designed to achieve opportunistic and databased learning of the uncertain human and electromechanical testbed parameters. Global exponential trajectory tracking and parameter estimation errors are proven through a Lyapunovbased stability analysis. The motor effectiveness is assumed to be unknown, and, to help with fatigue reduction, FES is enabled to switch between multiple electrodes on the biceps brachii, further complicating the analysis. A consequence of switching between the different uncertain subsystems is that the parameters must be opportunistically learned for each subsystem (i.e. each electrode and the motor), while that subsystem is active. Experiments were performed to validate the developed ICL controllers on twelve healthy participants. The average (± standard deviation) position tracking errors across each participant were 1.44±5.32 deg, -0.25±2.85 deg, and -0.17±2.66 deg across biceps Curls 1-3, 4-7, and 8-10, respectively, where the average across the entire experiment was 0.28±3.53 deg.

Textile smart sensors based on a biomechanical and multi-layer perceptron hybrid method

“…Stretch sensors were attached to the maximum expansion points of the arm muscles. 29,30 The brachioradialis/flexor carpi radialis, biceps brachii, and triceps brachii muscles have three sensors attached perpendicular to the direction of the muscle fibers. The elbow joint angle was measured by attaching a stretching sensor to the elbow.…”

Editorial: Hybrid intelligent algorithms based learning, optimization, and application to autonomic control systems, volume II

“…Nowadays, endless developments have been appeared to carry out the analysis and design of dynamic systems with typical hybrid dynamic characteristics. Unfortunately, the practical applications in the different fields, such as autonomous vehicles (Zhang et al, 2021 ), rehabilitation robots (Allen et al, 2022 ), and mechatronic systems (Liu et al, 2022 ), are of lack by using the existing hybrid intelligent algorithms to a great degree. Besides, it is not always easy to embed these hybrid intelligent algorithms to the application research and actual development of industrial installations.…”

“…More recently, various intelligent methodology and hybrid control technology have been developed for autonomic control systems in the literature (see Na et al, 2019 ; Allen et al, 2022 ; Che et al, 2022 and reference therein), which also have been applied to different scenarios in practice, including aerial vehicles (Kumar and Michael, 2012 ), permanent magnet synchronous machine (Egidio et al, 2022 ), and active magnetic bearing (Che et al, 2022 ), etc. Among them, it is desired that the sophisticated hybrid intelligent algorithms can be adapted to the learning-based optimization and control design to increase the autonomous performance of equipment.…”