Robust Cadence Tracking for Switched FES-Cycling With an Unknown Time-Varying Input Delay

Allen, Brendon C.; Cousin, Christian A.; Rouse, Courtney A.; Dixon, Warren E.

doi:10.1109/tcst.2021.3070189

Cited by 12 publications

(4 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In FES intervention, electrical currents are delivered to the excitable motor neurons of paralyzed muscle, in place of the central nervous system to induce muscle contractions, thereby generating force and torque [3]. Since FES can artificially induce muscle contraction, it has been widely utilized in the field of rehabilitation for various purposes, including the treatment of foot drop [4] and suppressing tremors [5], as well as assisting with cycling [6] and sit to stand [7] activities.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Hammerstein Model for FES-Induced Torque Prediction Based on Variable Forgetting Factor Recursive Least Squares Algorithm

Yang,

Li,

et al. 2024

IEEE Trans. Neural Syst. Rehabil. Eng.

View full text Add to dashboard Cite

Modeling the muscle response to functional electrical stimulation (FES) is an important step during model-based FES control system design. The Hammerstein structure is widely used in simulating this nonlinear biomechanical response. However, a fixed relationship cannot cope well with the time-varying property of muscles and muscle fatigue. In this paper, we proposed an adaptive Hammerstein model to predict ankle joint torque induced by electrical stimulation, which uses variable forgetting factor recursive least squares (VFFRLS) method to update the model parameters. To validate the proposed model, ten healthy individuals were recruited for short-duration FES experiments, ten for long-duration FES experiments, and three stroke patients for both. The isometric ankle dorsiflexion torque induced by FES was measured, and then the test performance of the fixedparameter Hammerstein model, the adaptive Hammerstein model based on fixed forgetting factor recursive least squares (FFFRLS) and the adaptive Hammerstein model based on VFFRLS were compared. The goodness of fit, root mean square error, peak error and success rate were applied to evaluate the accuracy and stability of the model. The results indicate a significant improvement in both the accuracy and stability of the proposed adaptive model compared to the fixed-parameter model and the adaptive model based on FFFRLS. The proposed adaptive model enhances the ability of the model to cope with muscle changes.

show abstract

Section: Introductionmentioning

confidence: 99%

An Adaptive Hammerstein Model for FES-Induced Torque Prediction Based on Variable Forgetting Factor Recursive Least Squares Algorithm

Yang,

Li,

et al. 2024

IEEE Trans. Neural Syst. Rehabil. Eng.

View full text Add to dashboard Cite

show abstract

“…Hence, it must be robust to the time-varying delays and the nonlinear muscle dynamics for a closed-loop FES controller to perform the desired tracking goals. In recent decades, the EMD response of muscles has been considered when developing neuromuscular electrical stimulation (NMES)/FES controller (Sharma et al, 2011 ; Iasson et al, 2013 ; Alibeji et al, 2015 ; Merad et al, 2016 ; Allen et al, 2022 ). For instance, a novel predictor-type method has been developed to address the EMD in Sharma et al ( 2011 ).…”

Section: Introductionmentioning

confidence: 99%

“…In more recent years, switched systems have received increasing attention because many practical systems can be modeled as switched systems. Motivated to improve FES-cycling performance and to complete rehabilitation tasks in different periods, a switching control strategy has been proposed in Cousin et al ( 2019 ) and Allen et al ( 2022 ). For instance, a cadence switching controller is employed to ensure that the motorized FES cycling system is globally exponentially stable (Cousin et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

“…The experiment results validate the effectiveness of the developed controller to improve the tracking performance. Similarly, a kind of robust switched controller is designed to deal with an unknown time-varying input delay in Allen et al ( 2022 ), and semi-globally exponential tracking to an ultimate bound is guaranteed. As a popular approach, Lyapunov-Krasovskii functionals have been frequently used to analyze the stability of time-delay systems, which are very effective at deriving the stability conditions for time-delay systems.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Robust tracking for functional electrical stimulation cycling with unknown time-varying input delays: A switched systems approach

Tong

Zhu²

2022

Front. Neurorobot.

View full text Add to dashboard Cite

Motorized functional electrical stimulation (FES) cycling has been demonstrated to have numerous health benefits for individuals suffering from neurological disorders. FES-cycling is usually designed to track the desired trajectories in real time. However, there are input delays between the exertion of the stimulation and the corresponding muscle contraction that potentially destabilize the system and undermine training efforts. Meanwhile, muscle fatigue gives rise to a time-varying input delay and decreased force. Moreover, switching between FES and motor control can be chattering and destabilizing owing to the high frequency. This article constructs Lyapunov-Krasovskii functionals to analyze the stability and robustness of the nonlinear cycling system with time-varying input delay. A new average dwell time condition is then provided to ensure the input-to-state stability of the considered systems. Finally, numerical simulations are illustrated to verify the effectiveness of the developed controller.

show abstract

Position and cadence tracking of a motorized FES-cycle with an unknown time-varying input delay using saturated FES control

2022

View full text Add to dashboard Cite

Robust Cadence Tracking for Switched FES-Cycling With an Unknown Time-Varying Input Delay

Cited by 12 publications

References 34 publications

An Adaptive Hammerstein Model for FES-Induced Torque Prediction Based on Variable Forgetting Factor Recursive Least Squares Algorithm

An Adaptive Hammerstein Model for FES-Induced Torque Prediction Based on Variable Forgetting Factor Recursive Least Squares Algorithm

Robust tracking for functional electrical stimulation cycling with unknown time-varying input delays: A switched systems approach

Position and cadence tracking of a motorized FES-cycle with an unknown time-varying input delay using saturated FES control

Contact Info

Product

Resources

About