Iterative Learning Impedance Control Based on Physical Recovery Condition Elevation

Rehabilitation robots have been designed to improve ability of patients with impaired limbs to perform activities of daily life. A new iterative learning impedance control (ITLC) algorithm was developed in the present study based on the physical recovery condition of impaired limbs to improve the performance of rehabilitation robots. The physical recovery condition elevation of impaired limbs was first evaluated in terms of periodic force and trajectory tracking ratio (TTR) parameters, and the desired impedance was then modified on line using the fuzzy method according to the change of physical recovery condition. Secondly, an adaptive impedance controller was designed and modified using the iterative learning method. The convergence was analyzed based upon the Lyapunov-like positive definite sequence, which was monotonically decreasing under the proposed control schemes. Finally, the simulation experiments were conducted to compare the proposed ITLC approach the traditional impedance control. Results demonstrate the effectiveness of our learning impedance controller in rehabilitative training