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首页> 外文期刊>Mechatronics, IEEE/ASME Transactions on >Adaptive Robust Cascade Force Control of 1-DOF Hydraulic Exoskeleton for Human Performance Augmentation
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Adaptive Robust Cascade Force Control of 1-DOF Hydraulic Exoskeleton for Human Performance Augmentation

机译：一自由度液压外骨骼的自适应鲁棒级联力控制，可增强人体性能

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摘要

Hydraulic exoskeleton with human–robot interaction becomes an important solution for those heavy load carrying applications. Good human motion intent inference and accurate human trajectory tracking are two challenging issues for the control of these systems, especially for hydraulically actuated exoskeleton where the nonlinear dynamics is quite complicated and various uncertainties exist. However, robust performance to model uncertainties has been ignored in most of the existing research. To regulate these control problems, an adaptive robust cascade force control strategy is proposed for 1-DOF hydraulically actuated exoskeleton, which is namely grouped into two control levels. In the high level, the integral of human–machine interaction force is minimized to generate the desired position (which can also be seen as the human motion intent). And in the low level, the accurate motion tracking of the generated human motion intent is developed. The nonlinear high-order dynamics with unknown parameters and modeling uncertainties are built, and adaptive robust control algorithms are designed in both control levels to deal with the complicated nonlinear dynamics and the effect of parametric and modeling uncertainties. Comparative simulation and experimental results indicate that the proposed approach can achieve smaller human–machine interaction force and good robust performance to various uncertainties.
机译：具有人机交互功能的液压外骨骼成为那些重载应用的重要解决方案。良好的人体运动意图推断和准确的人体轨迹跟踪是控制这些系统的两个挑战性问题，尤其是对于液压驱动的外骨骼，其中非线性动力学非常复杂并且存在各种不确定性。但是，在大多数现有研究中，建模不确定性的鲁棒性能已被忽略。为了调节这些控制问题，提出了一种针对1-DOF液压致动外骨骼的自适应鲁棒级联力控制策略，即将其分为两个控制级别。在较高的层次上，人机交互力的积分会最小化以生成所需的位置（也可以看作是人类的运动意图）。并且在低水平上，开发了对所生成的人类运动意图的精确运动跟踪。建立了具有未知参数和建模不确定性的非线性高阶动力学，并在两个控制级上设计了自适应鲁棒控制算法，以处理复杂的非线性动力学以及参数和建模不确定性的影响。对比仿真和实验结果表明，所提出的方法可以实现较小的人机交互力，并对各种不确定性具有良好的鲁棒性能。

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