Synchronous-substitution-type iterative learning control for discrete-time networked control systems with Bernoulli-type stochastic packet dropouts

The article develops an iterative learning control scheme for a class of repetitive discrete-time networked control systems with Bernoulli-type stochastic packet dropouts occurred in both the input and the output communication channels. Inspired by that the learning capacity is benefited from an appropriate compensation along the iteration direction, a synchronous substitution technique is proposed for handling the dropped instant-wise input and output data. Namely, the dropped instant-wise input and output are substituted by the synchronous data that are utilized at the previous iteration. By means of evaluating the tracking error in the sense of 1-norm in the form of mathematical expectation, the convergences of the developed learning scheme are derived for linear and nonlinear systems, respectively, which exhibit that the tracking errors vanish as the iteration goes on. Linear and nonlinear examples are respectively numerically simulated to testify the effectiveness and validity of the proposed scheme.