Search Results (1,170)

To address the control coupling challenges arising from task heterogeneity of unmanned surface vehicle (USV) formation, a distributed hybrid deep reinforcement learning (HDRL) framework is proposed. The framework decomposes the formation task into two subtasks: leader path planning using the single-agent deep reinforcement learning (SADRL) algorithm and follower formation tracking using the multi-agent deep reinforcement learning (MADRL) algorithm. By embedding the physical constraints of the real Otter USV into the training loop, the policy network outputs are mapped to propeller revolutions that conform to its dynamic characteristics. To optimize control performance, a dynamic gating mechanism triggered by formation position error is developed to mitigate multi-objective interference through temporal task scheduling. Concurrently, a mirror mapping mechanism leveraging the physical symmetry of the formation is designed to achieve policy sharing and data augmentation. Furthermore, the desired velocity calculated based on rigid-body kinematics is used to achieve kinematic-compensated formation tracking. The simulation results indicate that, compared to the SADRL algorithm, the planning success rate of HDRL is improved by 44.59%. Furthermore, compared to the MADRL algorithm, the integrated tracking performance is enhanced by 21.79–39.64%. Full article

This paper investigates a tracking control problem for a class of strict-feedback nonlinear systems with time delays, asymmetric output constraints, and deception attacks on the controller. First, by introducing a new error conversion technology, any nonzero and bounded initial state is converted to zero, which not only solves the overshoot/oscillation problem of the output during the constraint switching phase but also unifies the control design of constrained and unconstrained systems. Second, a barrier function with asymmetric output constraints is designed, which converts the problem of satisfying the tracking control of nonlinear systems under output constraints into one of ensuring the boundedness. In addition, radial basis function neural networks (RBFNNs) are utilized to handle both unknown uncertain terms and deception attacks simultaneously. By utilizing the new asymmetric delayed barrier function error together with an RBFNN technique, the tracking error is ultimately uniformly bounded, regardless of the presence or absence of output constraints. Finally, the superiority of the proposed strategy is verified through its simulation on an unmanned aerial vehicle (UAV) system. Full article

The security of heterogeneous unmanned systems (HUSs) operating in open environments has become a key concern. Therefore, this paper focuses on the fractional-order adaptive resilient clustering synchronization control for a class of networked HUSs composed of multiple unmanned surface vehicles and unmanned aerial vehicles subject to deception attacks and denial-of-service (DoS) attacks. First, a distributed cluster trajectory generator is designed for each vehicle in a networked HUS to estimate the output trajectory of the leader in their respective clusters in the presence of DoS attacks on the communication layer. Then, by combining backstepping control and fractional calculus, and immersion and invariance (I&I) theory, a fractional-order adaptive synchronization tracking controller is developed to form the desired cluster formation configuration under disturbances and actuator attacks. Among them, the I&I adaptive strategy is designed to estimate the lumped uncertainty caused by attacks and disturbances. Finally, stability analysis and simulation experiments demonstrate the effectiveness of the proposed control scheme. Full article

To address the challenge of stable tracking of moving maritime targets by unmanned aerial vehicle(UAV) swarm in environments with threat zones and platform failure risks, this paper proposes a cooperative tracking and guidance strategy integrating Distributed Model Predictive Control (DMPC) with Sequential Quadratic Programming (SQP). A cooperative tracking model is developed incorporating UAV kinematics, environmental threats, stereo-vision positioning, and field-of-view constraints. Two original strategies are introduced within the DMPC framework: an altitude-cooperative target recapture strategy reduces target total loss duration by approximately 7 s compared to fixed-altitude baselines, while a distributed formation reconfiguration strategy restores stable tracking within 10 s after member failure and ensures safe inter-UAV separation. A multi-constraint trajectory tracking controller based on DMPC-SQP achieves real-time co-optimization of threat avoidance, formation maintenance, and tracking accuracy. Simulation results in dense threat environments demonstrate a 93.4% Quadratic Programming feasibility rate, with mean tracking error reduced by 25.4% over fixed-altitude DMPC and 48.7% over methods based on the Linear Quadratic Regulator (LQR), while maintaining robust performance under 300 ms communication delay, sensor noise, and moderate wind disturbance. Full article

