On initial conditions in iterative learning control

2006 ◽  
Author(s):  
Jian-Xin Xu ◽  
Rui Yan ◽  
YangQuan Chen
Keyword(s):  
Iterative Learning Control ◽  
Initial Conditions ◽  
Learning Control ◽  
Iterative Learning

Spatial Iterative Learning Control for Multi-material Three-Dimensional Structures

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Zahra Afkhami ◽  
Christopher Pannier ◽  
Leontine Aarnoudse ◽  
David Hoelzle ◽  
Kira Barton
Keyword(s):  
Additive Manufacturing ◽  
Iterative Learning Control ◽  
Initial Conditions ◽  
Three Dimensional ◽  
Single Layer ◽  
Learning Control ◽  
Iterative Learning ◽  
Layer By Layer ◽  
Additive Process ◽  
Jet Printing

Abstract Iterative learning control (ILC) is a powerful technique to regulate repetitive systems. Additive manufacturing falls into this category by nature of its repetitive action in building three-dimensional structures in a layer-by-layer manner. In literature, spatial ILC (SILC) has been used in conjunction with additive processes to regulate single-layer structures with only one class of material. However, SILC has the unexplored potential to regulate additive manufacturing structures with multiple build materials in a three-dimensional fashion. Estimating the appropriate feedforward signal in these structures can be challenging due to iteration varying initial conditions, system parameters, and surface interaction dynamics in different layers of multi-material structures. In this paper, SILC is used as a recursive control strategy to iteratively construct the feedforward signal to improve part quality of 3D structures that consist of at least two materials in a layer-by-layer manner. The system dynamics are approximated by discrete 2D spatial convolution using kernels that incorporate in-layer and layer-to-layer variations. We leverage the existing SILC models in literature and extend them to account for the iteration varying uncertainties in the plant model to capture a more reliable representation of the multi-material additive process. The feasibility of the proposed diagonal framework was demonstrated using simulation results of an electrohydrodynamic jet printing (e-jet) printing process.

Robust point‐to‐point iterative learning control with trial‐varying initial conditions

2020 ◽  
Vol 14 (19) ◽  
pp. 3344-3350
Author(s):  
Hongfeng Tao ◽  
Jian Li ◽  
Yiyang Chen ◽  
Vladimir Stojanovic ◽  
Huizhong Yang
Keyword(s):  
Iterative Learning Control ◽  
Initial Conditions ◽  
Learning Control ◽  
Iterative Learning ◽  
Point To Point

scholarly journals Robust Iterative Learning Control for a Class of Linear Systems with Data Dropouts

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Wang Rui ◽  
Ma Xinghe ◽  
Bu Xuhui
Keyword(s):  
Iterative Learning Control ◽  
Control Algorithm ◽  
Contraction Mapping ◽  
Initial Conditions ◽  
Learning Control ◽  
Convergence Condition ◽  
Iterative Learning ◽  
Control Input ◽  
Initial Condition ◽  
Data Dropouts

A robust iterative learning control algorithm is proposed for a class of intermittent systems with disturbances and uncertain initial conditions. Based on the contraction mapping approach, the convergence condition for the proposed algorithm is first given, and then the bounds on control input and output trajectories can be obtained. It is shown that these bounds depend on bounds on the initial condition errors and disturbances, and the bounds are zero in the absence of these disturbances. A numerical example is also given to verify the theoretical result.

Sign in / Sign up

Export Citation Format

Share Document