Using Repetitive Control to Enhance Force Control During Human-Robot Interaction in Quasi-Periodic Tasks

Abstract

We investigated the use of repetitive control (RC) to enhance force control during human-robot interaction in quasi-periodic tasks. We first developed a two-mass spring damper model and formulated three different RCs under force control: a 1st order RC (RC-1), a 3rd order RC designed for random period error, and a 3rd order RC designed for constant period error. Then, we quantified the performance of these three RCs through simulations and experiments conducted on a bench top linear platform, subject to nominal cyclical inputs (input signal and fundamental frequency: 0.5 Hz), and subject to inputs with random and constant period errors. Moreover, we compared the performance achieved with the RCs with those achievable with a passive proportional controller (PPC), subject to known theoretical limits for passivity and coupled stability. In both simulated and real-world experiments, the root mean square force error under nominal conditions was reduced most effectively by the RC-1 to 0.7% and 12.9%, respectively, of the error achieved with the PPC. Subject to inputs with constant period errors, RCs performed better than PPC for period error values below 0.05 Hz, with the RC-1 performing significantly better than both 3rd order RCs. Subject to inputs with random period errors, all RCs performed better than PPC up to 0.11 Hz of frequency error. Our results indicate that RC can successfully integrated in force control schemes to improve performance beyond the one achievable with a PPC, in the range of period variability expected in applications such as walking assistance and rehabilitation.