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This paper provides an in-depth analysis of the current research landscape in the field of UAV (Unmanned Aerial Vehicle) swarm formation control. This review examines both conventional control methods, including leader–follower, virtual structure, behavior-based, consensus-based, and artificial potential field, and advanced AI-based (Artificial Intelligence) methods, such as artificial neural networks and deep reinforcement learning. It highlights the distinct advantages and limitations of each approach, showcasing how conventional methods offer reliability and simplicity, while AI-based strategies provide adaptability and sophisticated optimization capabilities. This review underscores the critical need for innovative solutions and interdisciplinary approaches combining conventional and AI methods to overcome existing challenges and fully exploit the potential of UAV swarms in various applications.

Satellite-based target positioning is vital for applications like disaster relief and precision mapping. Practically, satellite errors, e.g., thermal deformation and attitude errors, lead to a mix of fixed and random errors in the measured line-of-sight angles, resulting in a decline in target-positioning accuracy. Motivated by this concern, this study introduces a systematic error self-correction target-positioning method under continuous observations using a single video satellite. After analyzing error sources and establishing an error-inclusive positioning model, we formulate a dimension-extended equation estimating both target position and fixed biases. Based on the equation, a projection transformation method is proposed to obtain the linearized estimation of unknown parameters first, and an iterative optimization method is then utilized to further refine the estimate. Compared with state-of-the-art algorithms, the proposed method can improve positioning accuracy by 98.70% in simulation scenarios with large fixed errors. Thus, the simulation and actual data calculation results demonstrate that, compared with state-of-the-art algorithms, the proposed algorithm effectively improves the target-positioning accuracy under non-ideal error conditions.

This paper proposes an adaptive neural-network-based nonsingular fast terminal sliding mode (NN-NFTSMC) approach to address the trajectory tracking control problem of a quadrotor in the presence of model uncertainties and external disturbances. First, the dynamic model of the quadrotor with uncertainty is derived. Then, a control scheme using nonsingular fast terminal sliding mode control (NFTSMC) is proposed to guarantee the finite-time convergence of the quadrotor to its desired trajectory. NFTSMC is firstly formulated for the case that the upper bound of the lumped uncertainty is known in advance. Under this framework, a disturbance observer by using the hyperbolic tangent nonlinear tracking differentiator (TANH-NTD) is designed to estimate the external interference, and a neural network (NN) approximator is used to develop an online estimate of the model uncertainty. Subsequently, adaptive algorithms are designed to compensate the approximation error and update the NN weight matrix. An NN-NFTSMC algorithm is formulated to provide the system with robustness to the model uncertainty and external disturbance. Moreover, Lyapunov-based approach is employed to prove the global stability of the closed-loop system and the finite-time convergence of the trajectory tracking errors. The results of a comparative simulation study with other recent methods illustrate the proposed control method reduces the chattering effectively and has remarkable performance.

The robotic airships provide potential aerial platforms for various applications and require robust trajectory tracking to support these tasks. A time-specified nonsingular terminal sliding mode control (TS-NTSMC) scheme is proposed to address the problem of trajectory tracking for robotic airships, which can avoid the singularity problem and specify the convergence time of terminal sliding mode control. First, the problem of trajectory tracking of robotic airships is formulated. Second, a nonsingular terminal sliding manifold consisting of pre-specified nonlinear functions is proposed, and the TS-NTSMC law is designed for trajectory tracking. Time-specified convergence and stability of the closed-loop system can be guaranteed by Lyapunov theory. Finally, compared experimental simulations are given to illustrate the advantages of TS-NTSMC against NTSMC.

Unmanned Aerial Vehicles (UAVs) have recently shown great performance collecting visual data through autonomous exploration and mapping, which are widely used in reconnaissance, surveillance, and target acquisition (RSTA) applications. In this paper, we present an onboard vision-based system for low-cost UAVs to autonomously track a moving target. Real-time visual tracking is achieved by using an object detection algorithm based on the Kernelized Correlation Filter (KCF) tracker. A 3-axis gimbaled camera with separate Inertial Measurement Unit (IMU) is used to aim at the selected target during flights. The flight control algorithm for tracking tasks is implemented on a customized quadrotor equipped with an onboard computer and a microcontroller. The proposed system is experimentally validated by successfully chasing a ground and aerial target in an outdoor environment, which has proven its reliability and efficiency.

