For space missions such as deep space exploration and on-orbit operations, a high-precision 3D model of the target is a prerequisite for achieving autonomous navigation and precise manipulation. However, natural uncontrolled orbits impose strong geometry constraints and require long observation periods, while active orbital maneuvering accelerates data acquisition but increases fuel consumption and reduces mission endurance. This trade-off between maneuvering efficiency and observation completeness has become a bottleneck limiting spacecraft operations on-orbit. To address these challenges, this paper proposes a sensing-planning framework that integrates active observation with orbital maneuvering. First, a 3D reconstruction scheme based on 2D Gaussian splatting (2DGS) is designed, taking uncertainty into account. Next, the optimal observation views are estimated using Bayesian theory, followed by orbit selection combined with fuel consumption and observation time derived from orbital mechanics. Simultaneously, discrete point filtering is applied to improve the reconstruction quality of the 3D mesh in the space environment. Finally, the effectiveness of the proposed method is validated through simulations and experimental comparisons with state-of-the-art (SOTA) in a newly constructed multi-orbital observation space environment darkroom. Code and data are available at: https://github.com/YD-96/Active-2DGS and https://bhpan.buaa.edu.cn/link/AAA6508AF1B8714EF0B91A992489F2228F.

This article proposes a novel discounted inverse reinforcement learning (DIRL) algorithm for linear quadratic (LQ) control of unknown continuous-time (CT) systems with partially observable states and an unknown discounted value function. Existing DIRL methods predominantly rely on full-state feedback, limiting their applicability to practical scenarios where only input-output data are available. To this end, a state reconstruction method is designed for the system controlled by an expert using the measured desired output. Based on this, a model-free output-feedback (OPFB) DIRL algorithm is presented to iteratively solve the unknown value function and the corresponding optimal OPFB control policy equivalent to the expert control policy. The convergence of the proposed algorithm and the nonuniqueness of solutions are rigorously analyzed. Finally, comprehensive simulations reveal the effectiveness of the proposed algorithm in recovering the expert control policy and its superior computational efficiency compared to state-of-the-art (SOTA) methods.

This article develops a dual-link coded event-triggered control (DL-CEC) for consensus in nonlinear multiagent system. To reduce the communication burden of signal transmission between the control box and actuator box or among agents and to enhance the security of information exchange, a dual-link coded scheme is proposed to compress each transmitted information into an L-length string. Furthermore, since the intrinsic complexity of nonlinear systems often causes traditional prescribed performance methods to fail in meeting constraints during the initial stages of operation, an adaptive prescribed performance (APP) scheme is introduced. By utilizing auxiliary functions, the APP is capable of dynamically adjusting performance boundaries, enabling seamless adaptation to varying initial system conditions. As a result, it ensures the tracking error is rigorously guaranteed to remain within a user-defined range over a prescribed time horizon, effectively accommodating diverse initial conditions of the system. By integrating DL-CEC with the APP method, the proposed control strategy ensures bounded consensus tracking with reduced communication cost and prescribed-time performance under arbitrary initial conditions. Simulation experiments corroborate the effectiveness and feasibility of the proposed approach.

Accurate segmentation of multiple on-orbit spacecraft remains difficult in deep-space imagery because the scenes contain large uniform backgrounds, fine structural details, and limited labeled data. To address this problem, we propose SpaceSeg, a segmentation framework that adapts a vision foundation model to the spacecraft domain. The framework introduces a Multi-Scale Hierarchical Attention Refinement Decoder (MSHARD) to improve cross-scale feature decoding, a Spatial Domain Adaptation Transform (SDAT) training strategy to improve robustness to representative space-imaging disturbances, and a task-oriented objective that jointly optimizes segmentation accuracy and IoU-prediction quality. A lightweight connected-component-analysis module is also integrated into the pipeline for instance-aware target organization in multi-spacecraft scenes. We further construct SpaceES, a multi-scale on-orbit multi-spacecraft semantic segmentation dataset covering four space backgrounds and 17 spacecraft types. On SpaceES, SpaceSeg achieves 89.87% mIoU and 99.98% mAcc, setting a new state of the art among all evaluated baselines, surpassing the strongest competing method by 1.38 percentage points in mIoU with 59.6% fewer parameters, and exceeding the vanilla SAM2 baseline by 5.71 percentage points. Hardware-in-the-loop simulation and real satellite-to-satellite imagery experiments further support the practical relevance of the proposed method. Dataset and code are publicly available at https://github.com/Akibaru/SpaceSeg.

