Search Results (171)

This paper proposes a novel prescribed-time formation tracking control farmework of multi-fixed-wing UAVs under external disturbance and actuator failures. As the complexity of aerial missions intensifies, achieving precise position and attitude tracking within a user-defined upper bound of settling time becomes a paramount challenge for intelligent swarm systems. Unlike traditional finite or fixed-time methods, where convergence depends on initial states or suffers from conservative estimation, the proposed approach ensures stability within a prescribed time independent of initial conditions. A key innovation is the introduction of a piecewise reference convergence differential function. This mechanism eliminates the need for state transitions, thereby reducing computational complexity while ensuring smooth tracking without control surface chattering across the entire mission. Additionally, a prescribed-time sliding mode disturbance observer is developed to provide precise and timely compensation for external disturbances and actuator faults. Rigorous Lyapunov analysis proves that all closed-loop signals are bounded and the tracking errors converge to a small neighborhood of zero within the predefined time. Numerical simulations demonstrate that, under time-varying disturbances and actuator faults, the disturbance estimation errors converge within 4 s, while both attitude and velocity tracking errors converge within 6 s, achieving fast transient response and high tracking accuracy. The UAV swarm successfully maintains the desired formation during aggressive maneuvers, including speed variations, climbing, and diving. These results verify that the proposed method provides a computationally efficient, robust, and high-precision solution for time-critical formation control of fixed-wing UAV swarms under complex uncertainties. Full article

Quadrotor unmanned aerial vehicles (UAVs) operating in complex and dynamic environments, especially when subjected to unknown disturbances such as wind, can experience significant degradation in the stability of trajectory tracking control. Current research on UAV control has proposed algorithms that exhibit good disturbance rejection capabilities for small and weak disturbances, but their effectiveness decreases significantly as the disturbance magnitude increases. To address this issue, this paper proposes a hybrid control strategy that combines a Radial Basis Function Neural Network (RBFNN) with Integral Terminal Sliding Mode Control (ITSMC). The RBFNN is designed as an online disturbance observer, capable of estimating and compensating external disturbance forces and torques in real time, with an adaptive weight law. The ITSMC utilizes an integral term to eliminate steady-state errors and a terminal sliding mode term to achieve finite-time convergence of tracking errors. Simulation results demonstrate that the proposed controller maintains high-precision trajectory tracking and attitude control performance under various disturbance conditions, exhibiting strong robustness and anti-disturbance capability, and outperforms other controllers in overall performance. Full article

Model-aided navigation is a key approach for enhancing the positioning accuracy of autonomous underwater vehicles (AUVs). However, its precision is often degraded by model-based velocity errors arising from attitude-induced deviations and uncertainties in the mapping between propeller rotational speed and vehicle velocity. To overcome these limitations, this study proposes an attitude-compensated and acoustics-calibrated model-aided navigation framework for AUVs. The framework derives the vertical velocity from pressure sensor depth data to correct attitude-related model errors. It also dynamically refines the mapping between propeller speed and velocity using long-baseline (LBL) acoustic positioning data when LBL measurements are available. A sea trial was conducted in the South China Sea at a depth of 2000 m to verify the proposed method. The results showed that the system maintained a positional accuracy of 509 m over 5 h beyond LBL coverage. This outcome demonstrates its ability to achieve sustained high-precision navigation without external assistance. Full article

This paper addresses the three-dimensional trajectory tracking problem for underactuated autonomous underwater vehicles subject to external disturbances and model uncertainties in complex ocean environments. A robust control method integrating backstepping dynamic surface control and non-singular terminal sliding mode is proposed. Firstly, based on the kinematic and dynamic models of autonomous underwater vehicle, virtual velocity commands are constructed via backstepping approach to stabilize the position and attitude errors. To circumvent the “differential explosion” problem inherent in conventional backstepping control caused by repeated differentiations of virtual control variables, first-order low-pass filters are introduced to construct dynamic surface control, yielding smooth derivatives of virtual velocity commands. Secondly, to enhance convergence rate and robustness, a non-singular terminal sliding surface is designed at the dynamic level, and a terminal reaching law is formulated to achieve finite-time convergence of velocity tracking errors. Furthermore, to compensate for external disturbances and unmodeled dynamics, a disturbance observer based on the super-twisting algorithm is developed, enabling finite-time high-precision estimation of lumped disturbances, with the estimation results incorporated into the control law for feedforward compensation. Finally, comparative simulations are conducted under two typical disturbance scenarios. The results demonstrate that the proposed method achieves instantaneous disturbance estimation (reducing convergence time from 3 s to near zero), significantly smoother control inputs, and superior tracking accuracy with RMSE as low as 0.6788 m and MAE as low as 0.1468 m, reducing errors by up to 30.6% compared to baseline methods. Full article

