Hierarchical Trajectory Deformation Algorithm with Hybrid Controller for Active Lower Limb Rehabilitation

Robot-aided active rehabilitation has shown to be an effective treatment approach for hemiplegic patients. This paper presents an active control framework for lower limb rehabilitation, combining an interaction layer with a hierarchical trajectory deformation algorithm (HTDA), and an assist-as-needed (AAN) layer with a hybrid controller. The HTDA uses constrained optimization in both position and velocity domains to continuously generate smooth reference trajectories based on virtual interaction forces during physical human-robot interaction (pHRI). An additional optimization loop is also implemented to achieve adaptive parameter adjustment for HTDA. Meanwhile, the hybrid controller relies on a force field term and a velocity field term to provide AAN feature. The proposed method is validated on a two-degree-of-freedom lower limb rehabilitation robot for walking with variable step height and step length. The experimental results demonstrate that compared to previously developed admittance model (AM) and trajectory deformation algorithm (TDA), under four different evaluation metrics, HTDA can improve dimensionless squared jerk (DSJ) by $73.6\%$ comparing to AM and improve constraint force percentage (CFP) by $20.4\%$ comparing to TDA. This demonstrate the effectiveness of the proposed framework in reducing human-robot confrontation, especially in improving robot actuation compliance and movement smoothness.