Visual 3D reconstruction is a key technology in the field of computer vision, with significant implications for tasks such as robot manipulation, autonomous driving, and virtual reality. In recent years, methods based on neural radiance fields and Gaussian splatting have gained considerable attention due to their outstanding performance. Surface mesh reconstruction based on the 2D Gaussian model achieves high accuracy, providing critical information for subsequent target-centered perception tasks. However, challenges such as self-occlusion caused by the complex structure of spacecraft, limited observation positions due to orbital constraints, and the high cost of orbital transfer restrict data acquisition. These limitations prevent comprehensive target information from being obtained, as is possible in ground-based sampling, resulting in reconstruction failures or reduced quality. To address the above problem, this paper proposes a uncertainty-aware 2D Gaussian splatting method for 3D mesh reconstruction under restricted viewpoint observation. First, the fixed color value of the 2D Gaussian ellipsoid is expanded into a probability distribution to measure uncertainty. Then, a color negative log-likelihood loss function is designed to train the Gaussian elements to estimate the mean and variance of the probability distribution. The proposed method is validated on a spacecraft reconstruction dataset collected from our local darkroom environment, demonstrating its effectiveness through qualitative and quantitative comparisons of 3D mesh reconstruction, uncertainty estimation, and novel view synthesis.

A dual quaternion variational integrator Kalman filter (DQVIKF) is developed to determine relative attitude and position for defunct non-cooperative spacecraft under low sampling rates. By utilized the terse transformation properties of dual quaternions, the linearized error state dynamics equations are formulated. Specifically, a multiplicative EKF incorporating with a dual quaternion variational integrator is established. Taking the advantage of conserved momentum, the proposed method maintains high accuracy estimation even under slow sampling rates. Finally, through experimental validations on a dual-manipulator experimental platform, the proposed method achieves a 26.97% improvement in attitude estimation accuracy and a 3.26-fold enhancement in relative position.

This paper proposes a safe integrated planning and control strategy to address spacecraft safe proximity control problems under collision/occlusion avoidance and relative velocity constraints. First, capsule-shaped envelopes are constructed for the pursuer and target by accounting for the spacecraft geometry, thereby reformulating collision/avoidance constraints as analytical computations of minimum distance, specifically, segment-to-segment and point-to-segment distance evaluations. Subsequently, a safe, integrated planning and control framework is developed. Safety filtering is employed to generate a safety trajectory and velocity complying with motion constraints, followed by a safety trajectory tracking controller that maintains tracking errors within predefined safety tubes. By judiciously designing safety margins, the proposed strategy achieves safe approach control under constraints even with tracking errors. Numerical simulations validate the effectiveness of the proposed strategy.

Serving as a critical component in laser pointing systems, fast steering mirrors (FSMs) encounter various control challenges in precise tracking tasks. In response to these challenges, a disturbance-rejection model predictive control (DR-MPC) method is proposed in this work. Initially, an auxiliary state space model of the tracking error is formulated by exploiting the minimal polynomials of the reference and disturbance signals. Subsequently, an implicitly constrained optimization problem is constructed, eliminating the need for explicit modeling of the unmeasurable disturbance. The minimal polynomial framework enables the transformation of the original problem into an explicit constrained optimization problem by linking past and predicted control inputs. Finally, the coordinate descent method is employed to iteratively solve the constrained optimization problem, enabling the successful deployment of the proposed controller on 10kHz high-speed hardware. Experimental results indicate that the proposed DR-MPC method exhibits substantial advantages regarding disturbance rejection, constraint handling, and dynamic tracking.

Performing target tracking and surveillance in dynamic obstacle environments requires maintaining continuous visual focus on the target while ensuring collision avoidance. This paper presents a safety-critical tracking control method that ensures dynamic obstacles remain outside the camera’s line of sight while simultaneously avoiding collisions between the chaser vehicle and obstacles. A novel real-time occlusion detection function is developed, and motion constraints are systematically integrated using a hybrid framework combining the artificial potential field (APF) method with an observer-based control strategy. To address temporal-sensitive tasks, a prescribed time controller (PTC) based on time-scale transformation technique has been proposed. Furthermore, a prescribed time linear extended state observer (PTESO) is proposed, featuring a simplified structure to enable rapid and accurate estimation of unknown environmental disturbances and non-linear terms. Finally, the effectiveness of the proposed method was verified via simulation in a simplified physical scenario.

