Search Results (55)

This paper presents a novel design for precise vertical landing of drones based on the detection of three phase shifts in the range of ±180°. The design has three inputs to which the signal transmitted from an oscillator located at the landing point arrives with different delays. The circuit increases the aerial tracking volume relative to that achieved by detectors with theoretical unambiguous detection ranges of ±90°. The phase shift measurement circuit uses an analog phase detector (mixer), detecting a maximum range of ±90°and a double multiplication of the input signals, in phase and phase-shifted, without the need to fulfill the quadrature condition. The calibration procedure, phase detector curve modeling, and calculation of the input signal phase shift are significantly simplified by the use of an automatic gain control on each branch, dwhich keeps input amplitudes to the analog phase detectors constant. A simple program to determine phase shifts and guidance instructions is proposed, which could be integrated into the same flight control platform, thus avoiding the need to add additional processing components. A prototype has been manufactured in C band to explain the details of the procedure design. The circuit uses commercial circuits and microstrip technology, avoiding the crossing of lines by means of switches, which allows the design topology to be extrapolated to much higher frequencies. Calibration and measurements at 5.3 GHz show a dynamic range greater than 50 dB and a non-ambiguous detection range of ±180°. These specifications would allow one to track the drone during the landing maneuver in an inverted cone formed by a surface with an 11 m radius at 10 m high and the landing point, when 4 cm between RF inputs is considered. The errors of the phase shifts used in the landing maneuver are less than ±3°, which translates into 1.7% losses over the detector theoretical range in the worst case. The circuit has a frequency bandwidth of 4.8 GHz to 5.6 GHz, considering a 3 dB variation in the input power when the AGC is limiting the output signal to 0 dBm at the circuit reference point of each branch. In addition, the evolution of phases in the landing maneuver is shown by means of a small simulation program in which the drone trajectory is inside and outside the tracking range of ±180°. Full article

Unmanned Surface Vehicle (USV) offers a cost-effective platform for high-resolution marine magnetic surveys using shipborne fluxgate magnetometers. However, platform-induced magnetic interference and electromagnetic interference (EMI) can degrade data quality, even with paramagnetic hulls. This study evaluates fluxgate magnetometer data acquired from a paramagnetic-hulled USV. Noise characterization identified EMI and maneuver-induced high-frequency noise, the latter of which was effectively reduced through low-pass filtering. We compared four different correction approaches addressing both vessel attitude and magnetization. The results demonstrate that the paramagnetic hull significantly reduces magnetic interference and shortens the duration of viscous magnetization (VM) effects caused by eddy currents in the platform, compared to conventional ferromagnetic vessels. Nonetheless, residual magnetization from onboard ferromagnetic components still requires correction. A method utilizing all nine components of the susceptibility tensor demonstrated improved accuracy and stability. Despite corrections, low-frequency VM-related noise during azimuth changes and a consistent absolute offset (~200 nT) remain when compared to towed scalar magnetometer data. These findings validate the use of paramagnetic USV for vector magnetic surveys, highlighting their benefit in VM mitigation while emphasizing the need for further development in VM correction and offset correction to achieve high-precision measurements. Full article

The paper presents the innovative approach to a high-fidelity motorcycle riding simulator based on VR (Virtual Reality)-visualization, equipped with a Gough-Stewart 6-DOF (Degrees of Freedom) motion platform. Such a solution integrates a real-time tension sensor system as a source for highly realistic motion cueing control as well as the servomotor integrated into the steering system. Tension forces are measured at four points on the mock-up chassis, allowing a comprehensive analysis of rider interaction during various maneuvers. The simulator is developed to simulate realistic riding scenarios with immersive motion and visual feedback, enhanced with the simulation of external influences—headwind. This paper presents results of a validation study—pilot experiments conducted to evaluate selected riding scenarios and validate the innovative simulator setup, focusing on force distribution and system responsiveness to support further research in motorcycle HMI (Human–Machine Interaction), rider behavior, and training. Full article