Offshore operational environments are inherently stochastic, with waves, currents, and wind loads exerting a significant influence on vessel attitude and equipment stability. While Stewart platforms enable active motion compensation, conventional control strategies frequently suffer from time delays, actuator lag, and limited disturbance rejection, resulting in inadequate performance under complex sea conditions. To overcome these limitations, this paper presents a wave compensation control strategy for a Stewart platform that integrates deep learning-based prediction with active disturbance rejection control (ADRC). A bidirectional long short-term memory (BiLSTM) network is developed to predict vessel attitude in advance. The predicted attitude is transformed into actuator displacement commands through the inverse kinematics of the Stewart platform. An ADRC-based displacement controller is then designed to achieve fast and robust compensation under wave disturbances. Six-degree-of-freedom (6-DOF) dynamic models of a catamaran and a Stewart platform are established in Simulink and Simscape, and sea states 2, 4, and 6 are simulated using an enhanced Joint North Sea Wave Project (JONSWAP) wave spectrum. The simulation results show that, compared with Proportional–Integral–Derivative (PID) and ADRC methods, the proposed BiLSTM-ADRC strategy reduces the roll root mean squared error (RMSE) by 76.6% and 73.2%, and pitch RMSE by 64.1% and 58.1%, respectively, demonstrating an improved attitude stabilization performance. Full article

Accurate measurement of sea-surface winds is critical for climate science, physical oceanography, and the rapidly expanding offshore wind energy sector. Buoy-based platforms—moored meteorological buoys, drifters, and floating LiDAR systems (FLS)—provide practical alternatives to fixed offshore structures, especially in deep water where bottom-founded installations are economically prohibitive. Yet these floating platforms are subject to continuous pitch, roll, heave, and yaw motions forced by wind, waves, and currents. Such six-degree-of-freedom dynamics introduce multiple error pathways into the measured wind signal. This paper synthesizes the current understanding of motion-induced measurement errors and the techniques developed to compensate for them. We identify four principal error mechanisms: (1) geometric biases caused by sensor tilt, which can underestimate horizontal wind speed by 0.4–3.4% depending on inclination angle; (2) contamination of the measured signal by platform translational and rotational velocities; (3) artificial inflation of turbulence intensity by 15–50% due to spectral overlap between wave-frequency buoy motions and atmospheric turbulence; and (4) beam misalignment and range-gate distortion specific to scanning LiDAR systems. Compensation strategies have progressed through four recognizable stages: fundamental coordinate-transformation and velocity-subtraction algorithms developed in the 1990s; Kalman-filter-based multi-sensor fusion emerging in the 2000s; Response Amplitude Operator modeling tailored to FLS platforms in the 2010s; and data-driven machine-learning approaches under active development today. Despite this progress, key challenges persist. Sensor reliability degrades under extreme sea states precisely when accurate data are most needed. The coupling between high-frequency platform vibrations and turbulence remains poorly characterized. No unified validation framework or benchmark dataset yet exists to compare methods across platforms and environments. We conclude by outlining research priorities: end-to-end deep-learning architectures for nonlinear error correction, adaptive algorithms capable of all-sea-state operation, standardized evaluation protocols with open datasets, and tighter integration of intelligent software with next-generation low-power sensors and actively stabilized platforms. Full article

To mitigate the position divergence resulting from attitude error amplification in conventional fully coupled architectures, this study proposes a decoupled navigation architecture based on the Versatile Quaternion-based Filter (VQF). This architecture removes attitude estimation from the state vector, forming a two-layer structure comprising an independent attitude module and a navigation filter. The VQF is integrated as a standalone attitude module via a standardized interface. An uncertainty quantification model is developed by extracting the VQF’s internal correction states, which maps deviations among intermediate quaternion values to a measurable uncertainty metric. To compensate for the loss of cross-covariance induced by decoupling, a dual-layer compensation mechanism is introduced: a base layer adjusts the overall uncertainty using innovation statistics, while a compensation layer explicitly propagates attitude uncertainty through parameterized noise matrices. Experimental results demonstrate that the proposed method achieves notable improvements in positioning accuracy and significantly suppresses extreme errors in high-curvature scenarios. The approach is particularly effective for high-curvature, high-dynamic applications where process noise modeling is inherently difficult. Compared to traditional fully coupled architectures, the decoupled architecture offers enhanced robustness. The complementary characteristics identified between the two architectures provide valuable insights for expanding the operational envelope of underwater navigation systems. Full article