This paper proposes a novel Q-learning-based dual-loop force tracking control framework for robot grinding tasks in uncertain environments. A complete system state-space model is established, incorporating interaction dynamics and the desired force. By augmenting the system state, a discount cost function is defined to quantify the tracking errors of the force and reference trajectory. The modified Q-learning method is systematically designed to iteratively compute the optimal control gain in a model-free manner. To mitigate force overshoot during the transition from free space to contact space, a force reference model and a transition mechanism for the control gain are designed. Simulations and experiments validate the method’s effectiveness in precise force tracking with minimal overshoot and robustness to environmental variations.

In asteroid exploration and orbital servicing missions with space robots, accurate 3D structural of the target is typically relied upon for planning landing trajectories and controlling movements. Unlike conventional neural radiance fields (NeRF) studies, which rely on full-view random sampling of targets that can be easily achieved on the ground, spacecraft operations present unique challenges due to the kinematic orbit constraint, the high cost of controlled motion, and limited fuel reserves. This results in limited observation of space targets. In order to obtain 3D structure under close-flybys and restricted observation, we proposed Uncertainty Neural Surfaces (UNS) model based on Bayesian uncertainty estimation. UNS enhance the precision of reconstructed target surfaces under constrained-views, providing guidance for subsequent imaging view design. Specifically, UNS introduces Bayesian estimation based surface uncertainty on neural implicit surfaces. The estimation is calculated based on the degree of self-occlusion of the target and the difference between rendered and actual colors. This approach enables uncertain estimation of 3D space and arbitrary view. Finally, extensive systematic evaluations and analyses of spacecraft model sampling in a local darkroom validate the sophistication of UNS in uncertainty estimation and surface reconstruction quality. Code is available athttps://github.com/YD-96/UNS.

This brief proposes an optimized time-synchronized control method based on reinforcement learning for the bipartite consensus tracking problem. The study considers multiagent system comprising leaders and followers, where followers interact through signed directed graphs. Some agents track the leader's state, while others converge to its opposite value. The proposed method employs a time-synchronized sliding mode control framework to ensure fixed-time bipartite consensus among agents with signed interaction topology. Reinforcement learning is integrated to optimize the control process, wherein an actor-critic architecture is utilized to minimize the Bellman residual, enabling optimal control performance. Theoretical analysis proves the fixed-time convergence and Bellman optimality of the system, with the upper bound of convergence time explicitly determined by controller parameters. Simulation experiments validate the effectiveness of the proposed method: all followers simultaneously achieve bipartite consensus within a fixed time, while reinforcement learning significantly and adaptively optimizes the control process.

Linear quadratic control with unknown value functions and dynamics is extremely challenging, and most of the existing studies have focused on the regulation problem, incapable of dealing with the tracking problem. To solve both linear quadratic regulation and tracking problems for continuous-time systems with unknown value functions, this article develops a discounted inverse reinforcement learning (DIRL) method that inherits the model-independent property of reinforcement learning (RL). More specifically, we first formulate a standard paradigm for solving linear quadratic control using DIRL. To recover the value function and the target control gain, an error metric is elaborately constructed, and a quasi-Newton algorithm is adopted to minimize it. Furthermore, three DIRL algorithms, including model-based, model-free off-policy, and model-free on-policy algorithms, are proposed. The latter two rely on the expert's demonstration data or the online observed data, requiring no prior knowledge of the system dynamics and value function. The stability, convergence, and existence conditions of multiple solutions are thoroughly analyzed. Finally, numerical simulations demonstrate the effectiveness of the theoretical results.

The mechanical antenna (MA) is a potential solution for the extremely-low-frequency (ELF, 3–30 Hz) transmitter enabling industrial informatization. It can have the advantages of miniaturization and high efficiency compared to conventional transmitters. However, the current ELF MA has an issue with both high inertia and a long delay in symbol switching. Not only does this lower the data rate, but it also causes unnecessary power consumption in operation. This article proposes a compact ELF transmitter based on a heterogeneous architecture of piezoelectric cantilevers and oscillating electret, as well as a relevant information transfer program. The actuator fabricated from composite piezoelectric material exhibits a rapid dynamic reaction. Merging this with efficient spectrum utilization increases the data rate and reduces power consumption. A proof of concept demonstration conducted at a frequency of 26.4 Hz attained a data rate of 21 bit/s while consuming a mere 1.52 W of power. In addition, increasing the charge density of the electret can expand the transmission distance without requiring extra power consumption, thus adapting to more challenging applications such as Underwater Internet of Things, Through-the-Earth communication, pipeline inspection, and underground detection.

