Trajectory Optimization and Model Predictive Control for Functional Electrical Stimulation-Controlled Reaching

Wolf, Derek; Schearer, Eric M.

doi:10.1109/lra.2022.3145946

Cited by 14 publications

(13 citation statements)

References 23 publications

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“…The control structure developed in this paper has since been implemented in an individual with SCI [ 21 ]. That study was published as a companion article to the current study, and it focused on the practical implementation of these methods.…”

Section: Discussionmentioning

confidence: 99%

Data-Driven Dynamic Motion Planning for Practical FES-Controlled Reaching Motions in Spinal Cord Injury

Wolf

Bogert

Schearer

2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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Functional electrical stimulation (FES) is a promising technology for restoring reaching motions to individuals with upper-limb paralysis caused by a spinal cord injury (SCI). However, the limited muscle capabilities of an individual with SCI have made achieving FES-driven reaching difficult. We developed a novel trajectory optimization method that used experimentally measured muscle capability data to find feasible reaching trajectories. In a simulation based on a real-life individual with SCI, we compared our method to attempting to follow naive direct-to-target paths. We tested our trajectory planner with three control structures that are commonly used in applied FES: feedback, feedforward-feedback, and model predictive control. Overall, trajectory optimization improved the ability to reach targets and improved the accuracy for the feedforward-feedback and model predictive controllers ( p < 0.001). The trajectory optimization method should be practically implemented to improve the FES-driven reaching performance.

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Section: Discussionmentioning

confidence: 99%

Data-Driven Dynamic Motion Planning for Practical FES-Controlled Reaching Motions in Spinal Cord Injury

Wolf

Bogert

Schearer

2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…We developed a trajectory optimization routine which accounts for the muscle capabilities of the individual and the dynamics of the arm to find feasible trajectories to a target arm configuration. We compared the performance of controlling the arm along these optimized planned trajectories compared to naive direct-totarget paths using three control structures that are commonly used in FES-driven reaching: a feedback controller [9,14], a feedforward-feedback controller [19], and a model predictive control (MPC) controller [20]. An illustration of our control framework is seen in Fig.…”

Section: Methodsmentioning

confidence: 99%

Data-driven Dynamic Motion Planning for Practical FES-Controlled Reaching Motions in Spinal Cord Injury

Wolf¹,

Bogert²,

Schearer³

2023

Preprint

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<p>Functional electrical stimulation (FES) is a promising technology for restoring reaching motions to individuals with upper-limb paralysis caused by a spinal cord injury (SCI). However, the limited muscle capabilities of an individual with SCI have made achieving FES-driven reaching difficult. We developed a novel trajectory optimization method that used experimentally measured muscle capability data to find feasible reaching trajectories. In a simulation based on a real-life individual with SCI, we compared our method to attempting to follow naive direct-totarget paths. We tested our trajctory planner with three control structures that are commonly used in applied FES: feedback, feedforward-feedback, and model predictive control. Overall, trajectory optimization improved the ability to reach targets and improved the accuracy for the feedforward-feedback and model predictive controllers (p < 0.001). The trajectory optimization method should be practically implemented to improve the FESdriven reaching performance. </p>

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“…This is a fair assumption in the static, fully stimulated state of the muscles during system identification, but is potentially a worse assumption during a dynamic movement where the muscles will likely never be fully stimulated. The same researchers used this same static modelling technique to optimize dynamic trajectories for an electrically-stimulated arm [15]. In this case, the average wrist (end-effector) error was 8.5 ± 2.8 cm, which is potentially outside the acceptable range of functional reaching accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…In this case, the average wrist (end-effector) error was 8.5 ± 2.8 cm, which is potentially outside the acceptable range of functional reaching accuracy. In both studies, the authors indicated that improved modelling may be critical to improving controller performance [14,15].…”

Section: Introductionmentioning

confidence: 99%

Global system errors to simultaneously improve the identification of subsystems with mixed data Gaussian process regression

LaMack,

Schearer

2024

Mach. Learn.: Sci. Technol.

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This paper explores the use of Gaussian Process Regression (GPR) for system iden-tification in control engineering. It introduces two novel approaches that utilize thedata from a measured global system error. The paper demonstrates these approachesby identifying a simulated system with three subsystems, a one degree of freedommass with two antagonist muscles. The first approach uses this whole-system errordata alone, achieving accuracy on the same order of magnitude as subsystem-specificdata (9.28 ± 0.87 N vs. 6.96 ± 0.32 N of total model errors). This is significant, asit shows that the same data set can be used to identify unique subsystems, as op-posed to requiring a set of data descriptive of only a single subsystem. The secondapproach demonstrated in this paper mixes traditional subsystem-specific data withthe whole system error data, achieving up to 98.71% model improvement.

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Trajectory Optimization and Model Predictive Control for Functional Electrical Stimulation-Controlled Reaching

Cited by 14 publications

References 23 publications

Data-Driven Dynamic Motion Planning for Practical FES-Controlled Reaching Motions in Spinal Cord Injury

Data-Driven Dynamic Motion Planning for Practical FES-Controlled Reaching Motions in Spinal Cord Injury

Data-driven Dynamic Motion Planning for Practical FES-Controlled Reaching Motions in Spinal Cord Injury

Global system errors to simultaneously improve the identification of subsystems with mixed data Gaussian process regression

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