Aeromagnetic detection is a geophysical exploration technology that utilizes aircraft-mounted magnetometers to map variations in the Earth’s magnetic field. As a critical methodology for subsurface investigations, it has been extensively applied in geological mapping, mineral resource prospecting, hydrocarbon exploration, and engineering geological assessments. However, the metallic composition of aircraft platforms inherently generates magnetic interference, which significantly distorts the measurements acquired by onboard magnetometers. Aeromagnetic compensation aims to mitigate these platform-induced magnetic disturbances, thereby enhancing the accuracy of magnetic anomaly detection. Building upon the conventional Tolles-Lawson (T-L) model, this study introduces an enhanced compensation framework that addresses two key limitations: (1) minor deformations that occur due to the non-rigidity of the aircraft fuselage, resulting in additional interfering magnetic fields, and (2) coupled interference between geomagnetic field variations and aircraft maneuvers. The proposed model expands the original 18 compensation coefficients to 57 through dynamic parameterization, achieving a 22.41% improvement in compensation efficacy compared with the traditional T-L model. Furthermore, recognizing the operational challenges of large unmanned aerial vehicles (UAVs) in conventional calibration flights, this work redesigns the flight protocol by eliminating high-risk yaw maneuvers and optimizing the flight path geometry. Experimental validations conducted in the South China Sea demonstrate exceptional performance, with the interference magnetic field reduced to 0.0385 nT (standard deviation) during level flight, achieving an improvement ratio (IR) of 4.1688. The refined methodology not only enhances compensation precision but also substantially improves operational safety for large UAVs, offering a robust solution for modern aeromagnetic surveys. Full article

Autonomous vehicles are increasingly becoming a part of our daily lives, with active chassis control systems playing a pivotal role and drawing significant attention from both academia and industry. Current research on vehicle-to-vehicle overtaking behavior predominantly focuses on low-to-moderate speeds, with insufficient studies addressing high-speed lane-changing maneuvers. Under high-speed conditions, the variability and complexity of road environments significantly increase tracking errors, posing challenges for control algorithms that perform well at lower speeds but may suffer from reduced accuracy or instability at higher speeds. A hybrid control strategy based on vehicle dynamics for high-speed overtaking path tracking is developed to ensure vehicle stability and maneuverability. By integrating Model Predictive Control (MPC) with Four-Wheel Steering (4WS) controllers and employing a two-degree-of-freedom ideal model as the path-tracking response model, we have achieved effective control and path tracking for autonomous vehicles equipped with four-wheel steering. The effectiveness of the proposed control strategy was validated on the Carsim–Simulink integrated simulation platform. Experimental results demonstrate that this strategy offers higher path-tracking accuracy than single-controller approaches under high-speed conditions while also meeting vehicle stability requirements. The model provides robust support for enhancing the path-tracking performance of autonomous four-wheel steering vehicles at medium-to-high speeds, thereby advancing the reliability and safety of autonomous driving technology in practical applications. Full article

Coronary heart disease, a leading global cause of mortality, has witnessed significant advancement through robotic coronary artery bypass grafting (CABG), with the internal mammary artery (IMA) emerging as the preferred “golden conduit” for its exceptional long-term patency. Despite these advances, robotic-assisted IMA harvesting remains challenging due to the absence of force feedback, complex surgical maneuvers, and proximity to the beating heart. This study introduces a novel virtual simulation platform for robotic IMA harvesting that integrates dynamic anatomical modeling and real-time haptic feedback. By incorporating a dynamic cardiac model into the surgical scene, our system precisely simulates the impact of cardiac pulsation on thoracic cavity operations. The platform features high-fidelity representations of thoracic anatomy and soft tissue deformation, underpinned by a comprehensive biomechanical framework encompassing fascia, adipose tissue, and vascular structures. Our key innovations include a topology-preserving cutting algorithm, a bidirectional tissue coupling mechanism, and dual-channel haptic feedback for electrocautery simulation. Quantitative assessment using our newly proposed Spatial Asymmetry Index (SAI) demonstrated significant behavioral adaptations to cardiac motion, with dynamic scenarios yielding superior SAI values compared to static conditions. These results validate the platform’s potential as an anatomically accurate, interactive, and computationally efficient solution for enhancing surgical skill acquisition in complex cardiac procedures. Full article

