Iterative Learning Control For Industrial Robots

With the stimulation of industrial demand and the incentive of national policy,industrial robots have become the core role of the production line.The common form of production is mass production,large-scale manufacturing and repetitive flow shop,such as grinding,cutting,picking,placing,welding and spraying.Therefore,it is very important to study the trajectory control of industrial robots.Iterative learning control is a research method for the repeated work of industrial robots.It has the characteristics of clear structure and a priori information,which can improve the tracking precision of the robot trajectory.However,the problems of uncertainty and external disturbance still need to be further studied.In this dissertation,the iterative learning control of industrial robot s is studied.The design and application of iterative learning controller are studied for the disturbance and uncertain parameters of trajectory tracking control of robot s,and the performance index is analyzed and verified by experiments.In order to clarify the working principle and characteristics of the robot,the dynamic analysis of the industrial robot is carried out.The dynamic model of the manipulator is established by Lagrange method,and the dynamic model of the Delta robot is built by the principle of virtual work.On the basis of studying the iterative learning control principle,aiming at the chattering phenomenon in the error convergence process,the input compensation and the error compensation on the iterative axis are added based on the P-type learning law.The convergence smoothness of the effect of tracking control is improved,and the effectiveness of the algorithm is verified by simulation on the manipulator.Aiming at the trajectory tracking problem of rigid robot with uncertain parameter mod el,an adaptive iterative learning controller is designed by combining the adaptive control with iterative learning control.The simulation is applied to the manipulator to verify the tracking error.The problems of iterative learning control applied to the robot are analyzed.The robustness and convergence are used as the criteria to evaluate the performance of iterative learning control.On this basis,two evaluation indexes,robustness and convergence,are designed.The performance of the three different iterative learning control algorithms : P-type,PD-type and adaptive type on the manipulator is tested by the designed error index.The relationship and regularity between the robustness and convergence of the iterative learning algorithm are analyzed,which provides the basis for the design of iterative learning law and the adjustment of gain parameters,and extends the practicability of iterative learning control.Finally,two experimental platforms are selected to carry out experiments,from the two-dimensional experimental platform XY platform to the three-dimensional three-dimensional experimental platform Delta robot.The experimental results show that the algorithm can achieve accurate trajectory tracking for the actual industrial robot.