This paper presents an adaptive iterative learning control scheme that is applicable to a class of nonlinear systems. The control scheme guarantees system stability and boundedness by using the feedback controller coupled with the fuzzy compensator and achieves precise tracking by using the iterative learning rules. In the feedback plus fuzzy compensator unit, the feedback control part stabiliz...

This letter presents a data-based control approach to achieve high-performance trajectory tracking with Unmanned Aerial Vehicles (UAVs). We revisit an existing Iterative Learning Control (ILC) algorithm based on the notion that performance of system executes same task multiple times can be improved by learning from previous executions. While we will specifically refer multirotor platforms for e...

In this paper, an iterative learning controller applying to linear discrete-time multivariable systems with variable initial conditions is investigated based on two-dimensional (2-D) system theory. The paper first introduces a 2-D tracking error system, and shows the effect of tracking errors against variable initial conditions. The sufficient conditions for the convergence of the learning cont...

Machine learning methods are often used to classify objects described by hundreds of attributes; in many applications of this kind a great fraction of attributes may be totally irrelevant to the classification problem. Even more, usually one cannot decide a priori which attributes are relevant. In this paper we present an improved version of the algorithm for identification of the full set of t...

This paper focuses on the iterative learning tracking control problem for a class of nonlinear system with time-varying disturbances. First, because of the mismatches in time-varying disturbances functions, a high-order feed-forward iterative learning control (ILC) is employed to change the original system into an iterative system. Secondly, a variable forgetting factor is developed to stabiliz...

Databases for machine learning and data mining often have missing values. How to develop effective method for missing values imputation is an important problem in the field of machine learning and data mining. In this paper, several methods for dealing with missing values in incomplete data are reviewed, and a new method for missing values imputation based on iterative learning is proposed. The...

This paper presents an intelligent iterative learning control scheme that is applicable to a class of nonlinear systems. The presented controller guarantees system stability by using a feedback controller coupled with an intelligent compensator and achieves precise tracking by using a set of iterative learning rules. In the feedback plus intelligent controller unit, the feedback control part st...

Residual networks (Resnets) have become a prominent architecture in deep learning. However, a comprehensive understanding of Resnets is still a topic of ongoing research. A recent view argues that Resnets perform iterative refinement of features. We attempt to further expose properties of this aspect. To this end, we study Resnets both analytically and empirically. We formalize the notion of it...

This article develops iterative machine learning (IML) for output tracking. The inputoutput data generated during iterations to develop the model used in the iterative update. The main contribution of this article to propose the use of kernel-based machine learning to iteratively update both the model and the model-inversion-based input simultaneously. Additionally, augmented inputs with persis...