Research On SCARA Trajectory Tracking Based On Exponential Variable Gain Iterative Learning

In this paper,the robot kinematics model and kinematics model are established for the SCARA robot model.The trajectory planning research is carried out.An iterative learning algorithm based on exponential variable gain is proposed to realize the tracking control of SCARA robot.The main research contents of this paper are as follows:(1)The SCARA robot is modeled by D-H parameter method,and the forward kinematics and inverse kinematics equations are derived.The correctness of the equation is verified by Matlab simulation.The dynamics modeling of SCARA robot is carried out by Lagrange method.In practice,the SCARA robot is appropriately simplified to obtain a simplified kinetic equation.(2)The joint space trajectory planning and Cartesian space trajectory planning are introduced.The advantages and disadvantages of the two trajectory planning are compared.The linear functions of cubic polynomial interpolation,fifth-order polynomial interpolation and parabolic transition commonly used in joint space trajectory planning are derivated and the simulation verifications are carried out.The linear programming and circular trajectory planning in Cartesian space are introduced,and the interpolation equations for linear programming and circular trajectory planning are derived.(3)Combining the respective advantages of P-type and D-type learning laws,an exponential variable gain PD-type iterative learning algorithm is proposed based on the traditional fixed gain PD-type learning law,and it is proved that the algorithm will eventually converge with the increase of the number of iterations.Finally,the exponential variable gain PD-type iterative learningalgorithm is applied to the trajectory tracking control of SCARA robot.The simulation results show that the exponential variable gain PD-type iterative learning algorithm has better tracking control effect compared with the traditional fixed gain PD-type learning law.