Event-Triggered Adaptive Hybrid Torque-Position Control (ET-AHTPC) for Robot-Assisted Ankle Rehabilitation

Ankle rehabilitation for increasing number of strokes is highly demanded, and robot-assisted approach has shown great potential. Since the required movement and force assistances will concurrently change during rehabilitation sessions, the robotic assistances are supposed to be adjusted accordingly. In order to achieve both adaptive torque and synchronous position control for the robot in practice, a novel event-triggered adaptive hybrid torque-position control (ET-AHTPC) is proposed for a developed ankle rehabilitation robot driven by pneumatic muscles (PM). In the novel adaptive torque control scheme, the assistive torque adapted to the patient's recovery state is adjusted by a designed robot-assisted rehabilitation index mapping from the clinical assessment scale. The robotic assistance output is online corrected by patient's performance, based on a correcting index calculated by interaction torque and tracking errors. Then a model-based event-triggered optimal position controller is established and a critic neural network (NN) is introduced to reduce the control law update frequency for fast trajectory tracking. The stability of the overall system is proved by Lyapunov theorem. A series of experiments were conducted on the ankle rehabilitation robot to validate the controller's fast trajectory tracking and adaptive assistance capacity, which can online adjust the robot's assistive torque and allowable movement range for patients at different recovery stages.