Point set registration is an essential technique in the field of machine vision. In this article, we propose a robust global optimal solution to for the point set registration of feature points extracted from visual images, used in remote (300–120 km) space target tracking and targeting tasks. Specifically, we begin with cases where correspondences among point sets are known, establishing a cost function centered on maximizing the consensus set, wherein rotational and translational parameters are determined using voting methods and the branch-and-bound (BnB) algorithm, respectively. We then adapt this foundation to tackle the more challenging scenario of unknown correspondences in simultaneous pose and correspondence registration by adjusting the cost function and BnB bounding functions, supplemented with nested iterations to accurately determine rotation and translation parameters. Finally, the comprehensive experimental comparisons executed across synthetic and real datasets, along with ground-based spacecraft pose measurement setup, illustrate that, compared to existing methods, our proposed approach achieves precise estimations under the influence of noise and outliers. Moreover, compared to the globally nested BnB scheme, our method reduces computational complexity and enhances solution speeds.

This work develops an output feedback adaptive tracking control method based on dynamic regressor extension and mixing (DREM) for discrete-time uncertain parameter systems. A piecewise DREM estimator is designed for the uncertain parameters under conditions strictly weaker than the persistently excited condition, exhibiting the ability to capture the actual system dynamics in finite time. Accurate parameter estimation guarantees the performance of the controller utilizing the DREM estimator. Then, an adaptive optimal controller for any given reference trajectory is designed within the framework of receding horizon control. The system state and control input are theoretically guaranteed to remain bounded during tracking. The adaptive controller is restructured in a nonminimal state space to achieve output feedback without a state estimator. The proposed output feedback adaptive controller is fully consistent with its state-feedback counterpart. Simulation results for tracking different reference signals demonstrate the efficacy of the proposed strategy.

This paper addresses the issue of relative position tracking control for spacecraft rendezvous and proximity operations with a freely tumbling target, considering spatial motion constraints, including path and relative velocity limits, as well as mass uncertainty and external disturbances. To handle these challenges, a two-stage operation is proposed to ensure compliance with path constraints. In each stage, the constrained zones are parameterized using geometric features of the space, and corresponding artificial potential functions (APFs) are designed. Based on these APFs, an adaptive tracking controller is developed inside a backstepping control frame that allows the pursuer to reach terminal docking position while respecting the spatial motion constraints. Lyapunov stability analysis shows that the proposed control scheme ensures asymptotic convergence of tracking errors, provided that the APFs avoid local minima. Finally, numerical simulations are performed to validate the efficacy of the proposed control scheme.

This article investigates the autonomous exploration problem of an unmanned aerial vehicle (UAV) in a fully unknown three-dimensional (3D) space, subject to the constraints of collision avoidance, energy-saving, and computation consumption. To tackle this problem, a hybrid planning algorithm named FSHP is proposed. The algorithm consists of a novel local planner designed to explore unknown space within the onboard camera’s field of view (FoV) faster and less computationally. The local planner is a combination of the frontier-based and sampling-based methods, overcoming the bottlenecks of high computational time for the former and non-heuristics for the latter. Furthermore, the algorithm incorporates a global planner based on historical information to enhance performance in larger and more complex scenarios. The global planner includes a historical road map (HRM) using the rapidly-exploring random tree (RRT) and a historical tree (HST) based on the k-dimension (k-d) tree, built simultaneously. When no informative viewpoints are nearby, the planner replans trajectories globally to unexplored space. Finally, the proposed approach is evaluated in both simulations and real-world experiments, demonstrating the effectiveness and efficiency of the FSHP.Note to Practitioners—The motivation of this paper stems from the need to develop a fast and efficient autonomous exploration algorithm for a UAV for practical applications such as 3D reconstruction, search-and-rescue and military reconnaissance. Frontier-based and sampling-based methods are widely used to solve this problem due to their heuristics and low computational effort, respectively. However, either method can not meet the requirements related to exploration efficiency arising from increasingly complex and diverse tasks. To speed up the exploration process, reduce the exploration time and shorten the exploration path length, we propose this new method FSHP. It combines the advantages of global exploration (frontier-based methods) and local exploration (sampling-based methods) with random sampling in the frontiers. Furthermore, the replanning target selection and waypoints optimization schemes helps in reducing the path. Overall, this novel framework, FSHP, enables efficient and effective autonomous exploration tasks.

