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Aerospace
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Learning-Based Intelligent Control in Aerospace Applications
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Learning-Based Intelligent Control in Aerospace Applications

A special issue of Aerospace (ISSN 2226-4310).

Deadline for manuscript submissions: closed (15 December 2022) | Viewed by 14264

Special Issue Editor

Dr. Maolong Lv

E-Mail Website
Guest Editor
Faculteit Luchtvaart- en Ruimtevaarttechniek, TU Delft, 2628 CD Delft, The Netherlands
Interests: nonlinear control; intelligent control; adaptive learning control; deep reinforcement learning and intelligent decision making with applications in aerospace; hypersonic vehicles; unmanned autonomous systems

Special Issue Information

Dear Colleagues,

Since the creation of cybernetics, the development of control theory has been remarkable, with a range of considerable benefits. However, control theory has an essential weakness in that it requires significant precision. However, in many cases, especially in aerospace, this requirement cannot be met. With the rapid development of science and technology and production, the controlled object may be large in scale, extremely complex, time-varying, and nonlinear. Additionally, the working environment of the controlled object may be constantly changing, making it very difficult to obtain an accurate quantitative model. This shows that the uncertainty of the model must be considered. Although self-adaptive or self-correcting control can solve the uncertainty problem to a certain extent, it still requires online identification of the object model in essence, and the algorithm is complex and operation large, so the application scope is limited. The cross-combination of artificial intelligence and control theory provides a new way to solve the problems encountered by control theory. This cross-combination produces intelligent control. The basic goal of intelligent control is to relax the requirements on the object model and achieve high-performance control of large, complex, nonlinear, and time-varying control systems. This Special Issue focuses on the future development trends of aerospace technology and is committed to promoting the development of aerospace technology. It mainly invites articles on the application of intelligent control methods, nonlinear control methods, and learning-based methods in the aerospace field.

Dr. Maolong Lv
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.

Published Papers (7 papers)

