Filtered Iterative Learning Control of the Time-Delay Flexible Manipulator System with Slowly Varying Tracking Trajectory

In this paper, the filtered iterative learning control (FT-ILC) problem of a time-delay flexible manipulator system with variable tracking trajectory is studied. In practice, the system may be affected by random noise, which can reduce the control accuracy. In order to reduce the influence of noise on the system, this paper designs a filtered iterative learning law. At the same time, considering that the desired trajectory of the system is not fixed, but slowly changing, the learning law is extended to this situation. Through the compression mapping principle, the convergence of the time-delay flexible manipulator system is demonstrated. Finally, the numerical simulation results verify the effectiveness of the proposed algorithm.