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

Abstract

<p>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. </p>

Gait Event Detection with Proprioceptive Force Sensing in a Powered Knee-Ankle Prosthesis: Validation over Walking Speeds and Slopes

“…Phase is a measure of progression though the gait cycle that monotonically increases from 0 to 1 from heel strike to heel strike. In this work, phase was determined based on the thigh angle measured by a prosthesis-mounted inertial measurement unit as described in [20]. To estimate the GRF acting on the system, the effective point of contact with the ground is needed.…”

“…The device used the phase-based controller described in detail in [20]. This controller employs a continuously-varying impedance paradigm during stance and position control during swing.…”

“…In this equation ŝst is the stance phase estimate and χ is the task defined as the combination of walking speed and incline. The joint stiffness K, damping B, and equilibrium angle θ eq are modeled as functions of phase and task to best predict able-bodied joint torques given able-bodied joint kinematics [20]. For this paper, the correct task was given to the controller, not estimated in real-time as in [20].…”

“…The joint stiffness K, damping B, and equilibrium angle θ eq are modeled as functions of phase and task to best predict able-bodied joint torques given able-bodied joint kinematics [20]. For this paper, the correct task was given to the controller, not estimated in real-time as in [20]. The amount of torque was scaled by the mass of the user m, since the impedance parameters are normalized by mass.…”

“…During swing, the control strategy in [20] employs a proportional-derivative controller to track desired joint kinematics, θ d and θd , which are modeled as functions of phase and task based on able-bodied walking data [1]. Swing torque is then calculated as…”