| With the advent of the aging society,the rehabilitation of patients with limb movement disorders has become an urgent problem to be solved.The rehabilitation robot is of great significance for improving the rehabilitation efficiency,ensuring the rehabilitation quality and reducing the labor intensity of the workers.As an adjunctive treatment tool,the motion mode of rehabilitation robot should be rich and effective,and should be adjustable for patients with different condition and recovery period.Pneumatic muscle is a new type actuator and has a wide application and application prospect in new medical rehabilitation robots and flexible exoskeleton orthoses.It is important to carry out research on theory and technology of pneumatic muscle-driven ankle rehabilitation robot and its human-robot cooperation control under multiple interaction modes,providing a set of compliant,safe and efficient rehabilitation robot for people with limb movement disorders,which can enhance the rehabilitation effect and improve patients' rehabilitation initiative and enthusiasm.This research will not only play a positive role in promoting the pneumatic-driven materials and control,but also has important practical significance in rehabilitation system design.Based on the National Natural Science Foundation of China,this thesis studies the human-robot cooperation control method of pneumatic muscle-driven multiple degrees-of-freedom(DOF)ankle rehabilitation robot.The mechanism model and motion control technique of flexible ankle rehabilitation robot are analyzed.A variety of auxiliary training modes of the robot are realized based on the human-computer interaction and impedance model.The robot intelligent control based on biological signals such as electromyography(EMG)signals and electroencephalogram(EEG)signals is studied.A great deal of theoretical analysis and method design are carried out for the motion control,human-computer interaction and collaborative control of the multi-DOF flexible ankle rehabilitation robot.Human-computer cooperative control rehabilitation experiments under various interactive modes are performed on healthy subjects.Main contributions of this thesis include:(1)Mechanism modelling and advanced moton control of a two-DOF flexible ankle rehabilitation robot actuated by pneumatic muscles.Considering the driving characteristics of pneumatic muscle which determines the contracting length and contractility by the internal pressure,a parallel two-DOF ankle rehabilitation robot was designed.Kinematic and dynamic models of the robot are established and verified.Combined with the design,development and integration of robot hardware and software,a rehabilitation robot system is estibiliaehd.The high-performance motion control method of rehabilitation robot platform is studied by analyzing the relationship between pneumatic muscle pressure,and its displacement and output force.Aiming at the external disturbance such as modeling error and human-robot interaction existing in the process of robot control,an adaptive back-stepping sliding mode controller is proposed.By estimating the external disturbance and adjusting the control input adaptively,high-performance and accurate tracking performance can be achieved.The experiment results showed that this method has good robustness.(2)Human-robot interaction control of soft ankle rehabilitation robot.Impedance control model is established according to the patient's intention and initiative and the interaction with the robot.The assistant control strategy that adapts to the changes of patients' interaction and movement ability is studied.An innovative hierarchical compliant control structure was proposed considering the flexible characteristics of pneumatic muscle actuator.From the aspects of actuator compliance in joint space and impedance characteristic in task space,taking into account the patient's contribution and movement ability over the past period of time,an adaptive compliant control mode was achieved to complete the assistance-as-needed rehabilitation for the patients.The experimental results showed that the proposed hierarchical compliance control can adjust the auxiliary output online according to the participants' status,providing a feasible solution for the patient's human-robot interaction training.(3)Human-muscle-robot cooperative control method driven by surface EMG.Muscle synergy theory is used to extract the features of the ankle motion pattern.The patient's intention was classifited by using correlation analysis,realizing efficient recognition of ankle joint multi-DOF movement.The patient's muscle status is detected in real time to adjust training strategy adaptively during the robotic assistance.Time domain,frequency domain,time-frequency domain and nonlinear features related to patient's fatigue are extracted from sEMG to build the muscle fatigue assessment model.Human-muscle-robot cooperative control was achieved by adjusting the robot stiffness coefficient to the fatigue factor from the assessment model,which could help the patient adjust the rehabilitation training according to their own needs.When the patient entered the fatigue state,the robot movement would stop to avoid secondary damage to the patient.Experiment results showed that adaptive impedance control based on the muscle activity assessment could slow down the patient's fatigue and get better rehabilitation training effects.(4)Human-brain-robot cooperative control method based on patient motor imagery.A novel method of EEG signal channel selection based on brain networking theory is proposed.A motor imagery classifier based on multi-domain feature fusion is established,realizing accurate intention recognition in the rehabilitation training for patient.The patient's imaginary result is mapped into multi-DOF movement of the ankle rehabilitation robot,thus establish a brain-computer interface and collaborative control system based on motion imagery.A robot adaptive control technology based on EEG feedback is proposed.Synchronization and asynchronous control of ankle rehabilitation robot are realized through motion imagery.Fault tolerance mechanism is introduced to adjust robot training strategies and tasks according to patient's continuous motor imagery.Experiments showed that the proposed method can improve the classification accuracy of motor imagery tasks while reducing the number of channels,and the involved subjects can control the rehabilitation robot independently according to their own intentions during robotic training. |
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