Deploying unmanned aerial vehicles (UAVs) cooperatively with legged robots for disaster response and inspection requires autonomous docking on miniature walking platforms. This study addresses the problem of landing a foldable quadrotor onto the back of a trotting wheel-legged robot ($300\times 180$ mm) and subsequently taking off while carrying it as a payload. Four tightly coupled challenges distinguish this task from conventional mobile-platform landing: (i) an extremely small landing surface, (ii) gait-induced periodic vibrations at $2.5$ Hz, (iii) continuous platform translation at $0.3$–$0.8$ m/s, and (iv) surface docking that requires simultaneous position and attitude matching rather than mere point tracking. The proposed framework comprises four components: (1) a novel single-servo crank-rocker folding mechanism that reduces the folded body footprint by 48.5% and the maximum linear dimension from 590 mm to 309 mm (↓47.6%) compared with the prior dual-servo design; (2) a staged Continuous Fast Nonsingular Terminal Sliding Mode (CFNTSM) controller combined with a Gait-Frequency-Aware Finite-time Extended Observer (GFA-FEO); (3) a Feature-wise Linear Modulation Soft Actor-Critic (FiLM-SAC) residual reinforcement-learning policy conditioned on physical states and mission phase, with an adaptive trust weight $\lambda \left(t\right)$; and (4) a payload-adaptive takeoff strategy with parameter hot-switching to handle the twofold mass increase. Extensive Monte Carlo simulations and ablation studies across three experiment groups demonstrate that the proposed hierarchical framework achieves sub-centimetre (<10 mm) position accuracy and <3° attitude matching on a walking platform. Quantitatively, the full method reduces docking RMSE by 42% relative to the model-based CFNTSM + GFA-FEO controller without residual RL (4.2 vs. 7.2 mm) and reduces post-lock takeoff RMSE by 63% through FEO hot-switching (16.2 vs. 44.2 mm). Full article

Unmanned aerial vehicles (UAVs) serve as both communication relays and aerial couriers in modern urban logistics networks. Conventional trajectory optimization methods assume perfect localization and isotropic free-space tracking signal propagation, which limits their effectiveness in urban canyons. To address the positional uncertainty and signal blockage from buildings, we propose a digital twin-driven framework for continuous trajectory and resource optimization in UAV swarms. We model an urban environment containing random high-rise structures, applying a non-line-of-sight (NLoS) uncertainty to reflect realistic communication degradation. The digital twin (DT) architecture utilizes a dual-layer spatial representation that captures a dynamically decaying positional uncertainty radius of the recipient. We define a strict visual localization boundary that initiates deterministic target tracking with a state transition mechanism. To manage the complexity of swarm routing, we apply Density-Based Spatial Clustering of Applications with Noise (DBSCAN), assigning one UAV courier and one logistics transfer station to each cluster. The system executes a continuous re-optimization loop using an adaptive multi-objective Genetic Algorithm. This framework jointly minimizes cumulative outage probability and total flight time while enforcing a signal-to-noise ratio threshold and throughput constraints. This continuous adaptation mechanism mitigates NLoS blockage risks, supporting reliable communication and efficient delivery in Global Navigation Satellite System (GNSS)-degraded and obstacle-dense urban environments. Full article

We investigated the potential of multi-layer extreme learning machines (MLELMs) for trajectory tracking in autonomous unmanned vehicles, focusing on an unmanned surface vehicle (USV). MLELMs were used to design position and attitude controllers within a two-loop control architecture, ensuring that the USV accurately follows a reference trajectory. The performance of an MLELM-based controller in a tracking task was evaluated via numerical simulations of a USV dynamic model governed by nonlinear equations. The computational results confirmed the feasibility of the MLELM to accomplish this task with appropriate accuracy, demonstrating its potential applicability in control systems. Full article

The trajectory tracking problem of underactuated unmanned surface vehicles (USVs) with unknown physical parameters arising from hydrodynamic effects is addressed using a robust control strategy based on a sliding-mode dynamic neural network identifier. To handle the unknown physical parameters, a dynamic neural network identifier with a novel structure is developed, enabling the construction of an equivalent mathematical model of the USV dynamics. To compensate for the underactuated nature of the system, a coordinate transformation is introduced. Using this transformation, together with the proposed identifier, a nonsingular sliding-mode controller is designed. Lyapunov-based analysis establishes finite-time convergence of the neural weight estimation errors to zero and convergence of the identification errors to a bounded neighborhood of zero. Furthermore, once the identification errors enter this bounded region, they asymptotically converge to zero. In addition, the closed-loop stability analysis guarantees finite-time convergence of the tracking errors. The effectiveness of the proposed identifier–controller framework is validated through simulation studies that incorporate explicit actuator saturation constraints and external disturbances to emulate realistic operating conditions. These results demonstrate the practical applicability of the proposed control strategy, as the commanded inputs remain within the physical limits of the propulsion system. Comparative results with a state-of-the-art model-based super-twisting controller show that the proposed approach achieves comparable tracking performance while eliminating the need for prior knowledge of the system’s dynamic parameters. Full article

