Autonomous Assistance-as-Needed Control of a Lower Limb Exoskeleton With Guaranteed Stability

Abstract

The use of exoskeletons for clinical lower-limb stroke rehabilitation offers the potential of improved and customized rehabilitation that reduces the requirements and demands placed on multiple staff members. Initial research with lower-limb exoskeletons show potential to alleviate this problem. Conventional assistance-based exoskeleton devices simply enforce the desired gait trajectory for the patient in order to ensure safety and stability. Unfortunately, if the end-user does not have to work to contribute to successful motion, rehabilitation often does not occur. Recent evidence has suggested that assistance-asneeded control prevents users from slacking, facilitating functional motor recovery. Assistance-as-needed control turns off assistive torques during periods when the patient is able to execute a desired gait pattern but if the patients gait deviates sufficiently from a desired trajectory then assistive torques are generated to compensate for the patients loss of strength. This strategy encourages the patient to contribute effort while still enabling the exoskeleton to guide movements. Assistance-as-needed control inherently leads to aperiodic gait patterns and has accordingly been difficult to employ in lower-limb exoskeletons due to the need to ensure stability. This work demonstrates how virtual constraint control-a method with robust stability properties used in prostheses and assistive exoskeletons control-can be combined with a velocity-modulated deadzone to ensure stability. Simulations suggest that the method can accommodate a large deadzone while remaining stable across a range of unanticipated gait pathologies, as demonstrated using Lorenz mappings that can accommodate the aperiodic nature of the resulting gait.

Extended State Observer-Based Nonlinear Terminal Sliding Mode Control With Feedforward Compensation for Lower Extremity Exoskeleton

“…These devices can help users practice repetitively and free physical therapists from heavy work [1]. In past decades, many kinds of wearable exoskeletons are designed to help patients rehabilitate from the jury [2,3,4]. As the exoskeleton is a typical human-machine interaction system, it is essential to design and develop control strategies to track the defined human gait safely and robustly.…”

Optimal robust control with cooperative game theory for lower limb exoskeleton robot

“…In recently years, lots of excellent efforts have been carried out, such as robust control [5], hierarchical control [6], adaptive control [7], fault-tolerant control [8] and neural network control [9] and so on. Campbell et al [10] develop the assistance-as-needed control to help the patient with rehabilitation which means the robot will provide assistance only when the patient deviates from the desired gait. In order to overcome the unstable phenomenon, Sun et al [11] construct a reduced adaptive fuzzy control with compensation and verify its effective on LLER system.…”

Neural Networks Trained via Reinforcement Learning Stabilize Walking of a Three-Dimensional Biped Model With Exoskeleton Applications

“…Li et al (2020) proposed an algorithm based on the zero moment point (ZMP) to modify the gait generated through human walking synergy for paraplegic patients but required the use of bilateral canes, or crutches. Campbell et al (2020) combined virtual constraint control with a velocity-modulated dead-zone to ensure the stability of a walking model. Zhang et al (2018) presented a balance controller based on the extrapolated center of mass concept for maintaining walking stability.…”