Results 311 to 320 of about 816,103 (361)

An iteration-domain filter for controlling transient growth in Iterative Learning Control

Proceedings of the 2010 American Control Conference, 2010
Transient growth is a problem in Iterative Learning Control (ILC) in which the tracking error temporarily grows very large during the learning process, before converging to a small value. While some ILC algorithms can guarantee monotonic convergence, there are limitations when the model is uncertain.
Qing Liu, Douglas A. Bristow
openaire   +1 more source

Iterative Learning Control for a Flapping Wing Micro Aerial Vehicle Under Distributed Disturbances

IEEE Transactions on Cybernetics, 2019
This paper addresses a flexible micro aerial vehicle (MAV) under spatiotemporally varying disturbances, which is composed of a rigid body and two flexible wings.
Wei He, Tingting Meng, Xiuyu He, Changyin Sun   +3 more
semanticscholar   +1 more source

Vibration Suppression of a High-Rise Building With Adaptive Iterative Learning Control

IEEE Transactions on Neural Networks and Learning Systems, 2021
This article considers the design of an adaptive iterative learning controller for high-rise buildings with active mass dampers (AMDs). High-rise buildings in this article are seen as distributed parameter systems, in which the characteristics of every ...
Jiali Feng, Zhijie Liu, Xiuyu He, Qing Li, Wei He   +4 more
semanticscholar   +1 more source

Optimality and flexibility in Iterative Learning Control for varying tasks [PDF]

Automatica, 2016
Iterative Learning Control (ILC) can significantly enhance the performance of systems that perform repeating tasks. However, small variations in the performed task may lead to a large performance deterioration. The aim of this paper is to develop a novel
Jürgen Van Zundert, Joost Bolder, Tom Oomen   +2 more
exaly   +2 more sources

Stochastic Iterative Learning Control for Lumped- and Distributed-Parameter Systems: A Wiener-Filtering Approach

IEEE Transactions on Automatic Control, 2021
This article presents a stochastically optimal iterative learning control (ILC) approach by designing a general integral learning operator which minimizes the expected mean-squares output error.
A. Deutschmann‐Olek, G. Stadler, A. Kugi   +2 more
semanticscholar   +1 more source

On initial conditions in iterative learning control

2006 American Control Conference, 2006
Initial conditions, or initial resetting conditions, play a fundamental role in all kinds of iterative learning control methods. In this work we study five different initial conditions, disclose the inherent relationship between each initial condition and corresponding learning convergence (or boundedness) property.
Jian-Xin Xu 0001, Rui Yan, YangQuan Chen
openaire   +1 more source

Consensus tracking for nonlinear multi-agent systems with unknown disturbance by using model free adaptive iterative learning control

Applied Mathematics and Computation, 2020
In this paper, a distributed disturbance-compensation based model free adaptive iterative learning control (MFAILC) algorithm is proposed to achieve the consensus tracking of nonlinear multi-agent systems (MAS) with unknown disturbance.
Yingchun Wang, Haifeng Li, Xiaojie Qiu, Xiangpeng Xie   +3 more
semanticscholar   +1 more source

An iterative learning control scheme for manipulators

Proceedings of the 1997 IEEE/RSJ International Conference on Intelligent Robot and Systems. Innovative Robotics for Real-World Applications. IROS '97, 2002
This paper presents an iterative learning control scheme for high-geared industrial manipulators that perform repeated tasks. The input update law of the iterative learning control is given in the frequency domain, together with a sufficient condition for convergence of the iterative process.
Jung-Ho Moon, Tae-Yong Doh, Myung Jin Chung   +2 more
openaire   +1 more source

An iterative learning control of robot manipulators

IEEE Transactions on Robotics and Automation, 1991
An iterative learning scheme comprising a unique feedforward learning controller and a linear feedback controller is presented. In the feedback loop, the fixed-gain PD controller provides a stable open neighborhood along a desired trajectory. In the feedforward path, on the other hand, a learning control strategy is exploited to predict the desired ...
KUC, TY, LEE, JS, NAM, KH
openaire   +2 more sources

Multiple model iterative learning control

Neurocomputing, 2010
Iterative learning controller (ILC), which is based on the model of the system, can give good performance in the steady state if the input is updated from trial to trial. The model, on which the ILC is set up, is not always needed to be exact. But a better model will lead to the deduction of iteration times.
Xiaoli Li 0011, Wen Zhang
openaire   +1 more source