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Intelligent Autonomous Decision-Making and Cooperative Control Technology of High-Speed Vehicle...
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Intelligent Autonomous Decision-Making and Cooperative Control Technology of High-Speed Vehicle Swarms, Volume II

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Aerospace Science and Engineering".

Deadline for manuscript submissions: closed (20 April 2023) | Viewed by 7505

Special Issue Editors

Dr. Dong Zhang

E-Mail Website
Guest Editor
School of Astronautics, Northwestern Polytechnical University, Xian 710072, China
Interests: hypersonic vehicles; modeling; scramjet engine; aerodynamic analysis; propulsion/flight dynamics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Wei Huang

E-Mail
Guest Editor
College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, China
Interests: wind turbines; vortex; hypersonics; drag; vorticity; numerical simulation; flow; aerodynamics; aircraft; drag reduction
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Swarm intelligence technology is a new technology combining unmanned system technology, network information technology and artificial intelligence technology, and this has become a research hotspot.

Due to the difference in flight dynamics characteristics, the strong uncertainty caused by the large airspace of the flight environment and the fast time-varying cluster topology caused by high dynamics, it is difficult for traditional UAV swarm technology to be directly applied to the cluster system of high-speed vehicles. Therefore, there is an urgent need to study new theories and methods for the cooperative operation of high-speed vehicle swarm systems.

  1. Swarm distributed situation awareness and cognitive technology:

    a）Modeling of distributed situation awareness capability of multiple agents in an uncertain environment;

    b）Cooperative situation awareness method under multi-field coupling;

    c）Situation awareness consistency assessment method.

  2. Swarm autonomous decision-making method based on decision rule base;
  3. Swarm collaborative planning technology in a complex environment:

    a) Evaluation and system optimization framework design of swarm task planning;

    b) Swarm collaborative dynamic mission planning technology in an uncertain environment;

    c) Collaborative mission planning technology for swarm

  4. Swarm strike cooperative task planning technology under multiple constraints and strong coupling conditions:

    a）Autonomous control technology of high-speed vehicle swarms;

    b）Research on autonomous control method and control strategy of swarms;

    c）High-speed aircraft swarm control technology for topology switching;

    d）Robust adaptive control technology for high-speed vehicle swarms;

  5. Verification system of key technologies of swarm intelligent planning and autonomous control:

    a） Design and integration of full digital simulation verification platform for intelligent planning and autonomous control of high-speed vehicle swarms;

    b） Hardware in the loop simulation verification system of the collaborative planning controller;

    c） Verification of aircraft swarm flight tests in typical scenarios.

  6. Other relevant theories, methods, technologies, systems and platforms.

Dr. Dong Zhang
Prof. Dr. Wei Huang
Guest Editors

Manuscript Submission Information

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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. Applied Sciences is an international peer-reviewed open access semimonthly 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 (6 papers)

