Towards Real-Time Personalized Control in Wearable Robotics: A Hierarchical Architecture for Lower-Limb Assistance

Abstract

Personalized and effective control is essential for the acceptance of wearable robotic systems such as lower-limb exoskeletons. This paper introduces the adaptation and control problem in these systems, outlines key challenges, and presents a hierarchical control framework considering the BiArticular Thigh EXosuit (BATEX) as an example. Lower-limb exoskeletons are wearable robots that assist walking by applying joint-level torques in coordination with the user. The proposed architecture includes a low-level hybrid controller for velocity tracking, a mid-level neuromechanical controller for stiffness modulation based on biomechanical feedback, and a high-level user-in-the-loop gain adaptation. To explore the role of predictive methods, a model predictive controller is implemented at the actuator level, improving force tracking, disturbance rejection, and constraint handling compared to conventional control, outlining the potential of a unified predictive control framework exploited at the different levels. Experiments with human subjects indicate enhanced gait performance, demonstrating the promise of hierarchical and predictive control for adaptive, user-centered assistance in wearable robotics.