Upper limb rehabilitation robot rehabilitation training motion control method

Abstract

Disclosed is an upper limb rehabilitation robot rehabilitation training motion control method. The process is as follows: 1, establishment of a state equation, i.e., hypothesis conditions are given based on Lagrange's upper limb rehabilitation robot dynamical model, and an upper limb rehabilitation robot system equation is converted into a specific expression form and compensated; 2, transformation of the state equation, i.e., an H-infinity robustness repletion controller is designed to enable the system to be asymptotically stable; and 3, then upper limb rehabilitation robot rehabilitation training motion control is carried out, and by taking a formula (34) as a basis, conditions meeting the requirements for establishing a theorem 1 are taken as control requirements. According to the invention, for the characteristic of an upper limb rehabilitation robot model, upper limb rehabilitation robot state feedback robustness repetition control is brought forward on the basis of an upper limb rehabilitation robot training model; and the design problem of a multi-period repetition controller is converted into a state feedback design through introduction of state feedback so that the tracking performance and robustness stability of a system can be improved. Siimulation results indicate that the method can realize high-precision rapid tracking and is quite good in system stability.

Description

technical field [0001] The invention relates to the overall scheme design and application technical field of the rehabilitation training motion control part of an upper limb rehabilitation robot, and in particular provides a rehabilitation training motion control method of an upper limb rehabilitation robot. Background technique [0002] The basic control structure of the robot is usually divided into two parts: the mechanism body and the control system; the function of the control system is to operate and control the mechanism body according to the user's instructions, and complete various actions of the job. The performance of the controller system largely determines the performance of the robot. The schematic diagram of the basic control structure of the robot is as follows: image 3 shown. [0003] image 3 Among them, X represents the running trajectory of the end of the robot in the desired Cartesian coordinate system; the trajectory generation link obtains the kinema...

AI Technical Summary

Problems solved by technology

[0006] Although the above-mentioned control methods for uncertain robots have achieved certain results in theory and application, there are still problems such as large amount of calculation, difficulty in guaranteeing real-time performance, parameter drift or prone to "chattering" phenomenon, etc.

The upper limb rehabilitation robot studied in this paper is a special robot that assists or replaces physicians in performing rehabilitation training for affected limbs. When controlling the upper limb rehabilitation machine, it is necessary to consider the particularity of the patient’s wear and the repetition of rehabilitation training. The existing It is uncertain that the control method of the robot cannot fully meet the control requirements of the upper limb rehabilitation machine. Therefore, it is necessary to study its suitable control method according to the characteristics of the upper limb rehabilitation robot

[0007] In the process of rehabilitation training, the purpose of the dynamic control of the upper limb rehabilitation robot is to ensure the accuracy of the movement trajectory of the upper limb rehabilitation robot and the stability of the system operation. The main influencing factors are: (1) The rehabilitation training of the upper limb rehabilitation robot is a long-term process, when the upper limb rehabilitation robot is required to perform the same task for a long period of time, component wear and aging will have an impact on the repeated positioning accuracy; (2) the training method of the upper limb rehabilitation robot is adjusted in real time according to the patient's training status, in many applications Upper limb rehabilitation robots often need to reprogram new tasks. At this time, temperature changes in the control system and the surrounding environment, as well as transient response conditions between system stop and start, will affect the positioning accuracy; (3) in the upper limb rehabilitation robot model Uncertain factors such as arm active force and time-varying torque will affect the stability of the system

Images