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Smart Sensing and Control for Autonomous Intelligent Unmanned Systems
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Smart Sensing and Control for Autonomous Intelligent Unmanned Systems

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: 20 November 2025 | Viewed by 8488

Special Issue Editors

Dr. Yabin Gao

E-Mail Website
Guest Editor
Department of Control Science and Engineering, Harbin Institute of Technology, Harbin 150000, China
Interests: multi-agent systems; path planning and decision; state estimation; intelligent systems
Special Issues, Collections and Topics in MDPI journals
Dr. Yanxu Su

E-Mail Website
Guest Editor
School of Artificial Intelligence, Anhui University, Hefei 230601, China
Interests: distributed optimization; distributed MPC
Dr. Xiuyu He

E-Mail Website
Guest Editor
School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
Interests: distributed parameter systems; intelligent control; vibration control; flexible systems; robotics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Bo Xu

E-Mail Website
Guest Editor
Department of Control Science and Engineering, Harbin Engineering University, Harbin 150001, China
Interests: inertial navigation; cooperative navigation

Special Issue Information

Dear Colleagues,

Autonomous intelligent unmanned systems, such as unmanned (aerial) vehicles, autonomous underwater vehicles, service robots, space robots, marine robots, smart factories, and smart grids, have become a research hotspot in both academia and industry. Currently, methods of smart sensing and control are becoming increasingly important for such systems. Smart sensing supported by intelligent sensors combined with sensor integration and microprocessors can collect, process, and exchange data or information. Intelligent sensors, for example, machine vision sensors, have the virtue of low-cost and high-precision information collection and processing. They provide a certain space for programming automation for diversified functions. Meanwhile, intelligent control produced by the controller or actuator plays a key role in the operation of the unmanned systems with autonomy and intelligence, by using the necessary sensing information. Currently, intelligent sensors are mainly used to solve the control problems of complex systems with the characteristics of uncertain dynamics, high nonlinearity, and multitasking requirements.

This Special Issue focuses on the methodology and technology of smart sensing and intelligent control for autonomous intelligent unmanned systems, as introduced above. It ultimately aims to encourage the development and application of unmanned systems in artificial intelligence. Original research and review papers in this scope are encouraged.

Dr. Yabin Gao
Dr. Yanxu Su
Dr. Xiuyu He
Prof. Dr. Bo Xu
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. Sensors 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 2600 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

  • smart sampling
  • machine vision
  • machine learning
  • distributed filtering
  • intelligent detection
  • autonomous decision making
  • optimal control
  • cooperation control
  • game-based control
  • smart fault tolerant
  • safety and security

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Published Papers (5 papers)