Quadrotor unmanned aerial vehicles demonstrate broad application prospects, yet existing research still lacks a comprehensive solution that simultaneously addresses efficiency, disturbance rejection, environmental adaptability, and precision in their control performance. To achieve prescribed-time convergence and prescribed tracking performance, this work proposes a composite control scheme that integrates prescribed-performance control, disturbance estimation, and terminal sliding-mode control. First, a prescribed-time adaptive composite disturbance observer is developed to estimate and compensate for system composite disturbances, and a stability analysis shows that the disturbance estimation error converges to a small neighborhood of the origin within a prescribed time. Second, the system is decomposed into position and attitude subsystems, enabling tailored hierarchical control-law design and analysis based on their distinct dynamics. For position control, a prescribed-performance control method is employed, incorporating a prescribed-time performance function that accommodates large initial deviations, thereby guaranteeing convergence of the position-tracking errors to a small neighborhood within a specified time. For attitude control, a prescribed-time terminal sliding-mode surface and corresponding control law are designed to eliminate singularities and ensure convergence of the attitude errors to a small neighborhood within a predetermined time. The stability of both subsystems is rigorously substantiated through theoretical analysis. Finally, comparative simulation results confirm the effectiveness and superiority of the proposed control strategy. Full article

Ground-based full-physical experiments for space rendezvous and docking serve as a critical step in verifying the reliability of docking technology. The high-precision active attitude setting of spacecraft simulators represents a key technology for ground-based full-physical experiments. In order to satisfy the requirement for high-precision attitude control in these experiments, this paper proposes an enhanced method based on auto disturbance rejection control (ADRC). This paper addresses the limitations of traditional deadband–hysteresis relay controllers, which exhibit low steady-state accuracy and insufficient disturbance rejection capability. This approach employs a nonlinear extended state observer (NESO) to estimate and compensate for total system disturbances in real time. Concurrently, it incorporates an adaptive mechanism for deadband and hysteresis parameters, dynamically adjusting controller parameters based on disturbance estimates and attitude errors. This overcomes the trade-off between accuracy and power consumption that is inherent in fixed-parameter controllers. Furthermore, the method incorporates a nonlinear tracking differentiator (NTD) to schedule transitions, enabling rapid attitude settling without overshoot. The stability analysis demonstrates that the proposed controller achieves local asymptotic stability and global uniformly bounded convergence. The simulation results demonstrate that under three typical operating conditions (conventional attitude setting, pre-separation connector stabilisation, and docking initial condition establishment), the steady-state attitude error remains within ±0.01°, with convergence times under 3 s and no overshoot. These results closely match ground test data. This approach has been demonstrated to enhance the engineering applicability of the control system while ensuring high precision and robust performance. Full article

Addressing the difficulty of intuitively identifying and effectively correcting induced heave error in multibeam measurements, this paper proposes a two-stage methodology comprising error identification and correction. This scheme includes an error discrimination method based on regression diagnostics and an error correction method based on Partial Least Squares Regression (PLSR). By establishing a mathematical model between bathymetric discrepancies and attitude parameters, statistical diagnosis and effective identification of the error are achieved. To further mitigate the impact of induced heave error on bathymetric data, an elimination model based on PLSR is developed, enabling high-precision prediction and compensation of the induced heave error. Validation using field survey data demonstrates that this method can effectively estimate the installation offset parameters of the attitude sensor. After correction, the root mean square of bathymetric discrepancies between adjacent survey lines is reduced by approximately 78.8%, periodic stripe-shaped distortions along the track direction are essentially eliminated, and the quality of terrain mosaicking is significantly improved. This provides an effective solution for controlling induced heave error under complex topographic conditions. Full article

In a mother–child underwater bio-inspired robotic system, the equivalent lever arm between the master and slave inertial navigation systems (INSs) varies with launcher attitude changes and structural flexure. This time-varying lever arm introduces hard-to-model systematic velocity errors that degrade the accuracy and filter convergence of velocity difference-based transfer alignment. Traditional rigid body compensation relies on precise, constant lever-arm parameters and fails when booms, launch tubes, or flexible manipulators undergo appreciable deformation or reconfiguration. To address this, we augment a “velocity–attitude joint matching and innovation-based adaptive Kalman filter (AKF)” framework with an attention-based Long Short-Term Memory (LSTM) feed-forward module. Using only a short, real-time Inertial Measurement Unit (IMU) sequence from the slave INS, the module predicts and compensates the velocity bias induced by the lever arm. Numerical simulations of an underwater bio-inspired robot deployment scenario show that, under typical maneuvers (acceleration, turning, fin-flapping, and S-curve), the proposed method reduces the root-mean-square (RMS) misalignment angle error from about 14.5′ to 5.2′ and the RMS installation error angle from 8.8′ to 3.0′—average reductions of about 64% and 66%, respectively—substantially improving the robustness and practical applicability of transfer alignment under time-varying lever arms and flexible disturbances. Full article

