Intention-based control strategy for lower limb rehabilitation robot

In order to enhance patient engagement in rehabilitation training, an intelligent control strategy for lower limb rehabilitation robots based on intention recognition is proposed in this paper. Interaction forces exerted by the patient are captured in real-time through force sensors, and Kalman filtering is applied to extract valid intention signals, which are then mapped to the desired motion displacement. A sliding mode control algorithm is used to drive the lifting mechanism for compliant following. The system’s stability is verified through dynamic modeling and Lyapunov stability analysis. Simulation results show that good tracking accuracy and strong anti-interference capabilities are achieved. Prototype experiments confirm the system’s responsiveness and safety in real-world human-robot interaction scenarios. The proposed strategy provides an efficient and reliable intention-driven control solution for rehabilitation robots, with promising prospects for widespread application.