In vision-based low-altitude unmanned aerial vehicle (UAV) remote sensing, detecting small targets accurately and maintaining stable tracking under fast-motion conditions remain significant challenges. Specifically, small-object detection suffers from low feature representation, while camera motion often induces tracking drift and identity switches. To address these issues, this paper proposes a novel small target detection and tracking algorithm named TCYOLO-SofByteTrack, which integrates an improved YOLOv11 with ByteTrack. The algorithm comprises two core innovative modules: First, the TCYOLO detector is designed by integrating the C3k2-TA feature enhancement module with triplet attention mechanism to achieve cross-dimensional interaction modeling, significantly improving small target feature representation capability and network contextual awareness. A Cross-Scale Feature Fusion Module for UAVs (CCFM-UAV) is constructed to provide precise detection support for small targets at different scales. Second, building upon the ByteTrack framework, the SofByteTrack tracker is designed, which introduces a sparse optical flow-based motion compensation strategy. This strategy estimates and compensates for image displacement caused by UAV motion in real time, ensuring the stability of target bounding boxes under fast-motion conditions, thereby effectively mitigating tracking drift and identity switches. Experimental results demonstrate that the TCYOLO detector achieves a 7.4% improvement in mAP for small target detection compared to the baseline YOLOv11 model. The complete TCYOLO-SofByteTrack tracking algorithm achieves a HOTA score of 45.3%, MOTA of 42.7%, and IDF1 of 57.8%, representing improvements of 4.5%, 5.9%, and 8.0%, respectively, over the baseline methods. Furthermore, the number of successfully tracked targets increased by 37.3%, while identity switches decreased by 23.4%. These results demonstrate the notable advantages of the proposed method in small target detection accuracy, tracking precision, and identity consistency. Its generalization capability is further validated on a custom highway inspection dataset. Moreover, deployment tests on an NVIDIA Jetson Orin NX platform show that, compared to YOLOv11n, the proposed algorithm achieves higher detection accuracy while still meeting real-time processing requirements, highlighting its practical applicability in resource-constrained scenarios. Full article

This paper mainly focuses on the prescribed-time attitude tracking problem of quadrotor unmanned aerial vehicles (QUAVs) with unknown disturbances and actuator faults. Firstly, a prescribed-time disturbance observer (PTDO) is designed based on the prescribed-time stability theorem to estimate the compound lumped disturbance consisting of unknown disturbances and actuator faults, and its prescribed-time stability is proved. Then, a PTDO-based prescribed-time fault-tolerant controller is designed by using the sliding mode control method. A sliding mode fault-tolerant controller is designed based on a prescribed-time sliding surface and reaching law, and its prescribed-time stability is analyzed and proved. The controller aims to achieve the convergence of attitude tracking errors for the QUAVs within a prescribed time in the presence of unknown disturbances and actuator faults. In addition, the convergence time of the controller is determined by simple prescribed-time parameters. The simulation results show that the proposed prescribed-time fault-tolerant control scheme is effective. Full article

Coal mines constitute a vital component of the national security system, where the extraction and utilisation of coal resources directly impact mine stability and engineering safety. Therefore, addressing the surface deformation issues caused by repeated mining activities across multiple coal seams at the Daliuta Mine, this study proposes a multi-source remote sensing monitoring technology system, which aims to improve the accuracy of surface deformation in the mining area. At the mining area scale, optimised Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technology utilised 168 Sentinel-1A image scenes to generate 789 interferometric image pairs. This extracted the long-term surface deformation field of the Daliuta mining area, revealing the spatiotemporal evolution patterns of surface subsidence under repeated mining activities. To further enhance monitoring accuracy and reliability, this study proposed a Satellite Aerial-Prior Weighting (SA-PW) method. This approach integrated satellite-based time-series InSAR, aerial Pixel Offset Tracking (POT), and unmanned aerial vehicle light detection and ranging (UAV LiDAR) data through a dynamic priority weighting model. This enabled the synergistic inversion of high-precision surface deformation parameters for repeatedly mined areas. Results demonstrated that compared to SBAS-InSAR alone, the SA-PW method achieved a 10% improvement in surface deformation parameter accuracy. By constructing a dynamic priority-weighted model, this approach systematically integrated multi-source data to achieve collaborative inversion of high-precision surface deformation parameters in repeatedly mined areas. Results demonstrated that compared to SBAS-InSAR and UAV LiDAR methods, SA-PW data fusion yielded higher accuracy, with MAE and RMSE values of 60 mm and 90 mm on the A line, and 57 mm and 83 mm on the H line, respectively. This method facilitates the establishment of integrated air–space–ground real-time monitoring networks for mining areas, enables subsidence hazard early warning and management, identifies key zones for ecological restoration, explores synergistic mechanisms between repeated mining and ecological rehabilitation, and promotes safe and green mining operations and development. Full article

