Real-Time Nonlinear Tracking Control of Photopolymerization for Additive Manufacturing

2021 ◽  
Author(s):  
Koen Classens ◽  
Thomas Hafkamp ◽  
Steyn Westbeek ◽  
Joris J.C. Remmers ◽  
Siep Weiland
Keyword(s):  
Additive Manufacturing ◽  
Real Time ◽  
Tracking Control ◽  
Nonlinear Tracking

Nonlinear Tracking Control of Spacecraft with MPC: LPV Approach

2016 ◽  
Vol 15 (0) ◽  
pp. 133-140
Author(s):  
Atsushi SAKAMOTO ◽  
Yuichi IKEDA ◽  
Isao YAMAGUCHI ◽  
Takashi KIDA
Keyword(s):  
Tracking Control ◽  
Nonlinear Tracking

Application of FBG Technology in Additive Manufacturing: Monitoring Real-time Internal Temperature of Products

2020 ◽  
pp. 1-1
Author(s):  
Chengyu Hong ◽  
Chengzhi Bao ◽  
Jianbo Fei ◽  
Yifan Zhang ◽  
Xiaodong Wang
Keyword(s):  
Additive Manufacturing ◽  
Real Time ◽  
Internal Temperature

scholarly journals An Adaptive RBF-NMPC Architecture for Trajectory Tracking Control of Underwater Vehicles

Machines ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 105
Author(s):  
Zhenzhong Chu ◽  
Da Wang ◽  
Fei Meng
Keyword(s):  
Model Predictive Control ◽  
Prediction Model ◽  
Real Time ◽  
Predictive Control ◽  
Trajectory Tracking ◽  
Tracking Control ◽  
Working Environment ◽  
Gray Wolf ◽  
Trajectory Tracking Control ◽  
System Output

An adaptive control algorithm based on the RBF neural network (RBFNN) and nonlinear model predictive control (NMPC) is discussed for underwater vehicle trajectory tracking control. Firstly, in the off-line phase, the improved adaptive Levenberg–Marquardt-error surface compensation (IALM-ESC) algorithm is used to establish the RBFNN prediction model. In the real-time control phase, using the characteristic that the system output will change with the external environment interference, the network parameters are adjusted by using the error between the system output and the network prediction output to adapt to the complex and uncertain working environment. This provides an accurate and real-time prediction model for model predictive control (MPC). For optimization, an improved adaptive gray wolf optimization (AGWO) algorithm is proposed to obtain the trajectory tracking control law. Finally, the tracking control performance of the proposed algorithm is verified by simulation. The simulation results show that the proposed RBF-NMPC can not only achieve the same level of real-time performance as the linear model predictive control (LMPC) but also has a superior anti-interference ability. Compared with LMPC, the tracking performance of RBF-NMPC is improved by at least 43% and 25% in the case of no interference and interference, respectively.

A Novel Real-Time Thermal Analysis and Layer Time Control Framework for Large Scale Additive Manufacturing

2020 ◽  
Author(s):  
Sepehr Fathizadan ◽  
Feng Ju ◽  
Kyle Rowe ◽  
Alex Fiechter ◽  
Nils Hofmann
Keyword(s):  
Additive Manufacturing ◽  
Surface Temperature ◽  
Real Time ◽  
Large Scale ◽  
Production Efficiency ◽  
Control Method ◽  
Data Extraction ◽  
Critical Role ◽  
Imaging Data ◽  
Time Data

Abstract Production efficiency and product quality need to be addressed simultaneously to ensure the reliability of large scale additive manufacturing. Specifically, print surface temperature plays a critical role in determining the quality characteristics of the product. Moreover, heat transfer via conduction as a result of spatial correlation between locations on the surface of large and complex geometries necessitates the employment of more robust methodologies to extract and monitor the data. In this paper, we propose a framework for real-time data extraction from thermal images as well as a novel method for controlling layer time during the printing process. A FLIR™ thermal camera captures and stores the stream of images from the print surface temperature while the Thermwood Large Scale Additive Manufacturing (LSAM™) machine is printing components. A set of digital image processing tasks were performed to extract the thermal data. Separate regression models based on real-time thermal imaging data are built on each location on the surface to predict the associated temperatures. Subsequently, a control method is proposed to find the best time for printing the next layer given the predictions. Finally, several scenarios based on the cooling dynamics of surface structure were defined and analyzed, and the results were compared to the current fixed layer time policy. It was concluded that the proposed method can significantly increase the efficiency by reducing the overall printing time while preserving the quality.

Layered Image Collection for Real-Time Defective Inspection in Additive Manufacturing

2021 ◽  
Author(s):  
Jinwoo Song ◽  
Harika Bandaru ◽  
Xinyu He ◽  
Zhenyang Qiu ◽  
Young Moon
Keyword(s):  
Additive Manufacturing ◽  
Real Time ◽  
Image Collection
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