As autonomous vehicles increasingly populate roads, robust testing is essential to ensure their safety and reliability. Due to the limitation that traditional testing methodologies (real-world and simulation testing) are difficult to cover a wide range of scenarios and ensure repeatability, this study proposes a novel virtual-real fusion testing approach that integrates Graph Theory and Artificial Potential Fields (APF) in virtual-real fusion autonomous vehicle testing. Conducted using SUMO software, our strategic lane change and speed adjustment simulation experiments demonstrate that our approach can efficiently handle vehicle dynamics and environmental interactions compared to traditional Rapidly-exploring Random Tree (RRT) methods. The proposed method shows a significant reduction in maneuver completion times—up to 41% faster in simulations and 55% faster in real-world tests. Field experiments at the Vehicle-Road-Cloud Integrated Platform in Suzhou High-Speed Railway New Town confirmed the method’s practical viability and robustness under real traffic conditions. The results indicate that our integrated approach enhances the authenticity and efficiency of testing, thereby advancing the development of dependable, autonomous driving systems. This research not only contributes to the theoretical framework but also has practical implications for improving autonomous vehicle testing processes. Full article

Trajectory prediction is critical for ensuring the safety, reliability, and scalability of Unmanned Aerial Vehicle (UAV) in urban environments. Despite advances in deep learning, existing methods often struggle with dynamic UAV conditions, such as rapid directional changes and limited forecasting horizons, while lacking comprehensive real-time validation and generalization capabilities. This study addresses these challenges by proposing a gated recurrent unit (GRU)-based deep learning framework optimized through Look_Back and Forward_Length labeling to capture complex temporal patterns. The model demonstrated state-of-the-art performance, surpassing existing unmanned aerial vehicles (UAV) and aircraft trajectory prediction approaches, including FlightBERT++, in terms of both accuracy and robustness. It achieved reliable long-range predictions up to 4 s, and its real-time feasibility was validated due to its efficient resource utilization. The model’s generalization capability was confirmed through evaluations on two independent UAV datasets, where it consistently predicted unseen trajectories with high accuracy. These findings highlight the model’s ability to handle rapid maneuvers, extend prediction horizons, and generalize across platforms. This work establishes a robust trajectory prediction framework with practical applications in collision avoidance, mission planning, and anti-drone systems, paving the way for safer and more scalable UAV operations. Full article

Traditional fracture reduction relies heavily on the surgeon’s experience, which hinders the transmission of skills. This specialization bottleneck, coupled with the high demands on physical strength, significantly limits the efficiency of daily treatments in trauma orthopedics. Currently, most fracture surgery robots focus on open or minimally invasive reduction techniques, which inherently carry the risk of iatrogenic damage due to surgical incisions or bone pin insertions. However, research in closed reduction-oriented robotic systems is remarkably limited. Addressing this gap, our study introduces a novel bone-setting robot for the closed reduction of ankle fractures designed with a redundant parallel platform. The parallel robot’s design incorporates three sliding redundancy actuators that enhance its tilt flexibility while maintaining load performance. Moreover, a singularity-free redundant kinematic solver has been developed, optimizing the robot’s operational efficacy. Building upon the demonstrations from professional closed reduction techniques, we propose the use of a multivariate Student-t process as a multi-output regression model within dynamic movement primitive for accurately learning stable reduction maneuvers. Additionally, we develop an anthropomorphic variable impedance controller based on inverse dynamics. The simulation results demonstrate convincingly that the developed ankle bone-setting robot is proficient in effectively replicating and learning the nuanced closed reduction techniques. Full article

