A Multi-Input Single-Output iterative learning control for improved material placement in extrusion-based additive manufacturing

2021 ◽  
Vol 111 ◽  
pp. 104783
Author(s):  
Ashley A. Armstrong ◽  
Andrew G. Alleyne
Keyword(s):  
Additive Manufacturing ◽  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning ◽  
Single Output

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.

scholarly journals Multi-Layer Spatial Iterative Learning Control for Micro-Additive Manufacturing

2019 ◽  
Vol 52 (15) ◽  
pp. 97-102 ◽  
Author(s):  
Leontine Aarnoudse ◽  
Christopher Pannier ◽  
Zahra Afkhami ◽  
Tom Oomen ◽  
Kira Barton
Keyword(s):  
Additive Manufacturing ◽  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning

Resistance based iterative learning control of additive manufacturing with wire

Mechatronics ◽  
2015 ◽  
Vol 31 ◽  
pp. 116-123 ◽  
Author(s):  
Petter Hagqvist ◽  
Almir Heralić ◽  
Anna-Karin Christiansson ◽  
Bengt Lennartson
Keyword(s):  
Additive Manufacturing ◽  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning

Iterative Learning Control for a Type of Modified Smith Predictor

2020 ◽  
Vol 142 (4) ◽  
Author(s):  
Dongzuo Tian ◽  
Xingyong Song
Keyword(s):  
Iterative Learning Control ◽  
Tracking Error ◽  
Learning Control ◽  
Iterative Learning ◽  
Smith Predictor ◽  
Single Input Single Output ◽  
Single Output ◽  
Modified Smith Predictor ◽  
Frequency Domain Techniques ◽  
The Impact

Abstract This article proposes a novel iterative learning control (ILC) design for a type of modified Smith predictor, in particular, to control a single-input single-output unstable plant or integral process with a time delay. Frequency domain techniques are applied to synthesize the learning control law, and a sufficient condition is given to ensure robust convergence of the tracking error. Robustness of the system is studied, considering a multiplicative uncertainty. Moreover, the impact of the load disturbance over successive iterations is investigated as well. To this end, a numerical example is given to demonstrate the efficacy of the proposed approach.

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