The nonuniform trial length problem, which causes information dropout in learning, is very
common in various control systems such as robotics and motion control systems. This paper …

This paper conducts a survey on iterative learning control (ILC) with incomplete information
and associated control system design, which is a frontier of the ILC field. The incomplete …

This paper contributes to a technical overview of recent progresses on stochastic iterative
learning control (ILC), where stochastic ILC implies the learning control for systems with …

This note proposes ILC for discrete-time affine nonlinear systems with randomly iteration
varying lengths. No prior information on the probability distribution of random iteration length …

This technical note contributes to the convergence analysis for iterative learning control
(ILC) for linear stochastic systems under general data dropout environments, ie, data …

The standard assumption that a measurement signal is available at each sample in iterative
learning control (ILC) is not always justified, eg, when exploiting time-stamped data from …

The equivalent model of a class of unknown discrete-time systems is developed by data-
driven approach and fuzzy rules inference network when plant's control direction can vary …

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 …

This paper proposes a data-driven learning control method for stochastic nonlinear systems
under random communication conditions, including data dropouts, communication delays …

Active control is one of the most important advantages of active magnetic bearing (AMB),
and also can be used to suppress the imbalance of AMB rotor system (AMB-RS). This paper …