Impedance Learning-Based Hybrid Adaptive Control of Upper Limb Rehabilitation Robots

This paper presents a hybrid adaptive control strategy for upper limb rehabilitation robots using impedance learning. The hybrid adaptation consists of a differential updating mechanism for the estimation of robotic modeling uncertainties and periodic adaptations for the online learning of time-varying impedance. The proposed hybrid adaptive controller guarantees asymptotical control stability and achieves variable impedance regulation for robots without interaction force measurements. According to Lyapunov’s theory, we proved that the proposed impedance learning controller guarantees the convergence of tracking errors and ensures the boundedness of the estimation errors of robotic uncertainties and impedance profiles. Simulations and experiments conducted on a parallel robot validated the effectiveness and the superiority of the proposed impedance learning controller in robot-assisted rehabilitation. The proposed hybrid adaptive control has potential applications in rehabilitation, exoskeletons, and some other repetitive interactive tasks.