Decomposing a scene into its 3D geometry, surface material textures, and illumination is a challenging but important problem in computer vision and graphics. While recent neural implicit representation based works have shown tremendous advantages, existing methods are not applicable to images illuminated by a single dynamic point light. We propose an entirely self-supervised end-to-end neural implicit representation based reflectance decomposition algorithm for objects under a dynamic point light. Our method adopts a staged training framework to estimate the geometry, light source position, and surface material textures through volume rendering, self-shadow inverse rendering, and physical model based surface rendering respectively. This scheme allows accurate recovery of the surface material textures which are coupled to the dynamic light, improving the reflectance decomposition capability. For evaluation, we collect a new dataset of several synthetic and real world objects illuminated by a moving point light. Experiments show that our method achieves superior reflectance decomposition performance compared to state-of-the-art methods, and the recovered elements can be deployed in existing graphics pipelines to perform relighting, material editing, and scene composition.

This article introduces a novel approach called terminal sliding-mode control for achieving time-synchronized convergence in multi-input-multi-output (MIMO) systems under disturbances. To enhance controller design, the systems are categorized into two groups: 1) input-dimension-dominant and 2) state-dimension-dominant, based on signal dimensions and their potential for achieving thorough time-synchronized convergence. We explore sufficient Lyapunov conditions using terminal sliding-mode designs and develop adaptive controllers for the input-dimension-dominant case. To handle perturbations, we design a multivariable disturbance observer with a super-twisting structure, which is integrated into the controller. By utilizing the sliding-mode technique and the disturbance observer, the proposed controller ensures simultaneous convergence of all output dimensions. In the state-dimension-dominant case, where a full-rank system matrix is absent, only specific output elements converge to equilibrium simultaneously. We conduct comparative simulations on a practical system to highlight the effectiveness of our proposed method for the input-dimension-dominant case. Statistical results reveal the benefits of shorter output trajectories and reduced energy consumption. For the state-dimension-dominant case, we present numerical examples to validate the semi-time-synchronized property.

Horizontal annotation-based weakly supervised rotating object detection is a research field that has just been explored. This concept is expected to have a transformative effect on the advancement of data-driven rotating object detection methods. Existing pioneering researches directly regress the rotating rectangle based on angle description, which mainly face two limitations under the weakly supervised framework: 1) the regression form of weakly supervised learning is redundant relative to the training objective, thereby increasing the difficulty of model training and 2) the training objective of self-supervised (SS) learning does not have a close logical relationship with the test metrics, which may result in loss of accuracy. Addressing the above issues, this article proposes an angle-free weakly supervised rotating object detection framework, whose salient points mainly include the following: 1) by improving an angle-free rotating object representation, the decoupling between horizontal and rotating regression parameters in describing rotating objects is achieved; 2) a weakly supervised learning pipeline that is completely equivalent to the common horizontal object detection is designed to effectively relieve the difficulty of model training; and 3) a geometrically intuitive SS learning loss function is introduced to bridge the gap between the training objective and testing metrics. Experimental results on multiple large-scale remote sensing datasets confirm that the accuracy of this method is superior to the state-of-the-art (SOTA) weakly supervised rotating object detection methods, and is competitive even with SOTA fully supervised-related works.

A Line of Sight (LOS) angular acceleration-based three-dimensional (3-D) trajectory planning method is designed for aerial vehicles under constrained arrival without range measurement. To achieve this goal, a polynomial in time with arbitrary (greater than 3) orders including a single tuning parameter is developed to create the reference LOS profiles. By revealing the 3-D relation between the lead angle and reference LOS polynomials, the single parameter’s safe range is determined to create feasible LOS profiles that ensure the onboard sensor’s Field of View (FOV) is within its limit. The open-loop acceleration command is derived with time-only information, and the closed-loop command is constructed using the desired LOS angular acceleration profiles. Theoretical analysis proves that the actual LOS profiles perform the same as their desired ones. Moreover, the initial acceleration saturation is avoided, and the final acceleration and LOS rate are zero. The method does not involve the relative range detection, model linearization, switching logic, multiple design parameters, large scale optimization, and the all-covering numerical routine. Finally, extensive simulations are conducted to verify effectiveness of the proposed method.

