Iterative Learning Control for Time-Varying Systems Based on Extreme Learning Machine

In the actual production and life process, there are a plenty of time-varying linear systems. In this paper, ILC is extended to linear time-varying systems, and the proposed ILC update law is adopted to update the time-varying model, and the convergence performance of the algorithm is proved. Simpson integral method has an advantage in calculating the variable parameters to solve time-varying systems. Combining the extreme learning machine(ELM) in machine learning with ILC, the regression technology is employed to train the model to update the time-varying model parameters over time. Finally, the feasibility of the proposed algorithm is demonstrated by simulation.