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Research

25 pages, 10601 KiB  
Article
Risk Quantification and Visualization Method for Loss-of-Control Scenarios in Flight
by Guozhi Wang, Binbin Pei, Haojun Xu, Maolong Lv, Zilong Zhao and Xiangwei Bu
Aerospace 2023, 10(5), 416; https://doi.org/10.3390/aerospace10050416 - 28 Apr 2023
Viewed by 925
Abstract
This paper proposes a flight risk analysis method that combines risk assessment and visual deduction to study the causes of flight accidents, specifically the loss of control caused by failure factors. The goal is to explore the impact of these failure factors on [...] Read more.
This paper proposes a flight risk analysis method that combines risk assessment and visual deduction to study the causes of flight accidents, specifically the loss of control caused by failure factors. The goal is to explore the impact of these failure factors on loss-of-control events and illustrate the risk evolution under different scenarios in a clear and intuitive manner. To achieve this, the paper develops a failure scenario tree to guide flight simulations under different loss-of-control scenarios. The next step involves developing a multi-parameters risk assessment method that can quantify flight risk at each time step of the flight simulation. This assessment method uses entropy weight and a grey correlation algorithm to assign variable weights to the different parameters. Finally, the paper presents the visual deduction of the risk evolution process under different loss-of-control scenarios using a risk tree that concisely represents the time-series risk assessment results and failure logical chains. Taking three common failure factors (actuator failure, engine failure, and wing icing) as cases, the paper designs 25 different loss-of-control scenarios to demonstrate the flight risk analysis method. By comparing the risk evolution process under different loss-of-control scenarios, the paper explores the impact of the failure factors on flight safety. The analysis results indicate that this method combines risk analysis from both individual and global perspectives, enabling effective analysis of risk evolution in loss-of-control events. Full article
(This article belongs to the Special Issue Learning-Based Intelligent Control in Aerospace Applications)
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21 pages, 3725 KiB  
Article
Intelligent Game Strategies in Target-Missile-Defender Engagement Using Curriculum-Based Deep Reinforcement Learning
by Xiaopeng Gong, Wanchun Chen and Zhongyuan Chen
Aerospace 2023, 10(2), 133; https://doi.org/10.3390/aerospace10020133 - 31 Jan 2023
Cited by 2 | Viewed by 1893
Abstract
Aiming at the attack and defense game problem in the target-missile-defender three-body confrontation scenario, intelligent game strategies based on deep reinforcement learning are proposed, including an attack strategy applicable to attacking missiles and active defense strategy applicable to a target/defender. First, based on [...] Read more.
Aiming at the attack and defense game problem in the target-missile-defender three-body confrontation scenario, intelligent game strategies based on deep reinforcement learning are proposed, including an attack strategy applicable to attacking missiles and active defense strategy applicable to a target/defender. First, based on the classical three-body adversarial research, the reinforcement learning algorithm is introduced to improve the purposefulness of the algorithm training. The action spaces the reward and punishment conditions of both attack and defense confrontation are considered in the reward function design. Through the analysis of the sign of the action space and design of the reward function in the adversarial form, the combat requirements can be satisfied in both the missile and target/defender training. Then, a curriculum-based deep reinforcement learning algorithm is applied to train the agents and a convergent game strategy is obtained. The simulation results show that the attack strategy of the missile can maneuver according to the battlefield situation and can successfully hit the target after avoiding the defender. The active defense strategy enables the less capable target/defender to achieve the effect similar to a network adversarial attack on the missile agent, shielding targets from attack against missiles with superior maneuverability on the battlefield. Full article
(This article belongs to the Special Issue Learning-Based Intelligent Control in Aerospace Applications)
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16 pages, 3237 KiB  
Article
Intelligent Pursuit–Evasion Game Based on Deep Reinforcement Learning for Hypersonic Vehicles
by Mengjing Gao, Tian Yan, Quancheng Li, Wenxing Fu and Jin Zhang
Aerospace 2023, 10(1), 86; https://doi.org/10.3390/aerospace10010086 - 15 Jan 2023
Cited by 2 | Viewed by 2311
Abstract
As defense technology develops, it is essential to study the pursuit–evasion (PE) game problem in hypersonic vehicles, especially in the situation where a head-on scenario is created. Under a head-on situation, the hypersonic vehicle’s speed advantage is offset. This paper, therefore, establishes the [...] Read more.
As defense technology develops, it is essential to study the pursuit–evasion (PE) game problem in hypersonic vehicles, especially in the situation where a head-on scenario is created. Under a head-on situation, the hypersonic vehicle’s speed advantage is offset. This paper, therefore, establishes the scenario and model for the two sides of attack and defense, using the twin delayed deep deterministic (TD3) gradient strategy, which has a faster convergence speed and reduces over-estimation. In view of the flight state–action value function, the decision framework for escape control based on the actor–critic method is constructed, and the solution method for a deep reinforcement learning model based on the TD3 gradient network is presented. Simulation results show that the proposed strategy enables the hypersonic vehicle to evade successfully, even under an adverse head-on scene. Moreover, the programmed maneuver strategy of the hypersonic vehicle is improved, transforming it into an intelligent maneuver strategy. Full article
(This article belongs to the Special Issue Learning-Based Intelligent Control in Aerospace Applications)
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21 pages, 907 KiB  
Article
Constrained Integrated Guidance and Control Scheme for Strap-Down Hypersonic Flight Vehicles with Partial Measurement and Unmatched Uncertainties
by Minzhou Dong, Xinkai Xu and Feng Xie
Aerospace 2022, 9(12), 840; https://doi.org/10.3390/aerospace9120840 - 17 Dec 2022
Cited by 4 | Viewed by 1216
Abstract
This paper investigates the issue of integrated guidance and control (IGC) design for strap-down hypersonic flight vehicles with partial measurement information and unmatched uncertainties. A constrained IGC scheme is proposed by combining the barrier Lyapunov function-based backstepping methodology and the specific output-based finite-time [...] Read more.
