Disturbance rejection model predictive control of lower limb rehabilitation exoskeleton

Abstract

Nowadays, exoskeleton is broadly used in the rehabilitation training of many postoperative patients. However, the uncertainty and disturbances caused by different patients and system itself may lead to incompletely rehabilitation training as planned, or even unsafety. This paper addresses the control problem of a lower limb exoskeleton, in the spirit of the recent progress on model predictive control (MPC) and extended state observer (ESO). More precisely, our approach is based on the strategy that designing an ESO to estimate the total disturbance of the dynamics model and compensating it in the design of the MPC process. To accomplish this, we introduce the virtual control quantity to decouple the dynamics model of the system and summarize the human disturbances, unmeasured states and system non-linearity as the total disturbance of the model. By doing so, the uncertainty can be estimated by our designed ESO. Based on the moving horizontal optimization and feedback mechanism of MPC, the output prediction of the system can be more accurate since the uncertainty are effectively compensated. The virtual experiment results demonstrate that proposed controller significantly improves the control accuracy on lower limb rehabilitation exoskeleton with disturbances (improved by over 34%), comparing with conventional MPC and fuzzy PID. As a result, our achievements will make contributions to better rehabilitation training for patients using rehabilitation exoskeletons.