Study On Iterative Learning Control For Harnessing The Nonrepetitive Disturbance

Iterative learning control (ILC) is applied to the repetitive characteristics industrial control process. Current control information is revised in ILC by using the former control experience and the output errors of the system. The system output can track of a desired trajectory at a high precision. However, the non-repetitiveness always existed in the actual system and traditional iterative learning control algorithm cannot harness it well. So, the problem about the non-repetitive disturbance in ILC is investigated.The research work is introduced as follows:First, the basic theory of the iterative learning control is introduced. Then The development history, the present development situation at home and abroad and the existing problems of ILC in recent years are summed up, also. Second, the different architecture with predecessor is proposed.This architecture can improve the tracking performance of ILC. The architecture is a series connection of feedback controller and iterative learning controller.The performance of the series connection architecture is analyzed,and the results are compared. The series connection of feedback controller and iterative learning control is confirmed effectiveness by the simulation experiment. Third, non-repetitive disturbance in ILC is major problem. This problem is solved by disturbance observer (DOB). The basic framework of DOB is introduced. The performance of DOB,ILC and the whole structure are analyzed. To guarantee the stability of system, DOB can inhibit non-repetitive disturbance. The Simulink can verify this conclusion. At last, the scheme is designed to combining the series connection of feedback controller and ILC with DOB. Then The performance of system is analyzed. The designing system is proved to be effective for promoting tracking performance,and The non-repetitive disturbance in ILC is inhibited. The effectiveness is verified by the result of simulation.