scholarly journals Adaptive Artificial Neural Network Surrogate Model of Nonlinear Hydraulic Adjustable Damper for Automotive Semi-Active Suspension System

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 118673-118686 ◽  
Author(s):  
Jingliang Lin ◽  
Haiyan Li ◽  
Yunbao Huang ◽  
Zeying Huang ◽  
Zhiqian Luo
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Surrogate Model ◽  
Active Suspension ◽  
Suspension System ◽  
Artificial Neural

Heavy trucks with intelligent control of active suspension based on artificial neural networks

2020 ◽  
pp. 095965182095851
Author(s):  
Anis Hamza ◽  
Noureddine Ben Yahia
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Intelligent Control ◽  
Research Work ◽  
Active Suspension ◽  
Suspension System ◽  
International Organization ◽  
International Organization For Standardization ◽  
Neural Network Controller ◽  
Artificial Neural

The active control of a suspension system is meant to provide an isolated behavior of the system spring-mass (for example, increased comfort and performance). During this article, we are going to explain the importance of developing an intelligent control approach for active truck suspensions based on the artificial neural network. From where the main objective of this article is to obtain a mathematical model for active suspension systems then build a hydraulic model for active suspension control for trucks using an artificial neural network. In this article, a corresponding artificial neural network nonlinear active suspension controller has been designed and optimized for approximate road profiles, using simulation according to International Organization for Standardization 2631-5 and International Organization for Standardization 8608 standardizations. The model developed with MATLAB Toolbox, estimated and validated from data collected during tests carried out with a truck in other research work. To model the system, the laws of physics are used to describe the system and experimental data or information supplied about the system to determine the parameters of the system. The statement of the problem of this research is to develop a robust artificial neural network controller for the nonlinear active suspension system of the heavy truck that can improve the performances and its verifications using graphical and simulation output. The results of the simulation show that the methodology offers excellent performance. In addition, the robustness of the artificial neural network hydraulic controller is demonstrated for a variety of road profiles that increase the capabilities of the proposed methodology and prove its effectiveness.

An Assessment of Machine Learning Techniques for Predicting Turbine Airfoil Component Temperatures, Using FEA Simulations for Training Data

2019 ◽  
Author(s):  
James A. Tallman ◽  
Michal Osusky ◽  
Nick Magina ◽  
Evan Sewall
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Surrogate Model ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Artificial Neural

Abstract This paper provides an assessment of three different machine learning techniques for accurately reproducing a distributed temperature prediction of a high-pressure turbine airfoil. A three-dimensional Finite Element Analysis thermal model of a cooled turbine airfoil was solved repeatedly (200 instances) for various operating point settings of the corresponding gas turbine engine. The response surface created by the repeated solutions was fed into three machine learning algorithms and surrogate model representations of the FEA model’s response were generated. The machine learning algorithms investigated were a Gaussian Process, a Boosted Decision Tree, and an Artificial Neural Network. Additionally, a simple Linear Regression surrogate model was created for comparative purposes. The Artificial Neural Network model proved to be the most successful at reproducing the FEA model over the range of operating points. The mean and standard deviation differences between the FEA and the Neural Network models were 15% and 14% of a desired accuracy threshold, respectively. The Digital Thread for Design (DT4D) was used to expedite all model execution and machine learning training. A description of DT4D is also provided.

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