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Research

17 pages, 985 KiB  
Article
Trajectory Planning of Aerial Robotic Manipulator Using Hybrid Particle Swarm Optimization
by Suping Zhao, Chaobo Chen, Jichao Li, Song Gao and Xinxin Guo
Appl. Sci. 2022, 12(21), 10892; https://doi.org/10.3390/app122110892 - 27 Oct 2022
Cited by 1 | Viewed by 1116
Abstract
The trajectory planning of an aerial robotic manipulator system is studied using Hybrid Particle Swarm Optimization (HPSO). The aerial robotic manipulator is composed of an unmanned aerial vehicle (UAV) base and a robotic manipulator. The robotic manipulator is dynamically singular. In addition, strong [...] Read more.
The trajectory planning of an aerial robotic manipulator system is studied using Hybrid Particle Swarm Optimization (HPSO). The aerial robotic manipulator is composed of an unmanned aerial vehicle (UAV) base and a robotic manipulator. The robotic manipulator is dynamically singular. In addition, strong coupling exists between the UAV base and the robotic manipulator. To overcome the problems, the trajectory planning is studied in the join space using HPSO. HPSO combines superiorities of PSO and GA (Genetic Algorithm), prohibiting particles from becoming trapped in a local minimum. In addition, the control parameters are self-adaptive and contribute to fast searching for the global optimum. The trajectory planning problem is converted into a parameter optimization problem. Each joint trajectory is parameterized with a Bézier curve. The HPSO is implemented to optimize joint trajectories, satisfying specific objectives and imposed constraints. Numerical simulations are also carried out to validate the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Intelligent Autonomous Decision-Making and Cooperative Control Technology of High-Speed Vehicle Swarms, Volume II)
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16 pages, 402 KiB  
Article
Robust Quadratic Optimal Control for Discrete-Time Linear Systems with Non-Stochastic Noises
by Jiaoru Huang, Chaobo Chen, Song Gao, Xiaoyan Zhang and Guo Xie
Appl. Sci. 2022, 12(20), 10250; https://doi.org/10.3390/app122010250 - 12 Oct 2022
Viewed by 941
Abstract
In this paper, the quadratic optimal control problem is investigated for the discrete-time linear systems with process and measurement noises which belong to specified ellipsoidal sets. As the noises are non-stochastic, the traditional Kalman filtering and Dynamic Bellman Equation are not applicable for [...] Read more.
In this paper, the quadratic optimal control problem is investigated for the discrete-time linear systems with process and measurement noises which belong to specified ellipsoidal sets. As the noises are non-stochastic, the traditional Kalman filtering and Dynamic Bellman Equation are not applicable for the proposed control problem. To obtain the optimal control, we firstly converted the multi-step quadratic global optimal control problem to multiple one-step quadratic local approximate optimal control problems. For each one-step quadratic optimal control problem, considering that the system states are not fully available, the set-membership filtering is applied to estimate the true state feasible set. Then based on robust optimization, a robust state feedback control strategy can be obtained by solving a certain semidefinite programming (SDP) problem. The method can not only achieve the optimal control, but also estimate the system states more accurately. Finally, the simulation results verify the effectiveness of the proposed algorithm. Full article
(This article belongs to the Special Issue Intelligent Autonomous Decision-Making and Cooperative Control Technology of High-Speed Vehicle Swarms, Volume II)
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26 pages, 8289 KiB  
Article
A Mixing Process Influenced by Wall Jet-Induced Shock Waves in Supersonic Flow
by Ji Zhang, Daoning Yang, Yi Wang and Dongdong Zhang
Appl. Sci. 2022, 12(16), 8384; https://doi.org/10.3390/app12168384 - 22 Aug 2022
Cited by 2 | Viewed by 1221
Abstract
With the development of hypersonic air-breathing propulsion systems, such as the supersonic combustion ramjet (Scramjet) and rocket-based combined cycle (RBCC) engines, the mixing process of supersonic airstream with fuel in the engine combustor has been drawing more and more attention. Due to the [...] Read more.
With the development of hypersonic air-breathing propulsion systems, such as the supersonic combustion ramjet (Scramjet) and rocket-based combined cycle (RBCC) engines, the mixing process of supersonic airstream with fuel in the engine combustor has been drawing more and more attention. Due to the compressibility effects, the mixing process in a supersonic condition is significantly inhibited. In the present paper, the novel strategy of wall-jet induced shock waves (WJISW) is put forward to realize mixing enhancement. The interaction process between WJISW and the supersonic mixing layer is researched and the enhanced-mixing mechanism is revealed, employing large eddy simulation (LES) methods. The fine vortex structures of the flow field are well captured and presented, utilizing the numerical schlieren technique. Detailed visualization results indicate that WJISW in a low frequency condition can result in the ‘region action mode’ (RAM) never reported before. The drastic dynamic behaviors including growth, deformation, and distortion in the interaction region can undoubtedly promote the mixing of upper and lower streams. The Reynolds stress distributions along the streamwise x-direction suggest that more intense fluctuations can be achieved with a low frequency WJISW. Moreover, a sharp increase in mixing layer thickness can be realized in the interaction region. The dynamic mode decomposition (DMD) analysis results show that the mixing layer evolution process is dominated by the mode induced by WJISW, which leads to the coexistence of both large- and small-scale structures in the flow field. The entrainment process corresponding to large-scale vortices and the nibbling process corresponding to small-scale vortices can obviously promote mixing enhancement. It is suggested that the present proposed strategy is a good candidate for enhanced-mixing with application to Scramjet and RBCC combustors. Full article
