The Research On Dynamic Parameter Identification And Trajectory Tracking Control Of SCARA Robot

Due to its advantages of compact structure,large workspace,flexible operation,repeated positioning accuracy and high performance price Ratio,SCARA robot has been widely used in plastic,automobile,electronics,medicine,food and other industrial fields.However,SCARA robot is a multivariable,strong coupling nonlinear system.In order to improve the control accuracy and trajectory tracking accuracy of SCARA robot,it is of great significance to investigate the dynamics parameter identification and trajectory control algorithm of robot.In this paper,based on the theoretical analysis of robot kinematics and dynamics mathematical modeling,the dynamic mathematical model of SCARA robot is established,the random weight particle swarm optimization algorithm is proposed,and the corresponding program is developed to identify the dynamic parameters of SCARA robot.On this basis,a sliding mode robust neural network adaptive controller is designed to control the robot trajectory tracking.The main contents of this paper are as follows:(1)SCARA robot system dynamics mathematical modeling.The coordinate transformation relationship between SCARA robot joint and connecting rod in space is analyzed,and the kinematics model of SCARA robot is established.The kinematics simulation experiment of SCARA robot is conducted to verify the rationality of the established kinematics model.(2)Parameter identification of SCARA robot system dynamics model.Based on the analysis of the load influence on the torque of each joint,the structure of SCARA robot is simplified.The dynamic mathematical model of the robot with load is established by using Lagrange method,and the dynamic parameters of the robot end that need to be identified are determined.a random weight particle swarm optimization algorithm is proposed to identify the dynamic parameters of the robot,and the corresponding program is developed.The identification results show that the convergence speed and parameter particle search range of the random weight particle swarm optimization algorithm are significantly improved,and the torque curve of the robot identified is basically consistent with the actual output torque curve,which indicates that the algorithm has high accuracy for the dynamic parameters identification of the robot model.(3)Trajectory tracking control method for SCARA robot.Considering that the trajectory accuracy of SCARA robot will be affected by external disturbance in the actual working process,a neural network adaptive algorithm based on sliding mode robust term is proposed to control the trajectory of SCARA robot.The trajectory tracking experiment of SCARA robot is designed to verify the tracking accuracy and stability of the controller.The simulation results show that the maximum tracking errors of SCARA robot joints 1 and 2are reduced by 92.6% and 95.5% respectively,and the root mean square of tracking errors are reduced by 22.64% and 31.79% respectively.It is verified that the algorithm can not only ensure the high tracking accuracy of SCARA robot,but also compensate the corresponding interference.