Research On Gait Following Control Method Of Lower Extremity Exoskeleton Driven By Air Pressure

Assisted exoskeleton robot refers to a wearable device which provides power-boosting and strength-amplification to wearer’s complete limbs.It has been extensively studied because it can significantly enhance human body functions.The wearer and the machine in the exoskeleton system should perform their great work,learn from each other,share perceptions,think and make decisions.This article focuses on the trajectory tracking control of assisted lower extremity exoskeleton robot.The main contents are as follows:First,the study considers a pneumatic-driven lower extremity exoskeleton robot which is based on the characteristics of the human lower limbs.Through reasonable assumptions,the wearable lower extremity exoskeleton structure can be simplified into the two-link model,and then the relationship between joint angle and pneumatic muscle contraction state can be mathematically analyzed.The Lagrangian dynamics method is used to establish the dynamic model of the lower extremity exoskeleton on the swing phase and the stance phase,and the specific system model parameters are given separately.This chapter lays the foundation for the controller designing.Secondly,the human lower limb movement gait is analyzed,an angle acquisition module based on mpu6050 and a plantar pressure acquisition module based on High Dynamic Force Sensing Resistor are designed.The collection,conditioning,and storage of the data are realized simultaneously.The gait period is divided by the periodic characteristics of the data.The data preprocessing is completed based on MATLAB and the gait characteristics are defined according to the processed pressure data.The Extreme Learning Machine Algorithm is used to construct a gait recognition network.The network recognition result provides the basis for gait switching in closed-loop control.Thirdly,aiming at the characteristics of the exoskeleton robot system such as nonlinearity,uncertainty,and susceptibility to external interference,the gait following problem of the extremity exoskeleton under unpredictable conditions are considered,the sliding mode controller and extended state observer are designed by state feedback.The extended state observer estimates the total interference of the system and compensates it to the controller,controller gain and chattering phenomenon of the sliding mode controller are reduced.Due to the "time-consuming" property of pneumatic muscles,the joint input torque cannot jump during gait switching,so an active smooth switching mechanism is designed.Finally,according to the characteristics of the lower extremity exoskeleton pneumatic actuators,each pneumatic muscle actuator is viewed as an agent with "self-computing ability",and a multi-pneumatic-muscle model is established based on pneumatic muscle physical tests and theoretical model.An event-based lower extremity exoskeleton distributed controller is designed,pneumatic muscles maintain distributed control at a certain speed.The pneumatic muscles can reach the specified fixed relative position or dynamic relative position so that the lower extremity exoskeleton shows a coordinated movement with the limb.The control input update time is determined by the event trigger mechanism which reduces information exchange and communication resources between pneumatic muscles.