Research On Path Tracking Control Of Manipulator Based On Iterative Learning

Manipulators are widely used in handling,welding,spraying,automobile assembly and spinning mills,such as batch and large-scale manufacturing and repetitive flow operations.With the rapid advancement of industrialization,advanced industrial production lines have put forward higher requirements for precise control of robotic arms.For example,robotic arms must ensure sufficiently high-precision control performance even in the presence of measurement noise,external interference and parameter uncertainty.The iterative learning control method has the characteristics of clear control structure,full use of prior information,repeated control process,etc.,which helps to improve the joint position convergence speed and path tracking accuracy of the robotic arm system,and can effectively solve the robotic arm in the process of repetitive operations.There are problems such as uncertainty of control parameters and repeated disturbances from the outside world.The main purpose of this paper is to improve the path tracking control accuracy of the manipulator system joints and the system convergence speed.The main research contents are as follows:(1)Research the basic theory of manipulator control system based on iterative learning.The theoretical basis and application of iterative learning control and adaptive control are analyzed;according to the structural characteristics and working principles of the manipulator system,the Lagrange method is used to analyze the dynamics and solve the dynamic equation of the manipulator,and establish a multi-joint(N degrees of freedom)The dynamic model of the robotic arm system.(2)Aiming at the problem of unknown parameters and repeated interference in the robotic arm system,a variable gain iterative learning control combining the feedback PD control law with gain conversion technology and the feedforward learning control law with input torque curve is proposed.Algorithm,and based on MATLAB to compare the proposed algorithm with existing research examples.The experimental results show that the convergence speed and tracking accuracy of this method are better than the variable gain iterative learning control method proposed in the literature.(3)Aiming at the path tracking problem of the robotic arm with uncertain disturbances in the task space,this paper adopts the design idea of combining iterative learning control and adaptive control,drawing on the existing research results of variable gain iterative learning control,and presents the variable gain automatic Adapt to the iterative learning control algorithm,and use the Lyapunov function to analyze the convergence of the algorithm.Through comparison of simulation experiments,it is verified that the variable gain adaptive iterative learning control method in this paper has better stability and robustness.