Two updating schemes of iterative learning control for networked control systems with random data dropouts

The iterative learning control (ILC) problem is addressed in this paper for stochastic linear systems with random data dropout modeled by a Bernoulli random variable. Both intermittent updating scheme and successive updating scheme are provided on the basis of the available tracking information only and shown to be convergent to the desired input almost certainly. In the intermittent updating scheme, the algorithm only updates its control signal when data is successfully transmitted. In the successive updating scheme, the algorithm continuously updates its control signal with the latest available data in each iteration whether the output information of the last iteration is successfully transmitted or lost. Illustrative simulations verify the convergence and effectiveness of the proposed algorithms.