A novel reinforcement deep learning deterministic policy gradient agent-based sliding mode control (DDPG-SMC) approach is proposed to suppress the chattering phenomenon in attitude control for quadrotors, in the presence of external disturbances. First, the attitude dynamics model of the quadrotor under study is derived, and the attitude control problem is described using formulas. Second, a sliding mode controller, including its sliding mode surface and reaching law, is chosen for the nonlinear dynamic system. The stability of the designed SMC system is validated through the Lyapunov stability theorem. Third, a reinforcement learning (RL) agent based on deep deterministic policy gradient (DDPG) is trained to adaptively adjust the switching control gain. During the training process, the input signals for the agent are the actual and desired attitude angles, while the output action is the time-varying control gain. Finally, the trained agent mentioned above is utilized in the SMC as a parameter regulator to facilitate the adaptive adjustment of the switching control gain associated with the reaching law. The simulation results validate the robustness and effectiveness of the proposed DDPG-SMC method.

Microsatellite technologies are advancing rapidly, and CubeSats have become essential platforms for innovative space missions, including space debris removal, on‐orbit servicing, and advanced communication networks. Despite their potential, current on‐orbit launch systems are limited by insufficient thrust accuracy and slow response times. This study presents a novel on‐orbit electromagnetic launcher for CubeSats that utilizes a multi‐stage induction coilgun architecture to achieve precise velocity control. For a representative 20 kg CubeSat, the proposed system enables precise exit velocity regulation up to 321.56 m/s. By implementing Alpha‐shape algorithm for three‐dimensional reachable domain envelope calculation, the research reveals a ring‐shaped envelope surrounding the initial orbit. A parametric analysis quantifies the relationships among excitation voltage, exit velocity, and the volume of the reachable domain, clarifying how system parameters shape maneuver capability. This research provides key insights into electromagnetic launch technologies, establishing a theoretical foundation for future microsatellite deployment strategies, space debris removal, and rapid response communication missions.

The stratospheric satellite is regarded as an ideal stratosphere flight platform and is able to accomplish various missions such as surveillance, earth observation, and remote sensing, which requires a robust and effective trajectory tracking control method to support these tasks. A novel observer-based robust finite-time control scheme is proposed to address the trajectory tracking control problem dedicated to a stratospheric satellite in the presence of external disturbance and actuator saturation. Firstly, an extended state observer (ESO) is adopted to observe the unavailable velocity states and unknown disturbances simultaneously, and the estimated data are utilized in the robust control law design. Then, an auxiliary system based on anti-windup compensator is developed to directly compensate for the actuator saturation difference. After that, a backstepping nonsingular fast terminal sliding mode control (BNFTSMC) strategy is designed to track the desired trajectory with high accuracy, fast convergence rate, and finite-time convergence. Then, a stability analysis using Lyapunov-based theory is performed, in which the stabilization of the stratospheric satellite system and finite-time convergence are proven. Furthermore, a number of simulations are conducted further to verify the excellent performance of the designed control strategy.

In view of the growing threat of space debris and the limitation of current debris removal method. A novel conceptual solution for multiple space debris removal is proposed, and a low-cost and miniaturized removal system is designed in this paper. And the structural composition of this removal system is designed and the space debris removal process is introduced, the system can be divided into two components including satellite platform and many mission CubeSats. The mission CubeSats can be re-launched into the transfer orbits of different space debris through a push-launch method, and the fuel consumptions on the removal mission for three space debris, using the proposed multiple removal system and traditional removal system are calculated and compared. The compared results demonstrated that the proposed solution for multiple space debris removal and the designed removal system can decrease the fuel consumptions effectively.

This article presents a modeling and simulation method to simulate the deployment process of a space net. The space net is simplified into a spring-damping model using the finite element method, and a dynamic model of the process is established. The configuration of the net and the variation of internal forces during the ejection and deployment process are analyzed through simulation calculations. In addition, ground experiments were conducted and compared with simulation results. By calculating the effective deployment area during the flight of the net, the reliability of the model and the feasibility of capturing the target are verified.