Research On Human-robot Cooperative Control Of Flexible Manipulator Based On Human-surface EMG Signal

Compared with the traditional rigid manipulator,the flexible manipulator has flexible joints and flexible deformable connecting rod.Due to the advantages of high flexibility and strong adaptability,the flexible manipulator has become a kind of robot that can assist patients in medical rehabilitation.Intelligent cooperation of rehabilitation robots is the future development trend,but there are still shortcomings in human movement recognition and man-machine cooperation control.Therefore,this topic starts from the human surface electromyography signal,carries out research around the stiffness characteristics of human body,and combines the concept of human-machine collaboration,focusing on solving the compliant control problem of robotic arm based on the stiffness of human upper limb.The details are as follows.According to the requirements of robotic arm cooperation tasks and the collection mode of human electromyography signals,a human-machine collaborative control framework for flexible robotic arms based on human surface electromyography signals is constructed.By analyzing the acquisition and preprocessing problems in the quantization process of human electromyography signal,the electromyography bracelet wearing mode and the signal extraction method based on amplitude envelope were established.By analyzing the mapping problem between electromyography information and the upper extremity,the stiffness identification system based on electromyography was established.By analyzing the nonlinear uncertain factors existing in the process of human-robot collaborative task,Combined with human stiffness information,a cooperative control system of flexible manipulator based on human electromyography signal was established.Based on the impedance estimation of human upper extremity,the stiffness model of human upper extremity was established.Through the mapping identification of human upper extremity force and electromyography signal,the arm end stiffness model based on human electromyography information was established.Aiming at the compliance control problem of manipulator in the process of cooperation,the RBF neural network sliding mode controller was designed based on nonlinear uncertainties.Considering the time-varying characteristics of human stiffness parameters,combined with the fuzzy relationship between the traction force of human upper limb and the external torque of the manipulator,an adaptive RBF neural network sliding mode control method based on human electromyography signal was proposed.The human-robot cooperative control experiment platform was built,the ROS operating system was used to develop the control algorithm,and the traction and follow-up experiments of the manipulator were carried out under different fatigue degrees to verify the rationality and effectiveness of the adaptive RBF neural network sliding mode control method based on human electromyography signal.