Target tracking of unmanned aerial vehicle (UAV) swarms remains a significant challenge due to highly maneuverable target swarms and complex environments. To address these challenges, a hierarchical target tracking architecture is proposed, comprising a leader layer and a follower layer. This design reduces task complexity while improving formation adaptability and system scalability. In the leader layer, a distributed time-varying optimization model and a distributed protocol are developed to enable the UAV swarm to track highly maneuverable target swarms in real time. In the follower layer, a control protocol based on an affine transformation is employed to enable adaptive formation control under complex environmental constraints (e.g., threat avoidance). Moreover, the convergence performance of the proposed method is rigorously demonstrated through theoretical analysis. Finally, simulation results validate the convergence, feasibility, and scalability of the proposed method. Comparative simulations further demonstrate the superiority of the proposed method. Full article

Multirotor unmanned aerial vehicles (UAVs) suffer from significant control performance degradation during aggressive maneuvers, primarily due to aerodynamic nonlinearities and coupling effects. Conventional fixed-gain PID controllers struggle to simultaneously satisfy performance and robustness requirements across the wide flight envelope. To address this challenge, this paper presents a novel hierarchical safety-constrained reinforcement learning (RL) framework for adaptive PID tuning: the inner loop employs fixed gains, the outer loop leverages proximal policy optimization (PPO) for online adaptive gain scheduling, and linear matrix inequality (LMI) constraints delineate robust parameter boundaries for the adaptive exploration. Importantly, the LMI feasibility strictly guarantees theoretical stability for the fixed inner-loop parameters at the linearization vertices within a linear parameter-varying (LPV) framework. Concurrently, the online outer-loop RL stage is protected by safety boundaries and a Lagrangian penalty mechanism acting as an effective engineering safeguard rather than a rigorous global stability proof. Comprehensive high-fidelity simulation benchmarks demonstrate that, compared with a baseline fixed-gain PID controller, the proposed framework reduces overshoot by 18.5% in high-speed step responses and improves the overall mean RMSE by 15.0% across 100 randomized mixed-trajectory trials (with improvements of up to 40.9% in highly dynamic scenarios), yielding consistent gains in trajectory tracking accuracy and disturbance rejection despite uncertain model variations. By seamlessly blending control-theoretic hard constraints with RL-based soft-parameter tuning, the proposed architecture offers a safe and highly adaptive solution for large-envelope flight control, demonstrating strong engineering relevance. Full article

With the gradual development of science and technology, increasingly complex application environments impose higher requirements on the control performance of quadrotor unmanned aerial vehicles (UAVs). This requires UAVs to achieve high-performance tracking control under various challenging conditions, such as model uncertainties, external disturbances, actuator saturation, and actuator faults. Considering these issues, this paper proposes a novel fixed-time controller. First, to address the external disturbances and model uncertainties that UAVs may encounter during flight, a non-singular fixed-time terminal sliding mode control method is proposed, and a variable exponential fixed-time adaptive sliding mode disturbance observer is introduced to improve the estimation accuracy of the lumped disturbances. Secondly, considering the impact of actuator input saturation, an auxiliary system is constructed to mitigate the actuator saturation problem. Finally, a fixed-time fault-tolerant control scheme with actuator saturation and prescribed performance constraints is investigated for quadrotor UAVs. The convergence performance of the controller is rigorously established based on Lyapunov stability theory. Comparative simulation results are provided to demonstrate the effectiveness of the proposed control strategy. Full article

This paper investigates the fixed-time tracking control problem for underactuated quadrotor unmanned aerial vehicle systems subject to mass parameter uncertainties and user-defined time constraints. For the parameter uncertainties inherent in the system, the approximation capability of neural networks is exploited for compensation. Combined with the backstepping technique, this work proposes a new adaptive control strategy to ensure that the error variable converges within a small region near zero within a fixed time, and the designed controller effectively avoids singularity issues. Furthermore, a unified constraint framework with a shift function is introduced into the controller design, thereby providing a unified framework for user-defined time constraints that can flexibly handle different constraint scenarios without altering the control architecture. Finally, simulations are conducted to validate the effectiveness of the proposed method. Full article