The robust iterative learning control of networked control systems with varying references

In this paper, the iterative learning control (ILC) is applied to the networked control system (NCS) with iterative varying references and time-delayed states. A robust PD type ILC learning law is discussed including both feedback and feedforward terms. And a P type reference updating law is applied to improve the convergence speed. The convergence conditions are derived in both frequency and time domains. The learning gains in the updating law guarantee the convergence of the control strategy, and can be adjusted to improve the convergence speed to a large extent. A number of simulation results are provided to validate the concepts and the tuning rules are summarized as well.