Development and Dynamic State Estimation for Robotic Knee–Ankle Orthosis With Shape Memory Alloy Actuators

Abstract The development of rehabilitation robots has long been an issue of increasing interest in a wide range of fields. An important aspect of the ongoing research field is applying flexible components to rehabilitation equipment to enhance human−machine interaction. Another major challenge is to accurately estimate the individual’s intention to achieve safe operation and efficient training. In this article, a robotic knee−ankle orthosis (KAO) with shape memory alloy (SMA) actuators is developed, and the estimation method is proposed to determine the joint torque. First, based on the analysis of human lower limb structure and walking patterns, the mechanical design of the KAO that can achieve various rehabilitation training modes is detailed. Next, the dynamic model of the hybrid-driven KAO is established using the thermodynamic constitutive equation and Lagrange formalism. In addition, the joint torque estimation is realized by the nonlinear Kalman filter method. Finally, the prototype and human subject experiments are conducted, and the experimental results demonstrate that the KAO can assist lower limb movements. In the three experimental scenarios, reductions of 59.1%, 16.5%, and 73% of the torque estimation error during the knee joint movement are observed, respectively.