This paper investigates the issue of integrated guidance and control (IGC) design for strap-down hypersonic flight vehicles with partial measurement information and unmatched uncertainties. A constrained IGC scheme is proposed by combining the barrier Lyapunov function-based backstepping methodology and the specific output-based finite-time disturbance observer. Different from the existing methods, which require the state information and matched disturbances, the main features of the presented approach is capable of addressing the partial measurement knowledge and unmatched uncertainties simultaneously. The IGC model of hypersonic flight vehicles is first formulated, and based on that, the specific output-based finite-time disturbance observer (OFTDO) is proposed to achieve the finite-time estimation of the unmatched uncertainties through the output. Then, the constrained IGC strategy is constructed via the backstepping technique. The stability of the closed-loop system including the estimation and tracking errors dynamics is analyzed in detail. The effectiveness of the proposed method is verified by numerical simulations and Monte-Carlo tests. Full article
(This article belongs to the Special Issue Learning-Based Intelligent Control in Aerospace Applications)
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19 pages, 7823 KiB  
Article
Reentry Flight Capability Assessment Based on Dynamics–Informed Neural Network and Piecewise Guidance
by Kai Liu, Jili Zhang and Xinlu Guo
Aerospace 2022, 9(12), 790; https://doi.org/10.3390/aerospace9120790 - 03 Dec 2022
Cited by 1 | Viewed by 1230
Abstract
To improve the flexibility of the trajectory and the diversity of the drop point of the reentry vehicle, a flight capability assessment method based on a dynamics–informed neural network (DINN) is proposed. Firstly, the concept of a reachable domain is introduced to characterize [...] Read more.
To improve the flexibility of the trajectory and the diversity of the drop point of the reentry vehicle, a flight capability assessment method based on a dynamics–informed neural network (DINN) is proposed. Firstly, the concept of a reachable domain is introduced to characterize the flight capability of the reentry vehicle and to estimate whether there are appropriate TAEM points in the area. Secondly, after the impact characteristic analysis, the reachable domains corresponding to different initial flight states are obtained through moderate dynamic simulations and reasonable mathematical expansion. The flight states and boundary point positions of the reachable domain are used as the training database of DINN, and the acquired DINN can realize the fast solution of reachable domains. Finally, the effectiveness of DINN in solving the reachable domain is verified using simulation. The simulation results show that DINN manifests the same accuracy as the existing solving methods and can meet the demand of determining whether the target point is located in the reachable domain. Additionally, the running time is shortened to one–800th of the existing methods, reaching the millisecond level, to support real–time assessment and decision–making. A predictor–corrector guidance algorithm with the piecewise objective function is also introduced. The simulation results illustrate that the proposed algorithm can stably guide the vehicle from the initial state points to the target points in the reachable domain. Full article
(This article belongs to the Special Issue Learning-Based Intelligent Control in Aerospace Applications)
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19 pages, 3026 KiB  
Article
Two-Level Hierarchical-Interaction-Based Group Formation Control for MAV/UAVs
by Huan Wang, Shuguang Liu, Maolong Lv and Boyang Zhang
Aerospace 2022, 9(9), 510; https://doi.org/10.3390/aerospace9090510 - 14 Sep 2022
Cited by 5 | Viewed by 1385
Abstract
Cooperative group formation control of manned/unmanned aircraft vehicles (MAV/UAVs) using a hierarchical framework can be more efficient and flexible than centralized control strategies. In this paper, a two-level hierarchical-interaction-based cooperative control strategy is proposed for the MAV/UAVs group formation. At the upper level, [...] Read more.
Cooperative group formation control of manned/unmanned aircraft vehicles (MAV/UAVs) using a hierarchical framework can be more efficient and flexible than centralized control strategies. In this paper, a two-level hierarchical-interaction-based cooperative control strategy is proposed for the MAV/UAVs group formation. At the upper level, combined with the nonlinear disturbance observer (NDO) and dynamic surface control (DSC) algorithm, a trajectory tracking problem with external disturbances for MAV is formulated. At the lower level, the leader-following formation controller is utilized to deal with the sub-formation keeping control problem for UAVs, based on the sliding mode disturbance observer and fast terminal sliding mode control law, and the robust performance and control accuracy are effectively improved. Moreover, the overall stability of the MAV/UAVs system is demonstrated using Lyapunov theory. The proposed approach is evaluated by simulation under the ground penetration combat mission for MAV/UAVs, and the performance is compared with that of other control strategies. Full article
(This article belongs to the Special Issue Learning-Based Intelligent Control in Aerospace Applications)
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25 pages, 4013 KiB  
Article
Multi-UAV Unbalanced Targets Coordinated Dynamic Task Allocation in Phases
by Wenfei Wang, Maolong Lv, Le Ru, Bo Lu, Shiguang Hu and Xinlong Chang
Aerospace 2022, 9(9), 491; https://doi.org/10.3390/aerospace9090491 - 01 Sep 2022
Cited by 4 | Viewed by 2003
Abstract
Unmanned aerial vehicles (UAVs) can be used in swarms to achieve multiple tasks cooperatively. Multi-UAV and multi-target cooperative task assignments are difficult. To solve the problem of unbalanced, phased, cooperative assignment between UAVs and tasks, we establish an unbalanced, phased task assignment model [...] Read more.
Unmanned aerial vehicles (UAVs) can be used in swarms to achieve multiple tasks cooperatively. Multi-UAV and multi-target cooperative task assignments are difficult. To solve the problem of unbalanced, phased, cooperative assignment between UAVs and tasks, we establish an unbalanced, phased task assignment model that considers the constraints of task execution, time, and target task execution demand. Based on an improved consensus-based bundle algorithm (CBBA), we propose a two-tier task bidding mechanism. According to dynamic changes in new tasks, we study a dynamic assignment strategy and propose a mechanism based on task continuity adjustment and time windows. Finally, a simulation experiment is used to verify the feasibility and effectiveness of the proposed allocation method in multi-UAV target assignment scenarios. The results show that the dynamic task assignment strategy can efficiently assign random new tasks as they arise. Full article
(This article belongs to the Special Issue Learning-Based Intelligent Control in Aerospace Applications)
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