TY - JOUR
T1 - Torque and cadence tracking in functional electrical stimulation induced cycling using passivity-based spatial repetitive learning control
AU - Duenas, Victor H.
AU - Cousin, Christian A.
AU - Ghanbari, Vahideh
AU - Fox, Emily J.
AU - Dixon, Warren E.
N1 - Funding Information:
This research is supported in part by the National Science Foundation, USA Graduate Research Fellowship Program under Grant No. DGE-1315138 and AFOSR, USA award number FA9550-18-1-0109. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the sponsoring agency. The material in this paper was partially presented at the 2018 American Control Conference, June 27–29, 2018, Milwaukee, WI, USA. This paper was recommended for publication in revised form by Associate Editor Yang Shi under the direction of Editor Thomas Parisini
Funding Information:
This research is supported in part by the National Science Foundation, USA Graduate Research Fellowship Program under Grant No. DGE-1315138 and AFOSR, USA award number FA9550-18-1-0109 . Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the sponsoring agency. The material in this paper was partially presented at the 2018 American Control Conference, June 27–29, 2018, Milwaukee, WI, USA. This paper was recommended for publication in revised form by Associate Editor Yang Shi under the direction of Editor Thomas Parisini
Publisher Copyright:
© 2020 Elsevier Ltd
PY - 2020/5
Y1 - 2020/5
N2 - Due to the inherent periodic nature of cycling tasks, iterative and repetitive learning controllers are well motivated for rehabilitative cycling. Motorized functional electrical stimulation induced cycling is a rehabilitation treatment where multiple lower-limb muscle groups are activated jointly with an electric motor to achieve cycling objectives such as speed (cadence) and torque tracking. This paper examines torque tracking accomplished by the stimulation of six lower-limb muscles via a novel spatial repetitive learning control and cadence regulation by an electric motor using a sliding-mode controller. A desired torque trajectory is constructed based on the rider's kinematic efficiency, which is a function of the crank position. The learning controller takes advantage of the periodicity of the desired torque trajectory to provide a feedforward input to the stimulated muscles. A passivity-based analysis is developed to ensure stability of the torque and cadence closed-loop error systems. The muscle learning and electric motor controllers were implemented in real-time during cycling experiments on five able-bodied individuals and three participants with movement disorders. The combined average cadence tracking error was 0.01±1.20 RPM for a 50 RPM trajectory and the combined average power tracking error was 1.78±1.25 W for a peak power of 10 W.
AB - Due to the inherent periodic nature of cycling tasks, iterative and repetitive learning controllers are well motivated for rehabilitative cycling. Motorized functional electrical stimulation induced cycling is a rehabilitation treatment where multiple lower-limb muscle groups are activated jointly with an electric motor to achieve cycling objectives such as speed (cadence) and torque tracking. This paper examines torque tracking accomplished by the stimulation of six lower-limb muscles via a novel spatial repetitive learning control and cadence regulation by an electric motor using a sliding-mode controller. A desired torque trajectory is constructed based on the rider's kinematic efficiency, which is a function of the crank position. The learning controller takes advantage of the periodicity of the desired torque trajectory to provide a feedforward input to the stimulated muscles. A passivity-based analysis is developed to ensure stability of the torque and cadence closed-loop error systems. The muscle learning and electric motor controllers were implemented in real-time during cycling experiments on five able-bodied individuals and three participants with movement disorders. The combined average cadence tracking error was 0.01±1.20 RPM for a 50 RPM trajectory and the combined average power tracking error was 1.78±1.25 W for a peak power of 10 W.
KW - FES-cycling
KW - Functional Electrical Stimulation (FES)
KW - Human–machine interaction
KW - Passivity-based control
KW - Spatial repetitive learning control (RLC)
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U2 - 10.1016/j.automatica.2020.108852
DO - 10.1016/j.automatica.2020.108852
M3 - Article
AN - SCOPUS:85079035895
SN - 0005-1098
VL - 115
JO - Automatica
JF - Automatica
M1 - 108852
ER -