Research On Trajectory Tracking Of Upper Limb Rehabilitation Robot Based On Iterative Learning Control

With increasing aging trend of populations in the world,there are more and more stroke patients.It becomes an effective means for people to use rehabilitation robots to assist the rehabilitation of motor nerves of stroke patients.Therefore,domestic and foreign researches all focus on combining rehabilitation robots with modern medicines and using rehabilitation robots instead of physiotherapists to assist patients with the rehabilitation of their affected limbs.According to structures of human upper limbs,this thesis designs a kind of Three Degree of Freedom(3-DOF)upper limb rehabilitation robot,which can assist patients to simulate their upper limb movements and help realize rehabilitation treatment for patients with motor dysfunctions of upper limbs.Considering the complex characteristics of dynamic systems of the upper limb rehabilitation robot,this thesis uses the Iterative Learning Control(ILC)as the control theory basis to research the trajectory tracking problem of 3-DOF upper limb rehabilitation robot in the process of rehabilitation training.Finally,this upper limb rehabilitation robot can track the rehabilitation training trajectory of patients in a stable,fast and accurate way.This thesis mainly includes the following contents:1)The applicationprospect of the upper limb rehabilitation robot in our real life and its research status at home and abroad are introduced in this thesis.This thesis describes control algorithms currently applied to rehabilitation robots in detail and analyzes advantages and disadvantages of various control algorithms.2)Proe software is used to build a virtual prototype of 3-DOFupper limb rehabilitation robot to analyze structures of the upper limb rehabilitation robots and D-H method is used to obtain forward/inverse kinematics equations of the system.Then,Matlab is used to build a 3D kinematics model of 3-DOF upper limb rehabilitation robots.In addition,in order to effectively control rehabilitation robots,this thesis uses the Lagrange equation method to build a dynamics model for upper limb rehabilitation robots.3)The trajectory tracking control of upper limb rehabilitation robots is researched in this thesis based on exponential variable gain ILC.First of all,this thesis introduces the principle of Iterative Learning Control,open and close loop control and the basic P-type,D-type and PID control rules.Considering the slow learning speed of fixed-gain ILC,a closed-loop exponential variable gain D-type ILC control law is designed and its convergence is analyzed in detail.Besides,3-DOFupper limb rehabilitation robot is used as the controlled object to do the trajectory tracking control.Matlab simulation results show that compared with fixed-gain ILC algorithms,the closed-loop exponential variable gain D ILC has a fast learning speed in tracing trajectory of upper limb rehabilitation robots.4)The trajectory trcaking of the gain coefficient variable ILC of upper limb rehabilitation robots is researched based on the initial state learning.Each time the Iterative Learning Control operates,the initial state of the system shall be the same or should be the expected initial state.However,the initial state errors often exist in the actual rehabilitation training process,so it is difficult to keep the initial state of the system as the expected value.Therefore,this thesis introduces initial state learning control into the Iterative Learning Control strategy and design an exponential variable gain ILC Control law based on initial state learning.Besides,the convergence of the controlled system is proved by the strict operator theory and Matlab is used to do trajectory tracking control simulation experiments on 3-DOF upper limb rehabilitation robots.The experimental results show that the gain coefficient variable ILC based on initial state learning solves the problem that the Iterative learning Control strictly requires iterative states.At the same time,this thesis also uses the exponential gains so that the rehabilitation robots can track the expected track stably and quickly in the trajectory tracking process.5)The trajectory tracking of Adaptive Iterative Learning Control(AILC)of upper limb rehabilitation robot is also researched in this thesis based on uncertain disturbance.Iterative Learning Control strategy can effectively solve the problems of mathematical models of the system are inaccurate and have repetitive perturbations.However,when the patients suffer from convulsive disturbance and are influenced by external uncertain disturbance factors,the control can not have ideal effects,so an improved AILC control law,which can estimate and compensate the unknown disturbance of the system online in real time and which can do iterative learning to the control input,is designed.The convergence of the system is proved by the Lyapunov composite energy function.In addition,Matlab simulation experiments on trajectory tracking control are also done on the control algorithm proposed.The simulation results show that,compared with traditional AILC algorithms,the upper limb rehabilitation robots following improved AILC control laws can not only overcome the uncertainty disturbance,but can also track the expected trajectory quickly,which can finally improve the control quality of the system.