Iterative learning control with high-order internal model for first-order hyperbolic systems.

This paper studies the iterative learning control (ILC) algorithm for first-order hyperbolic systems. Unlike most of the ILC literature of distributed parameter systems, in the iteration domain, that require identical desired trajectories. Here the desired trajectories are iteratively varying and described by a high-order internal model (HOIM).

Pareto-Optimal Strategy for Linear Mean-Field Stochastic Systems With H Constraint.

This article presents results on designing the Pareto-optimal strategy under H constraint for the linear mean-field stochastic systems disturbed by external disturbances. First, combining the stochastic H control theory with the stochastic mean-field theory, we derive the stochastic bounded real lemma (SBRL) of our considered linear mean-field stochastic systems with the stochastic initial condition. Second, we use the mean-field forward-backward stochastic differential equation to solve the mean-field linear quadratic Pareto-optimal problem with indefinite cost functionals.

H control for time-delay systems with randomly occurring nonlinearities subject to sensor saturations, missing measurements and channel fadings.

The H control problem for a class of time-delay systems with randomly occurring nonlinearities (RONs) is addressed in this paper. Sensor saturations, missing measurements and channel fadings are governed by random variables obeying the Bernoulli distributions. The measurement output is subject to both data missing and randomly occurring sensor saturations (ROSSs) described by sector-nonlinearities as well as the channel fadings caused typically in wireless communication.