A new method to discover open-loop, unstable, longitudinal aerodynamic parameters, using a ‘two-stage optimization approach’ for designing optimal inputs, and with an application on the fighter aircraft platform, has been presented. System identification of supersonic aircraft requires formulating optimal inputs due to the extremely limited maneuver time, high angles of attack, restricted flight conditions, and the demand for an enhanced computational effect. A pre-requisite of the parametric model identification is to have a priori aerodynamic parameter estimates, which were acquired using linear regression and Least Squares (LS) estimation, based upon simulated time histories of outputs from heuristic inputs, using an F-16 Flight Dynamic Model (FDM). In the ‘first stage’, discrete-time pseudo-random binary signal (PRBS) inputs were optimized using a minimization algorithm, in accordance with aircraft spectral features and aerodynamic constraints. In the ‘second stage’, an innovative concept of integrating the Fisher Informative Matrix with cost function based upon D-optimality criteria and Crest Factor has been utilized to further optimize the PRBS parameters, such as its frequency, amplitude, order, and periodicity. This unique optimum design also solves the problem of non-convexity, model over-parameterization, and misspecification; these are usually caused by the use of traditional heuristic (doublets and multistep) optimal inputs. After completing the optimal input framework, parameter estimation was performed using Maximum Likelihood Estimation. A performance comparison of four different PRBS inputs was made as part of our investigations. The model performance was validated by using statistical metrics, namely the following: residual analysis, standard errors, t statistics, fit error, and coefficient of determination $({\mathrm{R}}^2)$. Results have shown promising model predictions, with an accuracy of more than 95%, by using a Single Sequence Band-limited PRBS optimum input. This research concludes that, for the identification of the decoupled longitudinal Linear Time Invariant (LTI) aerodynamic model of supersonic aircraft, optimum PRBS shows better results than the traditional frequency sweeps, such as multi-sine, doublets, square waves, and impulse inputs. This work also provides the ability to corroborate control and stability derivatives obtained from Computational Fluid Dynamics (CFD) and wind tunnel testing. This further refines control law design, dynamic analysis, flying qualities assessments, accident investigations, and the subsequent design of an effective ground-based training simulator. Full article

This paper focuses on the robust adaptive control of a coaxial-ducted-fan aircraft under the context of significant system uncertainty. To ensure accurate tracking even in the presence of model uncertainties and disturbances for a ducted-fan flight platform, a model reference adaptive control architecture is proposed in this paper on the basis of control augmentation. With the nominal closed-loop system as the reference system, this architecture overlays adaptive control inputs on top of the linear time-invariant control inputs to compensate for uncertainties in real time. This allows the reference system to be tracked rapidly and accurately by the output of the uncertain system, which ensures that the performance of the closed-loop system meets design requirements and that the flight envelope of the prototype is further expanded. The superiority of the proposed method is demonstrated by numerical simulation of the ducted-fan aircraft. The simulation results show that the proposed robust adaptive control algorithm is advantageous over the baseline control in rapidly compensating for the closed-loop performance of the system, even in the presence of a more severe disturbance. At the same time, the performance of control algorithms are verified by conducting flight experiments. The results show that when the prototype undergoes low-speed slow-change maneuvering, there is little difference in the tracking performance of the four channels of the two control strategies. However, when the prototype undergoes high-speed fast-change maneuvering, the root mean square (RMS) values of the tracking errors of the robust adaptive control and the baseline control are 0.749 m/s and 1.039 m/s, respectively, and the robust adaptive algorithm reduces the tracking error by about 38% on the basis of the baseline control. Full article

Fan power generation, a form of new energy, has gained significant attention. However, as fan capacities grow, the challenges in the development process also increase. A wind maneuver model experiment system must be constructed to simulate the actual fan operation dynamics. The wind turbine simulator is essential for conducting experiments on wind turbines and advancing the research of wind power production technology. However, due to the insufficient moment of inertia in wind turbine simulators under laboratory conditions, physical compensation methods are challenging to implement. Therefore, most scholars rely on software compensation algorithms to realize the stability simulation of wind turbine simulators with small moments of inertia and actual wind turbines with large moments of inertia. Under this research background, this paper presents the existing moment of inertia compensation strategies based on current research hotspots. The theoretical foundation covers the mechanical dynamic models of both actual wind turbines and simulators along with an analysis of inertia compensation strategies, including high-order filters and feedforward bias suppression. Finally, the Simulink simulation platform is used to compare and validate the effectiveness of these strategies. Full article

