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Lower-limb exoskeletons are promising applications of robotic rehabilitation for people with motor impairment. As current studies have tailored the design of gait trajectories for the target users, realizing a high-precision motion control is a critical issue for safe and effective assistance. The walking assistance involves unique characteristic phases that embody different physical constraints and requirements for assistance. Conventional methods often utilized gain-switching control for time-varying adaptation. However, despite their intuitiveness as well as simplicity, the control performance was unsatisfying due to unmodeled responses by human behavior and continuous interaction with the external environment. To tackle these challenges, this study proposes a hybrid control method applied to the disturbance observer that can provide robust robotic rehabilitation. The proposed method adaptively identifies the exoskeletal system as a hybrid nominal model and online exchanges model-based tracking controllers parallelly to the gait phase of a user. Furthermore, a unique filter named allowance filter is introduced to compensate for the plant dynamics, preventing instability of the inverted plant and realizing digital implementation. In this article, a practical user with complete paraplegia participated in the experiments for verification of the proposed methods. [ABSTRACT FROM AUTHOR]

Powered exoskeletons for people with complete paraplegia have been controlled based on predefined joint-reference trajectories. As the target users of such robots may not realize any voluntary movement, the human body is fully constrained and follows the movement of the powered exoskeleton joints. The predefined gait pattern, however, may or may not be adequate for every user because the gait pattern is resulting from complex interactions between the body segments and environment, as well as dynamic characteristics of the body segments. As all the persons and their body segments have different dynamic characteristics, therefore, a bespoke tuning of gait parameters is necessary in order to realize the natural gait motion, which is optimal for each user. In this article, an adaptive gait pattern adjustment method is proposed. The proposed method observes the ground contact timing, which is directly related to the adequacy of the gait pattern for the user wearing a robot. Based on the ground contact timing, the joint-reference trajectories are adjusted, which are parameterized by the trunk inclination angle. The proposed method iteratively calculates an appropriate trunk inclination angle from the information of ground contact timing. In this article, the derivation of the proposed method and its experimental verification with WalkON Suit, a powered exoskeleton, are introduced. The proposed algorithm successfully worked for two practical users with complete paraplegia, and the adapted gait patterns showed excessive performance in walking speed, oxygen consumption, palm force on crutches, etc. The results were also verified by winning both gold and bronze medals in the global competition, Cybathlon 2020, while accomplishing the best records among all the teams. [ABSTRACT FROM AUTHOR]