Flatness Control with Cascaded Filtered High-Gain and Disturbance Observers for Rehabilitation Exoskeletons

Accurate trajectory tracking in lower-limb exoskeletons is challenged by the nonlinear, time-varying dynamics of human-robot interaction, limited sensor availability, and unknown external disturbances. This study proposes a novel control strategy that combines flatness-based control with two cascaded observers: a high-gain observer to estimate unmeasured joint velocities, and a nonlinear disturbance observer to reconstruct external torque disturbances in real time. These estimates are integrated into the control law to enable robust, state-feedback-based trajectory tracking. The approach is validated through simulation scenarios involving partial state measurements and abrupt external torque perturbations, reflecting realistic rehabilitation conditions. Results confirm that the proposed method significantly enhances tracking accuracy and disturbance rejection capability, demonstrating its strong potential for reliable and adaptive rehabilitation assistance.