Research On Control Strategy Of Lower Limb Flexible Assist Suit

At present,lower-limb power-assisted clothing can include flexible power-assisted clothing and rigid power-assisted clothing.Rigid power-assisted clothing for lower limbs is mostly composed of rigid connecting rods,and has many shortcomings such as complex structure,heavy weight,and high operating power consumption.The lower limb flexible power-assisted clothing is driven by flexible materials to achieve power assistance,and has many advantages such as convenient wear and light weight.Therefore,the study of the control strategy of the lower limb flexible power-assisted suit has important research significance for the realization of efficient power assistance for each joint.This thesis studies the control strategy of the lower-limb flexible power-assisted suit,mainly studying the control strategy under the auxiliary motion of the host,the control strategy under the auxiliary follow-up control of the human host and the optimization of the follow-up control parameters.Establish the dynamic equation of the lower extremity flexible power-assisted suit.The correct dynamic model is the basis of the control strategy research.The power-assisted joints of the lower extremity flexible power-assisted suit are mainly divided into hip joints,knee joints and ankle joints.By analyzing the characteristics of human motion steps,determine the hip and knee joints to achieve power assistance,and introduce the flexibility of the lower limbs.Simplified model of man-machine integration of assisted service.The dynamic model of the system is established through Lagrangian equations,and the relationship between the motor output torque and the joint motion torque is analyzed,and the Matlab and ADAMS joint simulation are carried out.Research the master-assisted movement control strategy.Design a fuzzy PID controller and a PD feedback controller that calculates torque compensation to realize the assist torque control of the hip and knee joints.By comparing the two control strategies,it is found that the fuzzy PID control can adjust the control parameters according to the environment.The control strategy is compatible with the calculated torque compensation.The PD feedback control has better robustness in a disturbance environment,and the difference between the maximum trajectory tracking error within the complete gait cycle of these two control strategies is very small,and both have better control effects.Human-host-assisted follow-up control,the control compliance of the control system needs to be considered.By comparing force-based impedance control with position-based impedance control,position-based impedance control is selected as the human-host-assisted follow-up control strategy.The compensated PD feedback controller is used as the position-based impedance control position inner loop controller.At the same time,the position inner loop is optimized,and the RBF neural network compensator is added.The control system can finally have a better control effect.The control system has a good control effect in a disturbance environment.Better robustness.In the aspect of follow-up control parameter optimization,it is divided into offline optimization and online optimization.By analyzing the influence of impedance parameter changes of impedance control on the control system,the offline optimization of impedance parameters is realized by the variable weight variation particle swarm optimization algorithm.The objective function of the optimization algorithm is The force feedback peak value is added to suppress noise interference,which has better control accuracy than the control under random impedance parameters.Finally,the PI2 enhanced learning algorithm is used to realize the real-time online optimization of impedance parameters.Within a certain number of iterations,the control system can better adapt to the complex environment and improve the control effect and robustness of the control system.