This article addresses the event-triggered optimal tracking control problem for leader-follower spacecraft formation flying system using the adaptive dynamic programming technique. In order to solve the Hamilton–Jacobi–Bellman equation, a single-critic neural network (NN) is developed to approximate the optimal cost function. Moreover, by combining the parameter projection rule and gradient descent algorithm, a semiglobal adaptive update law is derived to tune the critic NN. In doing so, a continuous near optimal tracking controller is presented. Subsequently, an input-state-dependent event-triggered mechanism is designed to ensure that the near optimal tracking controller is implemented only when specific events occur, which significantly reduces the execution frequency of the control command. Remarkably, benefiting from the construction of an input-based triggering error, the conventional assumption on the Lipschitz continuity of the controller is tactfully removed, thus erasing the computable demand on the unknown Lipschitz constants. Rigorous analysis on the system stability and Zeno-free behavior is provided successively. Finally, numerical simulations on two formation satellites in low Earth orbit validate the effectiveness of the theoretical scheme.

This article addresses the event-triggered coordinated control problem for multiple Euler-Lagrange systems subject to parameter uncertainties and external disturbances. Based on the event-triggered technique, a distributed coordinated control scheme is first proposed, where the neural network-based estimation method is incorporated to compensate for parameter uncertainties. Then, an input-based continuous event-triggered (CET) mechanism is developed to schedule the triggering instants, which ensures that the control command is activated only when some specific events occur. After that, by analyzing the possible finite-time escape behavior of the triggering function, the real-time data sampling and event monitoring requirement in the CET strategy is tactfully ruled out, and the CET policy is further transformed into a periodic event-triggered (PET) one. In doing so, each agent only needs to monitor the triggering function at the preset periodic sampling instants, and accordingly, frequent control updating is further relieved. Besides, a parameter selection criterion is provided to specify the relationship between the control performance and the sampling period. Finally, a numerical example of attitude synchronization for multiple satellites is performed to show the effectiveness and superiority of the proposed coordinated control scheme.

This article investigates the autonomous spacecraft rendezvous and docking problem in the presence of space obstacle, path constraints, and thrust limitation constraint. By introducing the frame of explicit reference governor (ERG), a combination scheme of guidance and control is proposed to guarantee the system stabilization and constraints satisfaction. Specifically, an artificial potential function (APF) method is employed to guide a collision-free trajectory, and then the constraints are satisfied via limiting the states within the safe invariant set. Furthermore, a simplified method is proposed to obtain the maximum bound of the Lyapunov-based invariant sets, which ensures input bounded and collision avoidance. System convergence under the potential field is proved through the Lyapunov stability analysis. Numerical simulation results demonstrate the comprehensive validation and good performance of the proposed method.

The century-old development of the automotive industry has spawned one of the greatest Cyber-Physical Systems (CPSs) in the future-unmanned vehicles. The vehicle can obtain environmental information through different sensors, map it to the virtual coordinate system of the vehicle body to make decisions, and finally generate control instructions. However, a series of factors, such as complex road scenes, defective and irregular target sparse sampling, and large coding space, pose challenges to accurate, efficient, and stable perception results. To overcome the most challenging problem of dynamic target tracking, this paper designs a two-stage detection model based on non-uniform polar voxelization sampling of irregular 3D point cloud, which is used with local registration-based search to achieve efficient multi-target tracking. Non-uniform voxelization not only balances the spatial sampling and encoding efficiency of the point cloud for the backbone, but also adapts to the feature aggregation of the detection head, thereby achieving double acceleration. Finally, we tested our model on KITTI Tracking data. The comparison results show that the calculation speed of the final model is greatly improved and the tracking accuracy is competitive in all categories.

This article considers the connectivity preservation coordinated control problem for multiple spacecraft systems subject to limited communication resources and sensing capability. By constructing a novel bump function, a distance-dependent dynamic communication network model is first presented, which characterizes the interaction strength as a nonlinear smooth function varying with the relative distance of spacecraft continuously. Subsequently, based on an edge-tension potential function, a distributed event-driven coordinated control scheme is proposed to achieve formation consensus, while ensuring that adjacent spacecraft is always within the allowable connectivity range. Meanwhile, to avoid redundant data transmissions, a hybrid dynamic event-triggered mechanism with maximum triggering interval is developed to schedule the communication frequency among spacecraft. It is proven that the onboard communication resources occupation can be reduced significantly and the Zeno phenomenon is strictly excluded. Finally, the efficiency of the proposed method for, as an example, four-spacecraft formation system is substantiated.

