Robust iterative learning control strategy for five-degree-of-freedom upper limb exoskeleton system

Abstract

The invention discloses a robust iterative learning control strategy for a five-degree-of-freedom upper limb exoskeleton system. The control strategy employs a multivariable iterative learning item toestimate time-varying parameters in an upper limb exoskeleton model, and does not need to build an accurate mathematic model; besides, in order to improve tracking precision and obtain high robustness, sliding mode control is introduced into an iterative learning scheme; and a sliding mode controller is used for processing non-periodic lumped disturbance, and a self-adaptive law is designed in the control strategy and used for compensating uncertainty in the controller, so that a buffeting phenomenon caused by a switching item is weakened, tracking errors between human and machines are reduced, and tracking errors of all joints can be converged to be close to zero within limited iteration times. According to the control strategy, the tracking control problem of the five-degree-of-freedomupper limb exoskeleton system robot under unknown model parameters and aperiodic disturbance is solved, and the control strategy is high in algorithm robustness, small in control buffeting and good intracking performance.

Description

technical field [0001] The invention relates to the technical field of exoskeleton control, in particular to a robust iterative learning control strategy for a five-degree-of-freedom upper limb exoskeleton system, which can be applied to wearable upper limb rehabilitation robots and contributes to the development of upper limb exoskeleton-assisted sports. Background technique [0002] The upper extremity exoskeleton system is a wearable mechanical device. As a strength support, it can assist humans to complete heavy physical work. As a medical rehabilitation device, it can assist disabled people in rehabilitation training. It has important practical value in military, medical and other fields. The basic working principle of the upper extremity exoskeleton system is to sense the wearer's movement intention, provide a certain torque for its specific joints, adjust the position and angle of each joint of the exoskeleton robot in real time, and realize fast and accurate tracking ...

AI Technical Summary

Problems solved by technology

However, traditional iterative learning control methods have certain limitations in dealing with non-periodic signals such as system uncertainties and external disturbances, which seriously affect the dynamic characteristics of the system.

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