LAUSR

This systematic review explores recent advancements in control methods for rehabilitation exoskeleton robots, which assist individuals with motor impairments through guided movement. As robotics technology progresses, precise, adaptable, and safe control techniques have become accessible for effective human–robot interaction in rehabilitation settings. Key control methods, including computed torque and adaptive control, excel in managing complex movements and adapting to diverse patient needs. Robust and sliding mode controls address stability under unpredictable conditions. Traditional approaches, like PD and PID control schemes, maintain stability, performance, and simplicity. In contrast, admittance control enhances user–robot interaction by balancing force and motion. Advanced methods, such as model predictive control (MPC) and Linear Quadratic Regulator (LQR), provide optimization-based solutions. Intelligent controls using neural networks, Deep Learning, and reinforcement learning offer adaptive, patient-specific solutions by learning over time. This review provides an in-depth analysis of these control strategies by examining advancements in recent scientific literature, highlighting their potential to improve rehabilitation exoskeletons, and offering future recommendations for greater efficiency, responsiveness, and patient-centered functionality.