Active lower limb exoskeletons are increasingly used in rehabilitation therapy. An important aspect and ongoing research topic in developing such assistive devices is safe human–machine interaction. Another major challenge in the control of exoskeletons is determining the subject's intention to move for providing cooperative assistance. To address both challenges, we first present the design of a new lower limb exoskeleton with variable stiffness actuators for compliant coupling between human and robot. The exoskeleton's actuated hip, and knee joints are controlled via a stiffness-dependent torque control. In a second step, we derive a detailed model for the swing phase using the Lagrange formalism. We utilize the model and an interaction torque measurement based on the deflection of the serial elasticity to estimate the subject's motion intention. This estimation is realized by nonlinear state estimation using an unscented Kalman filter (UKF). The UKF is able to track the hip and knee torque contributed by the subject during normal gait in real time and provides more robust results than the inverse dynamics approach.

Active exoskeletons for the lower extremities are increasingly being used in rehabilitation therapy. One of the key areas of research in developing these assistive devices is ensuring safe human-machine interaction, which requires both a safe mechanical system and an effective assistive control framework. Therefore, we present a novel human cooperative control framework for exoskeletons with variable stiffness actuators to assist users during both the swing and stance phases of walking and other motion sequences such as sit-to-stand. The control framework estimates the user’s joint torques during the swing and stance phases using an Unscented Kalman Filter (UKF) and inverse kinematics, respectively. Using the Lower-Limb Exoskeleton with Serial Elastic Actuators (LExo-SE) as an example, the control approach was validated for its applicability to exoskeletons with compliant actuators. The validation results reveal a reduction of the average user’s joint torque during gait by 63.6%-78.4% for the hip and 40.8%-50.2% for the knee compared to non-assisted walking. Furthermore, we introduce an automatic stiffness selection for the serial elasticity of the variable stiffness actuator (VSA) based on the gait phases and the active human joint torque. The stiffness variation increases the physical human-robot interaction during the swing phase while maintaining a high control bandwidth during the stance phase.

This paper investigates a closed-loop torque-controlled variable stiffness actuator (VSA) combined with a disturbance observer for enhancing low output impedance. We implement the generalized extended state observer (GESO) for conveniently testing the stability of the time-varying VSA system. In our application, the GESO is also required to serve the operation of the low and high impedance task. Here, the most important aspect is to consider the influence of the physical stiffness on the output impedance, because the VSA has been regulated with the closed-loop torque control. Through the interaction-torque experiments, we verify that using the fast dynamics GESO with the low-stiffness actuator can achieve low output impedance and stable interaction under the reachable frequency of a human. These properties contribute to perform the low-impedance task. When performing the high-impedance task, where a large torque command is needed, the high-stiffness actuator and the slow dynamics GESO are implemented to achieve high bandwidth and proper tracking performance. The continuously variable observer responses in accordance with the stiffness values are achieved via the gain-scheduling control. Moreover, the present closed-loop linear parameter varying system can be verified to be quadratically stable. The VSA system is then implemented on a knee exoskeleton for a sit-to-stand application. The reference command of the exoskeleton is a joint torque, calculated from the inverse dynamics. This torque signal is also used as the reference command of the stiffness motor of the VSA. The effectiveness of the exoskeleton system is experimentally verified with one healthy volunteer. Subsequently, another two healthy volunteers also successfully experienced the system.

The lower limb exoskeleton investigated in this work actively supports the knee and hip and is intended to provide full motion support during gait. Parallel elastic actuators are integrated into the hip joints to improve the energy efficiency in gait. The prototype was tested in sit-to-stand and gait trials, in which the actuators were cascade-controlled with position trajectories. The compliant actuation of the hip in gait experiments proved to be more efficient; the peak torque was reduced by up to 31% and the RMS power was reduced by up to 36%.

Wearable robots hold great promise in supporting people during activities of daily living. Personalizing the assistance provided by this technology for not just level ground walking, but also other real-life activities like uphill and downhill walking, will foster its wider adoption. In this paper, we propose an approach to personalize the assistance delivered by a wearable robot -the Myosuit, a tendon-driven soft robot for mobility assistance -for downhill walking. We use a human-in-the-loop method with candidate profiles of 15-steps which aims to minimize knee extensor muscle activity, utilizing the covariance matrix adaptation evolution strategy algorithm (CMA-ES) and a cost function based on normalized RMS of surface EMG signals. By utilizing CMA-ES, we vary the magnitude and timing of Myosuit’s support to find the most appropriate assistance profiles. We compared the optimal profiles to assistance off (transparency) condition and a general trajectory derived using a model-based approach. Relative to the transparency condition, the average muscle activity was reduced by 12.67% (SD: ±8.73; t(20) = 5.25, p≤.01) across all participants and muscle groups. The results of our study may support future development of personalized assistance algorithms for wearable robots and lead to better adoption of this technology.