This paper compares three controllers for quasi-passive exoskeletons. The Utility Maximizing Controller (UMC) uses intent estimation to recognize user motions and decision theory to activate the support mechanism. The intent estimation algorithm requires demonstrations for each motion to be recognized. Depending on what motion is recognized, different control signals are sent to the exoskeleton. The Extended UMC (E-UMC) adds a calibration step and a velocity module to trigger the UMC. As a benchmark, and to compare the behavior of the controllers irrespective of the hardware, a Passive Exoskeleton Controller (PEC) is developed as well. The controllers were implemented on a hip exoskeleton and evaluated in a user study consisting of two phases. First, demonstrations of three motions were recorded: squat, stoop left and stoop right. Afterwards, the controllers were evaluated. The E-UMC combines benefits from the UMC and the PEC, confirming the need for the two extensions. The E-UMC discriminates between the three motions and does not generate false positives for previously unseen motions such as stair walking. The proposed methods can also be applied to support other motions.

翻译：本文比较了三种准被动外骨骼控制器。效用最大化控制器（UMC）通过意图估计算法识别用户动作，并运用决策理论激活支撑机构。该意图估计算法需要针对每个待识别动作采集示范数据，根据识别结果向外骨骼发送不同控制信号。扩展型UMC（E-UMC）在UMC基础上增加了标定步骤和速度触发模块。为建立基准并排除硬件差异对控制器性能的影响，本文还开发了被动外骨骼控制器（PEC）。三种控制器均在髋部外骨骼上实现，并通过两阶段用户实验进行评估：首先记录深蹲、左侧弯腰和右侧弯腰三种动作的示范数据，随后对控制器进行性能评估。实验表明，E-UMC融合了UMC与PEC的优势，证实了两种扩展功能的必要性。该控制器不仅能区分三种目标动作，且对楼梯行走等未训练动作不会产生误触发。所提方法可扩展应用于其他动作的辅助支撑场景。