During the transposition of a target satellite, dynamic coupling between the target satellite, the manipulators, and the base frequently leads to disturbances in the base’s attitude. To deal with the issue, this paper proposed a pose stabilization method for the base of the post-capture combined system using the feedforward compensation PD control. Firstly, the mission sequence for repositioning a target satellite using a discrete-serpentine heterogeneous dual-arm space robot (DSHDASR) was analyzed. The dynamics model of the combined system, composed of the DSHDASR and a target satellite, was established based on the Newton–Euler recursive formulation. Then, the pose stabilization method integrating dynamic feedforward compensation and PD control was developed to stabilize the base of the combined system. Finally, the mission of target satellite transposition was simulated through the co-simulation model. Compared with the traditional control algorithms, the position accuracy and attitude accuracy for the proposed method showed an overall improvement. The results demonstrated that the proposed method significantly reduced base pose errors under high-load and disturbed conditions. Full article

This paper presents the design, implementation, and experimental validation of a high-precision angular measurement system based on a round inductosyn. Dedicated hardware circuits, including excitation, signal conditioning, and resolver-to-digital conversion modules, together with software algorithms for coarse–fine data fusion and linear interpolation-based error compensation, are developed to achieve accurate and stable angular measurement. Experimental results obtained from repeated measurements over a full rotation demonstrate reliable system operation and effective suppression of nonlinear errors. After compensation, the residual angular error is limited to within ±3″, while measurement consistency across repeated experiments is significantly improved. The output angle exhibits good continuity and stability, confirming the feasibility and effectiveness of the proposed system for high-precision servo control and aerospace attitude measurement applications. Full article

Unmanned Aerial Vehicles (UAVs) are promising nodes for Integrated Sensing and Communication (ISAC), but accurate Direction-of-Arrival (DoA) estimation on a small airframe is challenged by platform loading, motion, attitude, and multipath. Traditionally, DoA algorithms have been developed and evaluated for stationary, ground-based (or otherwise mechanically stable) antenna arrays. Extending them to UAVs violates these assumptions. This work designs a six-element Uniform Circular Array (UCA) at 2.4 GHz (radius $\approx 0.5\lambda$) for a quadrotor and introduces a Pose-Aware MUSIC (MUltiple SIgnal Classification) estimator for DoA. The novelty is a MUSIC formulation that (i) applies pose correction using the drone’s instantaneous roll–pitch–yaw (pose correction) and (ii) applies a Doppler correction that accounts for platform velocity. Performance is assessed using data synthesized from embedded-element patterns obtained by electromagnetic characterization of the installed array, with additional channel/hardware effects modeled in post-processing (Rician LOS/NLOS mixing, mutual coupling, per-element gain/phase errors, and element–position jitter). Results with the six-element UCA show that pose and Doppler compensation preserve high-resolution DoA estimates and reduce bias under realistic flight and platform conditions while also revealing how coupling and jitter set practical error floors. The contribution is a practical PA-MUSIC approach for UAV ISAC, combining UCA design with motion-aware signal processing, and an evaluation that quantifies accuracy and offers clear guidance for calibration and field deployment in GNSS-denied scenarios. The results show that, across 0–25 dB SNR, the proposed hybrid DoA estimator achieves <$0.5^{\circ }$ RMSE in azimuth and elevation for ideal conditions and ≈$5^{\circ }$–$6^{\circ }$ RMSE when full platform coupling is considered, demonstrating robust performance for UAV ISAC tracking. Full article

This study presents the design and implementation of a real-time flight-path tracking control system for a quadrotor unmanned aerial vehicle (UAV) capable of accurately following a mobile ground target under dynamic and uncertain environmental conditions. The proposed framework integrates classical fixed-gain PID regulation executed on Pixhawk with its built-in adaptive mechanisms, namely autotuning, hover-throttle learning, and dynamic harmonic notch filtering, to enhance robustness under communication latency and disturbances. No machine learning PID tuning is used on Pixhawk; adaptive features are estimator based rather than ML based. The proposed system addresses critical challenges in trajectory tracking, including real-time delay compensation between the UAV and rover, external perturbations, and the requirement to maintain stable six-degree-of-freedom (DOF) control of altitude, yaw, pitch, and roll. A dynamic mathematical model, formulated using ordinary differential equations with embedded delay elements, is developed to emulate real-world flight behavior and validate control performance. Experimental evaluation demonstrates robust path-tracking accuracy, attitude stability, and responsiveness across diverse terrains and weather conditions, achieving a mean positional error below one meter and effective resilience against an 8.2 ms communication delay. Overall, this work establishes a scalable, computationally efficient, and high-precision control framework for UAV guidance and cooperative ground-target tracking, with potential applications in autonomous navigation, search-and-rescue operations, infrastructure inspection, and intelligent surveillance. The term “delay-aware” in this work refers to the explicit modeling of the measured 8.2 ms end-to-end delay and the use of Pixhawk’s estimator-based adaptive mechanisms, without any machine learning-based PID tuning. Full article