Micro-nozzles are essential for enabling precise satellite attitude control and orbital maneuvers. Accurate prediction of performance parameters, including thrust and specific impulse, is critical, necessitating careful design of these nozzles. Given the high Knudsen numbers associated with micro-nozzle flows, rarefied gas dynamics often dominate, and conventional computational fluid dynamics (CFD) methods fail to capture accurate flow expansion behavior. The Direct Simulation Monte Carlo (DSMC) method, developed by Bird, is widely used for modeling rarefied flows; however, it has been primarily implemented on platforms like OpenFOAM and FORTRAN, with limited exploration in MATLAB. This study presents the development of a viscosity-based DSMC (μDSMC) simulation framework in MATLAB for analyzing rarefied gas expansion through micro-nozzles. Key boundary conditions, including upstream and downstream pressure conditions and thermal wall treatments with diffuse reflection, are incorporated into the code. The μDSMC results are validated against traditional DSMC outcomes, showing strong agreement. Grid convergence studies indicate that the radial grid size must be less than one-third of the mean free path, with a more relaxed requirement on axial grid size. Flow characteristics within micro-nozzles are evaluated across varying ambient pressures and gas species in terms of the back pressure ratio, effective exit flow ratio, and exit flow velocity. Studies indicated that a minimum back pressure ratio is required, beyond which the effective nozzle flow expansion is achieved. Parametric analysis further suggests that gases with lower molecular weights are preferable for achieving optimal expansion in micro-nozzles under low ambient pressures. Full article

A bistatic synthetic aperture radar (BiSAR) system with a Medium-Earth-Orbit (MEO) SAR transmitter and high-maneuvering receiver (MEO/HM-BiSAR) can achieve a wide swath and high resolution. However, due to the complex orbit characteristics and the nonlinear trajectory of the receiver, MEO/HM-BiSAR high-resolution imaging faces two major challenges. First, the complex geometric configuration of the BiSAR platforms is difficult to model accurately, and the ‘non-stop-go’ effects should also be considered. Second, non-negligible wavefront curvature caused by the nonlinear trajectories introduces residual phase errors. The existing spaceborne BiSAR imaging algorithms often suffer from image defocusing if applied to MEO/HM-BiSAR. To address these problems, a novel high-resolution imaging algorithm named MSSWCC (Modified Second-Order Space-Variant Wavefront Curvature Correction) is proposed. First, a high-precision range model is established based on an analysis of MEO SAR’s orbital characteristics and the receiver’s curved trajectory. Based on the echo model, the wavefront curvature error is then addressed by two-dimensional Taylor expansion to obtain the analytical expressions for the high-order phase errors. By analyzing the phase errors in the wavenumber domain, the compensation functions can be designed. The MSSWCC algorithm not only corrects the geometric distortion through reverse projection, but it also compensates for the second-order residual spatial-variant phase errors by the analytical expressions for the two-dimensional phase errors. It can achieve high-resolution imaging ability in large imaging scenes with low computational load. Simulations and real experiments validate the high-resolution imaging capabilities of the proposed MSSWCC algorithm in MEO/HM-BiSAR. Full article

Commercial vehicles frequently experience lateral interferences, such as crosswinds or side slopes, during extreme maneuvers like emergency steering and high-speed driving due to their high centroid. These interferences reduce vehicle stability and increase the risk of rollover. Therefore, this study takes a bus as the carrier and designs an anti-rollover control strategy based on mixed-sensitivity and robust $H_{\infty }$ controller. Specifically, a 7-DOF vehicle dynamics model is introduced, and the factors influencing vehicle rollover are analyzed. Based on this, to minimize excessive intervention in the vehicle’s dynamic characteristics, the lateral velocity, roll angle, and roll rate are recorded at the vehicle’s rollover threshold as desired values. The lateral load transfer rate (LTR) is chosen as the evaluation index, and the required additional yaw moment is determined and distributed to the wheels for anti-rollover control. Furthermore, to verify the effectiveness of the proposed anti-rollover control strategy, a co-simulation platform based on MATLAB/Simulink and TruckSim is developed. Various dynamic lateral interferences (side winds with different changing trends and wind speeds) are introduced, and the fishhook and J-turn maneuvers are selected to analyze and compare the proposed control strategy with a fuzzy logic algorithm. The results indicate that the maximum LTR of the vehicle is reduced by 0.11. Additionally, the lateral acceleration and yaw rate in the steady state are reduced by more than 1.8 m/s² and 15°, respectively, enhancing the vehicle’s lateral stability. Full article