Position tracking control method of multi-degree-of-freedom upper limb rehabilitation robot

Abstract

The invention provides a position tracking control method of a multi-degree-of-freedom upper limb rehabilitation robot. The method comprises the following steps that S1, a kinetic equation of a multi-disturbance n-degree-of-freedom upper limb rehabilitation mechanical arm is established through a Lagrangian method, a tracking error signal equation is set according to the kinetic equation, a control rate is designed, the control rate is substituted into the kinetic equation, and a feedback control rate is obtained; S2, a sliding mode function is designed, stability proving is performed, and a control rate of the moment is obtained; S3, sliding mode control design is performed on the basis of the HJI theory; S4, a fuzzy sliding mode control strategy is designed; S5, a self-adaptive controller is designed; and S6, system stability analysis is performed. According to the method, the HJI theory is utilized, after a traditional sliding mode function is converted into the HJI inequality, fuzzy rules and adaptive adjustment approach are adopted for system disturbance terms, and the stable, rapid and high-precision position tracking effect of the upper limb rehabilitation mechanical arm is directly and accurately obtained.

Description

technical field [0001] The invention relates to the dynamic control of a mechanical arm, in particular to a position tracking control method of a multi-degree-of-freedom upper limb rehabilitation robot. Background technique [0002] As a classic control method, PID control has the advantages of adjustable dynamic characteristics and no dependence on the controlled object, but its control accuracy is lacking and its robustness is poor; sliding mode control is a nonlinear control method with high independence and is not affected by Influenced by the parameters of the dynamic model of the manipulator and external disturbance changes, the response speed is fast, but it is unstable, and it is prone to chattering during control; the torque control is highly dependent and requires the support of specific model parameters; although adaptive control can Adjust the changes of system parameters through continuous self-learning, but the amount of calculation in the whole process is huge...

AI Technical Summary

Problems solved by technology

[0002] As a classic control method, PID control has the advantages of adjustable dynamic characteristics and no dependence on the controlled object, but its control accuracy is lacking and its robustness is poor; sliding mode control is a nonlinear control method with high independence and is not affected by Influenced by the parameters of the dynamic model of the manipulator and external disturbance changes, the response speed is fast, but it is unstable, and it is prone to chattering during control; the torque control is highly dependent and requires the support of specific model parameters; although adaptive control can Adjust the changes of system parameters through continuous self-learning, but the amount of calculation in the whole process is huge, and it is only suitable for objects with simple structures; the robust control method has poor autonomy, and needs to be set first through the experience and subjective judgment of the staff The maximum value of input disturbance lacks self-learning ability and adaptability; intelligent control methods mainly include neural network control and reinforcement learning control. Neural network control itself has learning ability and strong adaptability, but it also has long learning time and difficult parameters. determine the limitations

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