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Research

17 pages, 8704 KiB  
Article
Event-Trigger Reinforcement Learning-Based Coordinate Control of Modular Unmanned System via Nonzero-Sum Game
by Yebao Liu, Tianjiao An, Jianguo Chen, Luyang Zhong and Yuhan Qian
Sensors 2025, 25(2), 314; https://doi.org/10.3390/s25020314 - 7 Jan 2025
Viewed by 560
Abstract
Decreasing the position error and control torque is important for the coordinate control of a modular unmanned system with less communication burden between the sensor and the actuator. Therefore, this paper proposes event-trigger reinforcement learning (ETRL)-based coordinate control of a modular unmanned system [...] Read more.
Decreasing the position error and control torque is important for the coordinate control of a modular unmanned system with less communication burden between the sensor and the actuator. Therefore, this paper proposes event-trigger reinforcement learning (ETRL)-based coordinate control of a modular unmanned system (MUS) via the nonzero-sum game (NZSG) strategy. The dynamic model of the MUS is established via joint torque feedback (JTF) technology. Based on the NZSG strategy, the existing coordinate control problem is transformed into an RL issue. With the help of the ET mechanism, the periodic communication mechanism of the system is avoided. The ET-critic neural network (NN) is used to approximate the performance index function, thus obtaining the ETRL coordinate control policy. The stability of the closed-loop system is verified via Lyapunov’s theorem. Experiment results demonstrate the validity of the proposed method. The experimental results show that the proposed method reduces the position error by 30% and control torque by 10% compared with the existing control methods. Full article
(This article belongs to the Special Issue Smart Sensing and Control for Autonomous Intelligent Unmanned Systems)
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18 pages, 5931 KiB  
Article
Disturbance Estimation and Predefined-Time Control Approach to Formation of Multi-Spacecraft Systems
by Zhicheng Zhang, Weimin Bao, Qimin Hou, Yinhao Ju and Yabin Gao
Sensors 2024, 24(17), 5671; https://doi.org/10.3390/s24175671 - 31 Aug 2024
Viewed by 1408
Abstract
Accurate sensing and control are important for high-performance formation control of spacecraft systems. This paper presents a strategy of disturbance estimation and distributed predefined-time control for the formation of multi-spacecraft systems with uncertainties based on a disturbance observer. The process begins by formulating [...] Read more.
Accurate sensing and control are important for high-performance formation control of spacecraft systems. This paper presents a strategy of disturbance estimation and distributed predefined-time control for the formation of multi-spacecraft systems with uncertainties based on a disturbance observer. The process begins by formulating a kinematics model for the relative motion of spacecraft, with the formation’s communication topology represented by a directed graph for the formation system of the spacecraft. A disturbance observer is then developed to estimate the disturbances, and the estimation errors can be convergent in fixed time. Following this, a disturbance-estimation-based sliding mode control is proposed to guarantee the predefined-time convergence of the multi-spacecraft formation system, regardless of initial conditions. It allows each spacecraft to reach its desired position within a set time frame. The results of the analysis of the multi-spacecraft formation system are also provided. Finally, an example simulation of a five-spacecraft formation flying system is provided to demonstrate the presented formation control method. Full article
(This article belongs to the Special Issue Smart Sensing and Control for Autonomous Intelligent Unmanned Systems)
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18 pages, 3621 KiB  
Article
Enhanced Predefined-Time Control for Spacecraft Attitude Tracking: A Dynamic Predictive Approach
by Jinhe Yang, Tongjian Guo, Yi Yu, Quanliang Dong and Yifan Jia
Sensors 2024, 24(16), 5127; https://doi.org/10.3390/s24165127 - 8 Aug 2024
Viewed by 1416
Abstract
This study presents a predefined-time control strategy for rigid spacecraft, employing dynamic predictive techniques to achieve robust and precise attitude tracking within predefined time constraints. Advanced predictive algorithms are used to effectively mitigate system uncertainties and environmental disturbances. The main contributions of this [...] Read more.
This study presents a predefined-time control strategy for rigid spacecraft, employing dynamic predictive techniques to achieve robust and precise attitude tracking within predefined time constraints. Advanced predictive algorithms are used to effectively mitigate system uncertainties and environmental disturbances. The main contributions of this work are introducing adaptive global optimization for period updates, which relaxes the original restrictive conditions; ensuring easier parameter adjustments in predefined-time control, providing a nonconservative upper bound on system stability; and developing a continuous, robust control law through terminal sliding mode control and predictive methods. Extensive simulations confirm the control scheme reduces attitude tracking errors to less than 0.01 degrees at steady state, demonstrating the effectiveness of the proposed control strategy. Full article
(This article belongs to the Special Issue Smart Sensing and Control for Autonomous Intelligent Unmanned Systems)
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16 pages, 3430 KiB  
Article
Environmental-Driven Approach towards Level 5 Self-Driving
by Mohammad Hurair, Jaeil Ju and Junghee Han
Sensors 2024, 24(2), 485; https://doi.org/10.3390/s24020485 - 12 Jan 2024
Cited by 2 | Viewed by 1833
Abstract
As technology advances in almost all areas of life, many companies and researchers are working to develop fully autonomous vehicles. Such level 5 autonomous driving, unlike levels 0 to 4, is a driverless vehicle stage and so the leap from level 4 to [...] Read more.
As technology advances in almost all areas of life, many companies and researchers are working to develop fully autonomous vehicles. Such level 5 autonomous driving, unlike levels 0 to 4, is a driverless vehicle stage and so the leap from level 4 to level 5 autonomous driving requires much more research and experimentation. For autonomous vehicles to safely drive in complex environments, autonomous cars should ensure end-to-end delay deadlines of sensor systems and car-controlling algorithms including machine learning modules, which are known to be very computationally intensive. To address this issue, we propose a new framework, i.e., an environment-driven approach for autonomous cars. Specifically, we identify environmental factors that we cannot control at all, and controllable internal factors such as sensing frequency, image resolution, prediction rate, car speed, and so on. Then, we design an admission control module that allows us to control internal factors such as image resolution and detection period to determine whether given parameters are acceptable or not for supporting end-to-end deadlines in the current environmental scenario while maintaining the accuracy of autonomous driving. The proposed framework has been verified with an RC car and a simulator. Full article
(This article belongs to the Special Issue Smart Sensing and Control for Autonomous Intelligent Unmanned Systems)
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19 pages, 3729 KiB  
Article
Optimal-Damage-Effectiveness Cooperative-Control Strategy for the Pursuit–Evasion Problem with Multiple Guided Missiles
by Xiang Ma, Keren Dai, Man Li, Hang Yu, Weichen Shang, Libo Ding, He Zhang and Xiaofeng Wang
Sensors 2022, 22(23), 9342; https://doi.org/10.3390/s22239342 - 30 Nov 2022
Cited by 2 | Viewed by 2058
Abstract
In this paper, an optimal-damage-effectiveness cooperative-control strategy based on a damage-efficiency model and a virtual-force method is proposed to solve the pursuit–evasion problem with multiple guided missiles. Firstly, different from the overly ideal assumption in the traditional pursuit–evasion problem, an optimization problem that [...] Read more.
In this paper, an optimal-damage-effectiveness cooperative-control strategy based on a damage-efficiency model and a virtual-force method is proposed to solve the pursuit–evasion problem with multiple guided missiles. Firstly, different from the overly ideal assumption in the traditional pursuit–evasion problem, an optimization problem that maximizes the damage efficiency is established and solved, making the optimal-damage-effectiveness strategy more meaningful for practical applications. Secondly, a modified virtual-force method is proposed to obtain this optimal-damage-effectiveness control strategy, which solves the numerical solution challenges brought by the high-complexity damage function. Thirdly, adaptive gain is designed in this strategy based on guidance-integrated fuze technology to achieve robust maximum damage efficiency in unpredictable interception conditions. Finally, the effectiveness and robustness of the proposed strategy are verified by numerical simulations. Full article
(This article belongs to the Special Issue Smart Sensing and Control for Autonomous Intelligent Unmanned Systems)
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