Toward Patients’ Motion Intention Recognition: Dynamics Modeling and Identification of iLeg—An LLRR Under Motion Constraints

Abstract

In order to implement model-based recognition of human motion intention, dynamics modeling and identification of a lower limb rehabilitation robot named iLeg is investigated. Due to the relatively strong motion constraints, the traditional identification methods become insufficient for iLeg in three aspects: 1) the coupling factors among joints have not been considered in the traditional joint friction models, which makes the structural error and the torque estimation errors relatively large; 2) because of the small and complicated feasible region caused by the motion constraints, the traditional initialization strategy, for searching the valid initial solutions of the optimization problem for the exciting trajectories, becomes very inefficient; and 3) the condition number of the observation matrix, calculated from the preliminary dynamic model and the associated optimized exciting trajectory, is too large for the identification, and, however, further reduction of the condition number has not been considered in the literature. Therefore, corresponding contributions are presented to overcome the limitation. First, the coupling factors among joints are considered in the joint friction model by using the Palmgren empirical formulation and a polynomial fitting method. Then, an indirectly generating strategy is designed, by which the valid initial solutions of the optimization problem can be found with good efficiency. Moreover, a recursive optimization method based on the optimization of the dynamic model and the exciting trajectories, is proposed to further reduce the condition number. Finally, the performance of the proposed methods is demonstrated by several experiments.