Digital surface model (DSM) presents height information of the Earth’s surface and plays an important role in many remote sensing (RS) applications. Since the conventional acquisition of DSM is laborious and expensive, DSM reconstruction from monocular RS images has attracted extensive research in recent years, which is an ill-posed problem and thus rather challenging. Related works have achieved great accomplishments in this regard; however, they still face some limitations in training robustness, accuracy, and efficiency. To address the issues, a semantic joint monocular RS image DSM regression framework is proposed in this article, whose salient points include that: 1) semantic segmentation is integrated into the DSM regression task so that a shared backbone can extract complementary features from each objective to improve the performance of the individual task. Meanwhile, based on the consistency of the two training objectives, a two-stage joint loss function is introduced to improve the convergence and robustness of model training; 2) an encoding–decoding backbone is designed based on feature multiplexing, which simultaneously achieves multiscale feature fusion and information decoupling, thereby greatly reducing model parameters and improving efficiency while ensuring feature extraction effect; and 3) an iterative upsampling approach is introduced to transform the full-scale spatial features into large receptive-field and locally discriminative dynamic kernels, which are used to inpaint coarse-grained features while decoding, thus enhancing regression accuracy. Finally, experiments demonstrate the effectiveness of the proposal. It is easy to train and achieves superior or comparable accuracy compared with state-of-the-art related works while improving the efficiency by a large margin.

A passive fault tolerant control scheme is proposed for the full reentry trajectory tracking of a hypersonic vehicle in the presence of modeling uncertainties, external disturbances, and actuator faults. To achieve this goal, the attitude error dynamics with relative degree two is formulated first by ignoring the nonlinearities induced by the translational motions. Then, a multivariable twisting controller is developed as a benchmark to ensure the precise tracking task. Theoretical analysis with the Lyapunov method proves that the attitude tracking error and its first-order derivative can simultaneously converge to the origin exponentially. To depend less on the model knowledge and reduce the system uncertainties, an incremental twisting fault tolerant controller is derived based on the incremental nonlinear dynamic inversion control and the predesigned twisting controller. In this article, it is shown that not only the benefits of both incremental control and twisting control are inherited, but also their side effects are reduced. Notably, the proposed controller is user friendly in that only fixed gains and partial model knowledge are required. Numerical simulations in various cases and comparison studies are conducted to verify the effectiveness of the proposed method.

In this article, a practical three-dimensional (3-D) guidance is designed to achieve trajectory constrained flight, i.e., arriving at the destination with desired approach angles under limited field-of-view (FOV) and varying velocity. For this goal, the nonlinear engagement model is first transformed to a set of new differential equations in terms of the line-of-sight (LOS) angles. Then, the approach angle constraints are handled by constructing two reference range profiles as cubic polynomials of LOS angles. By solving the unknown coefficients involved in the polynomials, a semianalytical 3-D guidance solution is derived from the second-order range dynamics. To meet the FOV limit and avoid the initial guidance saturation, the achievable approach angle set is determined for capturability analysis by introducing the look angle dynamics with respect to the range profiles. The technique does not involve the model linearization, guidance switching logic, constant velocity assumption, and LOS rate information. Notably, only the LOS angle measurement is required during the guidance process once the initial conditions are provided, which makes it preferable for vehicles equipped with passive angles-only sensors. Extensive numerical simulations and Monte Carlo test are conducted to validate effectiveness and robustness of the proposed technique.

For high-resolution imaging implementations, the spacecraft attitude tracking control accuracy is crucial to determining the imaging quality. This investigation addresses the attitude tracking issue of imaging spacecraft subject to system uncertainties (unavailable inertia tensor, unexpected disturbances, and actuator faults). An adaptive sliding mode control (SMC) strategy is proposed to guarantee practical fixed-time closed-loop stability even in the presence of system uncertainties. Unlike existing methodologies, the sliding mode surface is developed to satisfy a novel sufficient condition of fixed-time stability. The sliding manifold design also circumvents the unwinding phenomenon arising in quaternion representations. Particularly, this controller is developed to generate a smooth control profile by using a new parameter update law. Rigorous Lyapunov analyses are further employed to ensure the fixed-time closed-loop stability irrespective of the system initial states. Finally, numerical examples are performed to demonstrate the feasibility and highlight the inherent features of the derived control law.

