Data-Driven Variable Impedance Control of a Powered Knee-Ankle
Prosthesis for Adaptive Speed and Incline Walking
Abstract
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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.