The past, present and future of control architectures in lower-limb cable-driven robots for gait rehabilitation

Due to their high power-to-weight ratio, modular and reconfigurable architectures, and inherent compliance, cable-driven rehabilitation robots (CDRRs) provide safe, lightweight, backdrivable solutions for gait and movement rehabilitation. However, they continue to face unique control challenges due to cable properties and user variability. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, this systematic review explores control strategies for lower-limb CDRRs from the past decade. Out of the 968 studies initially identified, 70 met the selection criteria and were classified into six categories: position and velocity, force- and torque-based, compliance-based, model-based and optimal, learning-based and intention-informed, and hierarchical frameworks. Our analysis revealed a chronological evolution from traditional classical control toward more personalized, adaptive, learning-based, and intention-driven methods. Impedance and admittance control remain fundamental for ensuring safety, while newer approaches enable user-specific and environment-responsive assistance. This review proposes a unified hierarchical framework linking high-level intent detection to low-level actuation providing researchers and developers with a structured understanding of the control landscape for cable-driven lower-limb exoskeletons in healthcare and beyond. Control strategies were also linked to clinical outcomes to relate them to functional improvements across patient populations. Advancing CDRRs will require unified, multi-layer architectures that couple constraint-aware model-based control with adaptive and intention-driven learning to achieve safe, scalable, and clinically meaningful rehabilitation.