Research On The Motion Control Strategies For Lower Limbs Rehabilitation Robot

The rehabilitation robot is an important branch of medical robot that can help the affected limbs recovery,which involves many fields,such as rehabilitation medicine,robotics and control science.Patients with paralysis a re gradually increasing with the aging phenomenon becoming more and more serious in recent years.In this paper,a lower limbs rehabilitation robot is designed for patients with lower limbs paralysis,mainly discusses the control strategies,including passive training control and active training control,and the control strategies are verified by simulations and experiments.We need a detailed understanding of the experimental platform in order to control the motion of the robot better.In this paper,the horizontal lower limbs rehabilitation robot has 3 degrees of freedom,which can meet the patients' daily gait training.The dynamics of the robot system is analyzed by using the Lagrange equation,and the relationship between the driving force and the torque is derived.Firstly,the joint driving torques are obtained by the dynamic equation;secondly,the dynamic model of the robot system is established by ADAMS software,and we can also obtain the driving torques by simulation experiments;finally,we can verify the correctness of the dynamics equation and the dynamics model by comparing the two kinds of the driving torques.The patient needs passive exercise training when the affected limb is completely paralyzed,and the passive exercise control can be regarded as trajectory tracking control.In this paper,three kinds of control methods are used: the computed torque control is used to control the nominal model of the system,the RBF neural network is used to compensate the unknown uncertainties of the system,and the adaptive robust controller is used to compensate the error of the neural network approximation and the external interference.In this paper,Lyapunov stability theory is used to verify the stability of the algorithm,and the effectiveness of the algorithm is verified by simulation experiments.The patient can carry out active exercise training when the affected limb gradually recovered with a certain ability to move,the desired force of the li mb is tracked by the impedance control based on the position.Firstly,the human-robot contact impedance is studied;then the influence of the impedance parameters on the control performance of the system is analyzed,using particle swarm optimization algorithm to optimize the impedance parameters;finally,the model reference adaptive control is used to improve the robustness of the system,and the simulation results show the effectiveness of the algorithm.The experimental system of the horizontal lower limbs rehabilitation robot is built,including hardware system and software system.The corresponding settings of the motors and the drivers are set up according to the characteristics of the experimental platform.And the sensors are calibrated so that the environment can be detected in the real time.The input and outp ut of the system are detailed deduced so that it can ensure the accuracy of the system,and the software limit is designe d to ensure the security of the system.Finally,the effectiveness of the proposed neural network robust control based on the computed torque control and the model reference adaptive impedance control in improving the control accuracy is verified by experiments.