Data-Driven Variable Impedance Control of a Powered Knee-Ankle Prosthesis for Adaptive Speed and Incline Walking Journal Article uri icon

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abstract

  • © 2022 IEEE.  Personal use of this material is permitted.  Permission; from IEEE must be obtained for all other uses, in any current or future; media, including reprinting/republishing this material for advertising; or promotional purposes, creating new collective works, for resale or; redistribution to servers or lists, or reuse of any copyrighted; component of this work in other works.; DOI (identifier) 10.1109/TRO.2022.3226887; Abstract:; Most impedance-based walking controllers for powered knee-ankle; prostheses use a finite state machine with dozens of user-specific; parameters that require manual tuning by technical experts. These; parameters are only appropriate near the task (e.g. walking speed and; incline) at which they were tuned, necessitating many different; parameter sets for variable-task walking. In contrast, this paper; presents a data-driven, phase-based controller for variable-task walking; that uses continuously-variable impedance control during stance and; kinematic control during swing to enable biomimetic locomotion. After; generating a data-driven model of variable joint impedance with convex; optimization, we implement a novel task-invariant phase variable and; real-time estimates of speed and incline to enable autonomous task; adaptation. Experiments with above-knee amputee participants (N=2) show; that our data-driven controller 1) features highly-linear phase; estimates and accurate task estimates, 2) produces biomimetic kinematic; and kinetic trends as task varies, leading to low errors relative to; able-bodied references, and 3) produces biomimetic joint work and; cadence trends as task varies. We show that the presented controller; meets and often exceeds the performance of a benchmark finite state; machine controller for our two participants, without requiring manual; impedance tuning.

publication date

  • December 30, 2022

has restriction

  • hybrid

Date in CU Experts

  • January 10, 2023 12:04 PM

Full Author List

  • Best T; Welker C; Rouse E; Gregg R

author count

  • 4

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