Algorithm Research Based on Network Iterative Control

Since it is increasingly necessary to control machines in industry, the goal of controlled objects with repetitive operating characteristics is to ideally track the desired input to meet the needs of industrial production. For the current research situation in practice, a new research direction of iterative learning control has been developed in control science and technology. In this paper, the basic concepts of forgetting factors and iterative control algorithms and learning laws of related simple system types are first explained, and then for linear stationary systems, a proof of PD type iterative learning control tracking expected output convergence with forgetting factors is given. Among them, the PD-type forgetting factor learning law is used to expand the narrative and algorithm proof, and then use MATLAB to simulate the expected trajectory tracking of a single input single output linear stationary system to ensure the correctness of the conclusion. Finally, the system is summarized and analyzed for different iterations and whether or not the control algorithm is forgotten.