부산대학교 도서관

Motorized functional electrical stimulation (FES) induced cycling is a rehabilitation intervention and exercise strategy that can benefit people with movement disorders. Power tracking is an objective in which leg muscles are artificially activated to track an active torque trajectory while an electric motor achieves a desired speed (cadence). Technical challenges remain to enhance muscle torque tracking performance since muscles experience saturation, which can induce error build-up. Further, the electric motor controller needs to account for the cross muscle torque input in the kinematic tracking loop and thus reduce potential cycling fluctuations. In this paper, a FES muscle torque tracking controller is designed with an anti-windup term in an integral torque error signal to mitigate the effect of muscle saturation. Then, an adaptive-based concurrent learning controller is developed for the electric motor to track cadence and estimate uncertain constant parameters of the cycle-rider system. The adaptive motor controller embeds the muscle torque (since the muscle controller is implementable) in the regressor and exploits it as a feedforward term in the cadence loop, rather than canceling the muscle torque input as it is usually performed in robust control designs. Globally uniformly ultimately bounded (GUUB) tracking is obtained for the torque tracking objective and exponential cadence tracking and parameter convergence is obtained by the learning controller after a finite excitation condition is satisfied.