Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
3.4
2.1
Journals
Aerospace
Special Issues
Advanced GNC Solutions for VTOL Systems
share announcement

Advanced GNC Solutions for VTOL Systems

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

Deadline for manuscript submissions: closed (15 December 2024) | Viewed by 6037

Special Issue Editors

Dr. Haichao Hong

E-Mail Website
Guest Editor
School of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai, China
Interests: flight testing; trajectory optimization; flight mechanics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Florian Holzapfel

E-Mail Website
Guest Editor
Institute of Flight System Dynamics, Technical University of Munich, Munich, Germany
Interests: guidance and control of manned and unmanned aircrafts; simulation; parameter identification and flight safety; trajectory optimization; sensors, navigation and data fusion; avionics and safety critical systems
Special Issues, Collections and Topics in MDPI journals
Dr. Peter Chudý

E-Mail Website
Guest Editor
Faculty of Information Technology, Brno University of Technology, 60190 Brno, Czech Republic
Interests: aircraft design; flight control; guidance; navigation; autonomous systems; machine perception; tracking & state estimation; system identification; pilot training; cyber-physical systems; user centered design; human machine interface; human factors; cognitive architectures
Prof. Dr. Shiqiang Hu

E-Mail Website
Guest Editor
School of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai, China
Interests: image understanding and analysis; autonomous technologies for unmanned systems; nonlinear filtering and state estimation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In an era of growing public interest in utilizing airspace and energy resources in an efficient and socially responsible manner, vertical take-off and landing (VTOL) systems are emerging as a viable solution for next-generation aerial transportation. With an eye towards propelling the advanced air mobility, visionary innovations in VTOL systems, such as novel hybrid configurations and highly distributed electric propulsion systems, are becoming a reality and standards are rapidly being iterated and refined by authorities. Such advances have resulted in an unprecedented need for new guidance, navigation, and control (GNC) technologies to address issues arising in the development, experiment, and operation of VTOL systems. These GNC solutions aim to address the lack of confidence in flight safety and other operational aspects.

Therefore, we are pleased to announce this Special Issue dedicated to state-of-the-art GNC solutions for VTOL systems. Papers addressing real-world operational problems are especially welcome.

We look forward to receiving your contributions.

Dr. Haichao Hong
Prof. Dr. Florian Holzapfel
Dr. Peter Chudý
Prof. Dr. Shiqiang Hu
Guest Editors

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

  • guidance
  • navigation
  • control
  • vertical take-off and landing
  • urban air mobility
  • advanced air Mobility
  • flight safety
  • electric aircraft

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

19 pages, 4427 KiB  
Article
Robust MPS-INS UKF Integration and SIR-Based Hyperparameter Estimation in a 3D Flight Environment
by Juyoung Seo, Dongha Kwon, Byungjin Lee and Sangkyung Sung
Aerospace 2025, 12(3), 228; https://doi.org/10.3390/aerospace12030228 - 11 Mar 2025
Viewed by 464
Abstract
This study introduces a pose estimation algorithm integrating an Inertial Navigation System (INS) with an Alternating Current (AC) magnetic field-based navigation system, referred to as the Magnetic Positioning System (MPS), evaluated using a 6 Degrees of Freedom (DoF) drone. The study addresses significant [...] Read more.
This study introduces a pose estimation algorithm integrating an Inertial Navigation System (INS) with an Alternating Current (AC) magnetic field-based navigation system, referred to as the Magnetic Positioning System (MPS), evaluated using a 6 Degrees of Freedom (DoF) drone. The study addresses significant challenges such as the magnetic vector distortions and model uncertainties caused by motor noise, which degrade attitude estimation and limit the effectiveness of traditional Extended Kalman Filter (EKF)-based fusion methods. To mitigate these issues, a Tightly Coupled Unscented Kalman Filter (TC UKF) was developed to enhance robustness and navigation accuracy in dynamic environments. The proposed Unscented Kalman Filter (UKF) demonstrated a superior attitude estimation performance within a 6 m coil spacing area, outperforming both the MPS 3D LS (Least Squares) and EKF-based approaches. Furthermore, hyperparameters such as alpha, beta, and kappa were optimized using the Sequential Importance Resampling (SIR) process of the Particle Filter. This adaptive hyperparameter adjustment achieved improved navigation results compared to the default UKF settings, particularly in environments with high model uncertainty. Full article
(This article belongs to the Special Issue Advanced GNC Solutions for VTOL Systems)
►▼ Show Figures

