Research On The Manipulator Of Path Planning Method With Iterative Learning Control

With the rapid development of modern manufacturing industry in China,many control technologies have been widely used in complex systems such as industrial robots.At present,most scholars have applied iterative learning control theory to complex industrial robots.It has become a hot topic.This paper establishes the Lagrangian dynamics model of industrial robots.Aiming at the uncertainties of industrial robots,an iterative learning controller with disturbance is designed,which provides a theoretical basis and method for the study of robot path planning and trajectory tracking.Therefore,the paper has important theoretical research significance and application value.In this paper,the two-degree-of-freedom manipulator is used as the research object,and the related dynamics model is established.The iterative learning controller is designed and the two-degree-of-freedom manipulator system is used for path planning.The main research contents of this paper include:Firstly,the dynamic model of industrial robot is analyzed,and the La grange modeling method is used.Secondly,the theoretical basis of time-varying inverse matrix of Zhang Neural Network is introduced,The design method of ZNN model is summarized,which lays a foundation for the subsequent stability proof,and briefly explains the basic block diagram of the control law of D-type and PID-type iterative learning control.Finally,introducing the Bellman-Gronwall theorem in two forms,which paves the way for the convergence of iterative learning control theory.Secondly,this paper presents a scheme for solving the highly nonlinear shortest path problem.Inspired by the optimality principle of Bellman equation dynamic programming,the shortest path problem is expressed as a linear equation,and the corresponding linear equation is solved by tense neural network.The shortest path is found during the movement of a two-degree-of-freedom manipulator.We first carried out theoretical analysis,and then use Lyapunov stability theory to prove the asymptotic stability of the neural network algorithm.In order to ensure that the proposed scheme can solve the performance of the shortest path.In addition,illustrative examples are provided to verify the proposed scheme and theoretical results.Lastly,with the two-degree-of-freedom manipulator nonlinear system,iterative learning control has better control effect on the mechanical arm with repetitive motion characteristics.In the case of disturbance,a PD-type iterative learning control law is designed.With the increasing number of iterations of the system,the required correction interval is shortened by modifying the gain matrix in real time in the interval,so as to accelerate the convergence speed.The simulation results show that the convergence speed of PD-type iterative learning control is faster than that of P-type iterative learning control.The PD-type iterative learning control with perturbation has better convergence effect than the traditional perturbed PD control.Industrial robot systems are also guaranteed to have good dynamic performance.