(This article belongs to the Special Issue Intelligent Autonomous Decision-Making and Cooperative Control Technology of High-Speed Vehicle Swarms, Volume II)
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14 pages, 2905 KiB  
Article
Trajectory Tracking Control Method Based on Adaptive Higher Order Sliding Mode
by Jingang He, Yuanjie Meng, Jun You, Jin Zhang, Yuanzhuo Wang and Cheng Zhang
Appl. Sci. 2022, 12(16), 7955; https://doi.org/10.3390/app12167955 - 09 Aug 2022
Viewed by 1253
Abstract
To resolve the problem of high-precision trajectory tracking control under interference conditions in a missile’s mid-guidance phase, according to the constructed nominal trajectory, an improved adaptive high-order sliding mode trajectory tracking controller (AHSTC) is proposed. In this method, the open-loop nominal trajectories are [...] Read more.
To resolve the problem of high-precision trajectory tracking control under interference conditions in a missile’s mid-guidance phase, according to the constructed nominal trajectory, an improved adaptive high-order sliding mode trajectory tracking controller (AHSTC) is proposed. In this method, the open-loop nominal trajectories are established according to the nonlinear programming and Gaussian pseudospectra method. A high-precision trajectory tracking controller is developed by designing a nonlinear sliding mode surface and an adaptive high-order sliding mode approaching law combined with the trajectory tracking nonlinear error model. To verify the effectiveness and superiority of the proposed method, analysis and simulation are carried out through the example of a missile mid-guidance phase tracking control. Compared to the linear quadratic regulator (LQR) and active disturbance rejection controller (ADRC) method, the simulation results show that the proposed AHSTC method shows faster convergence and improved tracking effect. Therefore, the proposed AHSTC method has a good results and engineering application value. Full article
(This article belongs to the Special Issue Intelligent Autonomous Decision-Making and Cooperative Control Technology of High-Speed Vehicle Swarms, Volume II)
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22 pages, 17281 KiB  
Article
Consensus Cooperative Encirclement Interception Guidance Law for Multiple Vehicles against Maneuvering Target
by Mingkun Guo, Guangqing Xia, Feng Yang, Cong Liu, Kai Liu and Jingnan Yang
Appl. Sci. 2022, 12(14), 7307; https://doi.org/10.3390/app12147307 - 20 Jul 2022
Cited by 2 | Viewed by 1296
Abstract
This paper studies a cooperative encirclement interception guidance law against a maneuvering target that utilizes a leader–follower control scheme. The control design is decoupled into two parts. In the line-of-sight (LOS) direction, a fixed-time distributed disturbance observer is presented to estimate the maneuvering [...] Read more.
This paper studies a cooperative encirclement interception guidance law against a maneuvering target that utilizes a leader–follower control scheme. The control design is decoupled into two parts. In the line-of-sight (LOS) direction, a fixed-time distributed disturbance observer is presented to estimate the maneuvering of the target. Based on the proposed disturbance observer, the guidance law is designed for the followers to guarantee that each follower’s total flight time achieves consensus with that of the leader. In the normal direction of the LOS, the control command is designed to realize the encirclement interception with a predefined-time consensus protocol. The convergence of the guidance algorithm is proven by the Lyapunov stability theory. Numerical simulations are provided to demonstrate the effectiveness and superiority of the proposed cooperative-guidance law. Full article
(This article belongs to the Special Issue Intelligent Autonomous Decision-Making and Cooperative Control Technology of High-Speed Vehicle Swarms, Volume II)
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17 pages, 2257 KiB  
Article
Two Low-Level Feature Distributions Based No Reference Image Quality Assessment
by Hao Fu, Guojun Liu, Xiaoqin Yang, Lili Wei and Lixia Yang
Appl. Sci. 2022, 12(10), 4975; https://doi.org/10.3390/app12104975 - 14 May 2022
Cited by 1 | Viewed by 1129
Abstract
No reference image quality assessment (NR IQA) aims to develop quantitative measures to automatically and accurately estimate perceptual image quality without any prior information about the reference image. In this paper, we introduce two low-level feature distributions (TLLFD) based method for NR IQA. [...] Read more.
No reference image quality assessment (NR IQA) aims to develop quantitative measures to automatically and accurately estimate perceptual image quality without any prior information about the reference image. In this paper, we introduce two low-level feature distributions (TLLFD) based method for NR IQA. Different from the deep learning method, the proposed method characterizes image quality with the distributions of low-level features, thus it has few parameters, simple model, high efficiency, and strong robustness. First, the texture change of distorted image is extracted by the weighted histogram of generalized local binary pattern. Second, the Weibull distribution of gradient is extracted to represent the structural change of the distorted image. Furthermore, support vector regression is adopted to model the complex nonlinear relationship between feature space and quality measure. Finally, numerical tests are performed on LIVE, CISQ, MICT, and TID2008 standard databases for five different distortion categories JPEG2000 (JP2K), JPEG, White Noise (WN), Gaussian Blur (GB), and Fast Fading (FF). The experimental results indicate that TLLFD method achieves superior performance and strong generalization for image quality prediction as compared to state-of-the-art full-reference, no reference, and even deep learning IQA methods. Full article
(This article belongs to the Special Issue Intelligent Autonomous Decision-Making and Cooperative Control Technology of High-Speed Vehicle Swarms, Volume II)
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