Data-Driven Variable Impedance Control of a Powered Knee-Ankle Prosthesis for Adaptive Speed and Incline Walking
Data-Driven Variable Impedance Control of a Powered Knee-Ankle Prosthesis for Adaptive Speed and Incline Walking
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DOI：
10.1109/tro.2022.3226887
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发表时间：
2023-01-13
影响因子：
7.8
通讯作者：
Gregg, Robert D.
中科院分区：
文献类型：
--
作者：
Best, T. Kevin;Welker, Cara Gonzalez;Gregg, Robert D.
大多数基于阻抗的步行控制器用于电力膝关节座假肢，使用有限的状态机，其中具有数十个特定用户特定参数，这些参数需要技术专家手动调整。这些参数仅在对其进行调整的任务附近（例如步行速度和倾斜度）附近适当，因此需要许多不同的参数集来进行可变任务行走。相比之下，本文提出了一个基于数据驱动的，基于阶段的控制器，用于可变任务行走，该控制器在姿势和挥杆过程中使用连续可变的阻抗控制，以启用仿生运动。在生成具有凸线优化的可变关节阻抗的数据驱动模型之后，我们实施了一种新型的任务不变相变和速度和倾斜度的实时估计，以实现自主任务适应。对膝盖上截肢者参与者的实验（n = 2）表明，我们的数据驱动控制器1）具有高度线性相位估计和准确的任务估计，2）产生仿生的运动学趋势和动力学趋势，因为任务变化，导致相对于有能力的参考的少量差异，以及生物含糊的关节工作和CADECENCENCES趋势。我们表明，介绍的控制器会遇到，并且通常超过了我们两个参与者的基准有限状态机控制器的性能，而无需手动阻抗调整。
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 article 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.