Figure 1

21 pages, 6862 KiB  
Article
Research on Self-Learning Control Method of Reusable Launch Vehicle Based on Neural Network Architecture Search
by Shuai Xue, Zhaolei Wang, Hongyang Bai, Chunmei Yu and Zian Li
Aerospace 2024, 11(9), 774; https://doi.org/10.3390/aerospace11090774 - 20 Sep 2024
Cited by 2 | Viewed by 1977
Abstract
Reusable launch vehicles need to face complex and diverse environments during flight. The design of rocket recovery control law based on traditional deep reinforcement learning (DRL) makes it difficult to obtain a set of network architectures that can adapt to multiple scenarios and [...] Read more.
Reusable launch vehicles need to face complex and diverse environments during flight. The design of rocket recovery control law based on traditional deep reinforcement learning (DRL) makes it difficult to obtain a set of network architectures that can adapt to multiple scenarios and multi-parameter uncertainties, and the performance of deep reinforcement learning algorithm depends on manual trial and error of hyperparameters. To solve this problem, this paper proposes a self-learning control method for launch vehicle recovery based on neural architecture search (NAS), which decouples deep network structure search and reinforcement learning hyperparameter optimization. First, using network architecture search technology based on a multi-objective hybrid particle swarm optimization algorithm, the proximal policy optimization algorithm of deep network architecture is automatically designed, and the search space is lightweight design in the process. Secondly, in order to further improve the landing accuracy of the launch vehicle, the Bayesian optimization (BO) method is used to automatically optimize the hyperparameters of reinforcement learning, and the control law of the landing phase in the recovery process of the launch vehicle is obtained through training. Finally, the algorithm is transplanted to the rocket intelligent learning embedded platform for comparative testing to verify its online deployment capability. The simulation results show that the proposed method can satisfy the landing accuracy of the launch vehicle recovery mission, and the control effect is basically the same as the landing accuracy of the trained rocket model under the untrained condition of model parameter deviation and wind field interference, which verifies the generalization of the proposed method. Full article
(This article belongs to the Special Issue Advanced GNC Solutions for VTOL Systems)
►▼ Show Figures

Figure 1

41 pages, 8422 KiB  
Article
Online Deterministic 3D Trajectory Generation for Electric Vertical Take-Off and Landing Aircraft
by Zoe Mbikayi, Agnes Steinert, Dominik Heimsch, Moritz Speckmaier, Philippe Rudolph, Hugh Liu and Florian Holzapfel
Aerospace 2024, 11(2), 157; https://doi.org/10.3390/aerospace11020157 - 15 Feb 2024
Cited by 4 | Viewed by 2359
Abstract
The use of non-piloted eVTOL aircraft in non-segregated airspace requires reliable and deterministic automatic flight guidance systems for the aircraft to remain predictable to all the users of the airspace and maintain a high level of safety. In this paper we present a [...] Read more.
The use of non-piloted eVTOL aircraft in non-segregated airspace requires reliable and deterministic automatic flight guidance systems for the aircraft to remain predictable to all the users of the airspace and maintain a high level of safety. In this paper we present a 3D trajectory generation module based on clothoid transition segments in the horizontal plane and high order polynomial transition segments in the vertical plane. The expressions of the coefficients of the polynomial are derived offline are used to generate the trajectory online, making the system capable of running in real time without requiring enormous computational power. For the horizontal plane, we focus on the flyby transition, and therefore present a thorough analysis of the flyby geometry and the limitations linked to this geometry and the construct of three-segment trajectory generation around a fixed turn rate. We present feasible solutions for these limitations, and show simulation results for the combined horizontal and vertical plane concepts, allowing the system to generate complex 3D trajectories. Full article
(This article belongs to the Special Issue Advanced GNC Solutions for VTOL Systems)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop