Application of Multidisciplinary Optimization and Artificial Intelligence Techniques to Aerospace Engineering (Volume II)

A special issue of Aerospace (ISSN 2226-4310). This special issue belongs to the section "Aeronautics".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 2828

Special Issue Editor

School of Engineering, University of Central Lancashire, Preston PR1 2HE, UK
Interests: aerospace engineering; multidisciplinary optimization; machine learning; data science; artificial intelligence; space robotics; UAVs
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Special Issue Information

Dear Colleagues,

Multidisciplinary Optimization (MDO) and Artificial Intelligence (AI)/Machine Learning (ML) play an increasingly important role in aerospace applications. All this is particularly true in space and modern aerospace engineering, where a variety of technological opportunities have arisen, each requiring novel approaches and algorithms to address the corresponding technological challenges. The aerospace industry presents several unique opportunities and challenges for the integration of data intensive analysis techniques. These methods hold the promise of bringing new possibilities to the development of new robotic platform designs, multisensory navigation and space exploration approaches to previously unsolvable problems. With the advancement in AI, robotics and unmanned aerial vehicle (UAV) technology, ML has emerged as a viable technology for solving constrained multi-objective optimization problems (CMOPs). Aided by advances in hardware and algorithms, modern ML is poised to enable this optimization, allowing a much broader and integrated perspective.

The goal of this Special Issue is to illustrate applications of Multidisciplinary Optimization, Machine Learning and Artificial Intelligence methods to problems in Aerospace Engineering. Novel ML/AI algorithms and/or application of existing approaches to problems involving space exploration, UAV operations, space robotics and other fields of aerospace engineering will be examined.

Topics of interest include but are not limited to:

  • Machine Learning and Artificial Intelligence for Space and Aerospace Applications;
  • Multi-objective Optimization for Space and Aerospace Applications;
  • Autonomous Agents and Multiagent Systems in Aerospace Applications;
  • UAV Trajectory Optimization Using Machine Learning;
  • Energy-efficient UAV Communications;
  • Robotics, Perception and Vision in Aerospace Applications;
  • Intelligent Control for Space and Aerospace Systems;
  • Big Data, Machine Learning and Data Mining in Aerospace Applications;
  • Planning and Scheduling for Autonomous Systems;
  • Data Processing and Satellite Applications;
  • Bio-inspired Solutions for System Design and Control;
  • AI for Air and Space Traffic Management and Operations.

Dr. Jules Simo
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Aerospace is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • aerospace engineering
  • multidisciplinary optimization
  • artificial intelligence
  • machine learning
  • data science
  • UAVs

Related Special Issue

Published Papers (2 papers)

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Research

21 pages, 14886 KiB  
Article
Bayesian Optimization for Fine-Tuning EKF Parameters in UAV Attitude and Heading Reference System Estimation
by Assefinew Wondosen, Yisak Debele, Seung-Ki Kim, Ha-Young Shi, Bedada Endale and Beom-Soo Kang
Aerospace 2023, 10(12), 1023; https://doi.org/10.3390/aerospace10121023 - 09 Dec 2023
Viewed by 1292
Abstract
In various applications, the extended Kalman filter (EKF) has been vital in estimating a vehicle’s translational and angular motion in 3-dimensional (3D) space. It is also essential for the fusion of data from multiple sensors. However, for the EKF to perform effectively, the [...] Read more.
In various applications, the extended Kalman filter (EKF) has been vital in estimating a vehicle’s translational and angular motion in 3-dimensional (3D) space. It is also essential for the fusion of data from multiple sensors. However, for the EKF to perform effectively, the optimal process noise covariance matrix (Q) and measurement noise covariance matrix (R) must be chosen correctly. The use of EKF has been challenging due to the need for an easy mechanism to select Q and R values. As a result, this research focused on developing an algorithm that can be easily applied to determine Q and R, allowing us to harness the full potential of EKF. Accordingly, an EKF innovation consistency statistics-driven Bayesian optimization algorithm was employed to achieve this goal. Q and R values were tuned until the expected result met the performance requirement for minimum error through improved measurement innovation consistency. The comprehensive results demonstrate that when the optimum Q and R, as tuned by the suggested technique, were used, the performance of the EKF significantly improved. Full article
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19 pages, 4287 KiB  
Article
A Study on the Optimal Design Method for Star-Shaped Solid Propellants through a Combination of Genetic Algorithm and Machine Learning
by Seok-Hwan Oh, Tae-Seong Roh and Hyoung Jin Lee
Aerospace 2023, 10(12), 979; https://doi.org/10.3390/aerospace10120979 - 22 Nov 2023
Viewed by 1009
Abstract
This study was focused on the configuration design of a star grain by using machine learning in the optimal design process. The key to optimizing the grain design is aimed at obtaining a set of configuration variables that satisfy the requirements. The optimization [...] Read more.
This study was focused on the configuration design of a star grain by using machine learning in the optimal design process. The key to optimizing the grain design is aimed at obtaining a set of configuration variables that satisfy the requirements. The optimization problem consists of an objective area profile subject to certain constraints and an objective function that quantitatively calculates the design level. Designers must formulate suitable optimization problems to achieve an optimal design. However, because a method to alleviate the influence of the sliver section is not yet available, the optimization problem is typically solved based on experience, which is time- and effort-intensive. Consequently, a more practical and objective grain design method must be developed. In this study, an optimal design method using machine learning was developed to increase the convenience and success rate. A support vector machine was used to train a classification model that predicts a class. The classification model was used to alleviate the influence of the sliver zone and correct the search problem to ensure that an optimal solution existed in the region satisfying the requirements. The proposed method was validated through star grain optimal design using the genetic algorithm. The optimization was performed considering the area profiles, and the effectiveness of the proposed method was demonstrated by the enhanced accuracy. Full article
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