Results 111 to 120 of about 81,148 (167)

Mask Optimization for High-Precision Extraction of Geometric Features in Microscopic Scenes. [PDF]

J Imaging
Kang T, Zhang J, Zhao X, Sun M, Zhang Y.
europepmc   +1 more source

Dual chaotic encryption method for wireless communication privacy data based on deep learning. [PDF]

PLoS One
Yu H.
europepmc   +1 more source

Some of the next articles are maybe not open access.

Related searches:

On initial conditions in iterative learning control

IEEE Transactions on Automatic Control, 2005
Initial conditions, or initial resetting conditions, play a fundamental role in all kinds of iterative learning control methods. In this note, we study five different initial conditions, disclose the inherent relationship between each initial condition and corresponding learning convergence (or boundedness) property.
Jian-Xin Xu
exaly   +5 more sources

Iterative Identification of Feedforward Controllers for Iterative Learning Control

IFAC Proceedings Volumes, 2009
Abstract One the the main disadvantages to the standard iterative learning control (ILC) problem is that the learning filter is static and does not update based on the current iteration information. This means that while the learning control is updating the feedforward control input, the learning controller itself is not updating.
Hoday Stearns, Benjamin Fine, Masayoshi Tomizuka   +2 more
openaire   +1 more source

Iterative learning of impedance control

Proceedings 1999 IEEE/RSJ International Conference on Intelligent Robots and Systems. Human and Environment Friendly Robots with High Intelligence and Emotional Quotients (Cat. No.99CH36289), 2003
This paper proposes an iterative learning control scheme for impedance control of robotic tasks when the tool endpoint covered by soft and deformable material presses a rigid object or environment at a prescribed periodic force pattern. To this end, an iterative learning control scheme for a class of linear dynamical systems with a negative feedback ...
Pham Thuc Anh Nguyen, Hyun-Yong Han, Suguru Arimoto, Sadao Kawamura   +3 more
openaire   +1 more source

Intermittent iterative learning control

2006 IEEE Conference on Computer Aided Control System Design, 2006 IEEE International Conference on Control Applications, 2006 IEEE International Symposium on Intelligent Control, 2006
In this paper we present a mathematical formulation of the problem of robust iterative learning control (ILC) design when the system is subject to data dropout. It is assumed that an ILC scheme is implemented via a networked control system (NCS) and that during the data transfer from the remote plant to the ILC controller data dropout occurs, resulting
Hyo-sung Ahn, Yangquan Chen, Kevin Moore
openaire   +1 more source

Iterative Learning in Ballistic Control

2007 American Control Conference, 2007
In this paper, we formulate and explore the characteristics of iterative learning in ballistic control problems. The ILC theory provides a suitable framework for derivations and analysis of ballistic control under learning process. To overcome the obstacles caused by uncertain gradient and redundant control input, we incorporate extra trials into ...
Jian-Xin Xu 0001, Wei Wang 0365, Deqing Huang   +2 more
openaire   +1 more source

Predictive Iterative Learning Control

Intelligent Automation & Soft Computing, 2003
There are applications in which the control system is commanded to execute identical and repetitive tasks. These include pick-and-place robot control, moulding control, and many semiconductor processes. The iterative learning control (ILC) method first introduced by Arimoto et al. (1984) is based on the previous control history and a learning mechanism.
Sunan Huang 0001, Ser Yong Lim
openaire   +1 more source

Parameter optimisation in iterative learning control

2003 European Control Conference (ECC), 2003
In this paper parameter optimization through a quadratic performance index is introduced as a method to establish a new iterative learning control law. With this new algorithm, monotonic convergence of the error to zero is guaranteed if the original system is a discrete-time LTI system and it satisfies a positivity condition.
David H. Owens 0001, K. Feng
openaire   +1 more source

An iterative learning controller for nonholonomic robots

Proceedings of IEEE International Conference on Robotics and Automation, 2002
We present an iterative learning controller for nonholonomic systems in chained form. The learning algorithm relies on the fact that chained-form systems are linear under piecewise-constant inputs. The proposed control scheme requires the execution of a small number of experiments in order to drive the system to the desired state in finite time, with ...
ORIOLO, Giuseppe, Stefano Panzieri, Giovanni Ulivi   +2 more
openaire   +2 more sources