This article details the development and evaluation of a practical solution for path-constrained proximity maneuvers of spacecraft. Whereas no velocity measurement is utilized within the feedback structure, the controller rigorously enforces actuator magnitude constraints. Specifically, the control algorithm is constructed from a potential function method repelling the spacecraft from possible collisions. The proposed controller can guarantee potential functions to be navigated to the origin and thus overcome the stubborn local minima problem. Moreover, the control capability under any given control limit can be estimated and adjusted by changing the feedback gains. The specific performance with guaranteed safety can be also explicitly calculated by designers. The results are obtained through a Lyapunov-based stability analysis to prove uniformly ultimate boundedness. Numerical simulation results illustrate the performance and features of the developed control method.

This paper addresses the relative position coordinated control problem for spacecraft formation flying under an undirected communication graph, whilst considering mass uncertainties, external disturbances, and limited communication resources. A new event-triggered information transmission mechanism is first presented, where each spacecraft only requires accessing to the states of neighbors intermittently. Subsequently, a novel event-based coordinated control scheme is proposed by combining a smooth adaptive projection rule that confines the parameter estimations to well-defined bounded convex hypercubes. Under the proposed control framework, the information exchange among spacecraft occurs only when the specified event is triggered, thereby significantly reducing the communication load and saving the onboard resources. Furthermore, a positive lower bound on interevent time intervals is guaranteed to exclude Zeno behavior. By virtue of Lyapunov stability analysis and graph theory, it is proved that the relative position tracking errors can converge to small invariant sets around the origin, and that all closed-loop signals are bounded, even in the presence of mass uncertainties and external disturbances. Finally, numerical simulations are given to evaluate the effectiveness and highlight the advantages of the developed control algorithm.

In this paper, a novel adaptive neural network (NN) control scheme is proposed for a class of nonlinear systems with unknown control direction. By introducing some differentiable functions and high-order Lyapunov functions, the obstacle caused by unknown control direction in NN control is successfully circumvented and all closed-loop signals are shown to be uniformly bounded up to infinite time. Meanwhile, by introducing an error transformation technique, it is rigorously proved that the argument of the unknown nonlinearities remains within a compact set which can be explicitly calculated a priori, making the NN approximation always valid. Moreover, with the aid of a bound estimation approach, we effectively compress the impact of approximation errors and external disturbances and steer the tracking error into a predefined small residual set. Simulation results illustrate the effectiveness of the proposed scheme.

This paper is devoted to distributed adaptive containment control for a class of nonlinear multiagent systems with input quantization. By employing a matrix factorization and a novel matrix normalization technique, some assumptions involving control gain matrices in existing results are relaxed. By fusing the techniques of sliding mode control and backstepping control, a two-step design method is proposed to construct controllers and, with the aid of neural networks, all system nonlinearities are allowed to be unknown. Moreover, a linear time-varying model and a similarity transformation are introduced to circumvent the obstacle brought by quantization, and the controllers need no information about the quantizer parameters. The proposed scheme is able to ensure the boundedness of all closed-loop signals and steer the containment errors into an arbitrarily small residual set. The simulation results illustrate the effectiveness of the scheme.

This paper proposes a novel velocity-free nonlinear proportional-integral (PI) control allocation scheme for fault tolerant attitude control of flexible spacecraft under thruster redundancy. More specifically, the nonlinear PI controller for attitude stabilization without using body angular velocity measurements is firstly designed as virtual control of a control allocator to produce the three axis moments, and can guarantee uniform ultimately boundedness of the closed-loop system in the presence of external disturbances and possible stuck faults. The associated stability proof is constructive and accomplished by the development of a passivity filter formulations together with the choice of a Lyapunov function containing cross/mixed terms involving the various states. Then, a robust least squares based control allocation is employed to deal with the problem of distributing the three axis moments over the available thrusters under redundancy, in which the focus of this control allocation is to find the optimal control vector of actuator by minimizing the worst-case residual, under the condition of thruster faults and control constraints like saturation.

This paper is concerned with vibration control of a flexible spacecraft in the presence of parametric uncertainty/external disturbances as well as control input nonlinearity through distributed piezoelectric sensor/actuator technology. To satisfy pointing requirements and simultaneously suppress vibrations, two separate control loops are adopted. The first uses piezoceramics as sensors and actuators to actively suppress certain flexible modes by designing positive position feedback (PPF) compensators which add damping to the flexible structures in certain critical modes. The second feedback loop is designed based on an output feedback sliding mode control (OFSMC) design where control input nonlinearity is taken into consideration. Simulation studies for the proposed control strategy on a flexible spacecraft demonstrate the effectiveness of the proposed approach