Neuro-Fuzzy Musculoskeletal Model-Driven Assist-as-Needed Control via Impedance Regulation for Rehabilitation Robots

In rehabilitation applications, encouraging patients to actively participate in training is essential for effective recovery. However, personalized control design in robot-assisted therapy remains challenging due to variations in patients' motor capabilities. To address this issue, this paper proposes an assist-as-needed (AAN) control framework that integrates a hybrid fuzzy-transformer neural network (HFTN) with a fuzzy echo state network (FESN)-based variable impedance controller to ensure personalized support and active engagement. The HFTN integrates fuzzy logic with transformer architectures in parallel paths, establishing a novel neuro-fuzzy musculoskeletal (MSK) model that maps surface electromyography (sEMG) signals to joint torque through combined uncertainty and temporal modeling for enhanced real-time estimation. The variable impedance controller constructs the stiffness and damping matrices of the robotic system through the FESN and develops an adaptive update law for the FESN output weights, effectively addressing instability issues in variable stiffness control. Furthermore, driven by physiologically estimated joint torques from the HFTN, the adaption of the FESN reservoir states enables real-time modulation of stiffness and damping, facilitating transitions between human-dominated and robot-dominated modes. This realizes the AAN concept, ensuring personalized and responsive assistance. Various experiments on an upper limb rehabilitation robot were conducted to validate the effectiveness of both the neuro-fuzzy MSK model and the AAN controller in delivering optimal assistance while promoting active user participation.