Combined state and parameter estimation for adaptive control and feedback applications for a gait rehabilitation robot

Unsupervised gait training of patients using a robotic trainer requires an individual adaption of the modelbased control for each patient as well as a feedback of the patient´s performance. For these adaption and feedback tasks, estimation of the kinematic states, the friction states, and the patient-dependent parameters of the model is necessary. In this contribution, a combined estimation approach is proposed for a new gait training robot using a central difference Kalman filter, which is based on a simplified gait trainer model extended by a dynamic friction model. In the resulting overall model, the three dominating mass parameters, which depend on the patient´s mass and activity, are redefined as states with integrating character. The observer approach is applied on a prototype of the gait trainer and its accuracy is evaluated with measured data using reference weights.