Yaw Angle Research Articles

Model predictive-based DNN control model for automated steering deployed on FPGA using an automatic IP generator tool

With the increase in the non-linearity and complexity of the driving system’s environment, developing and optimizing related applications is becoming more crucial and remains an open challenge for researchers and automotive companies alike. Model predictive control (MPC) is a well-known classic control strategy used to solve online optimization problems. MPC is computationally expensive and resource-consuming. Recently, machine learning has become an effective alternative to classical control systems. This paper provides a developed deep neural network (DNN)-based control strategy for automated steering deployed on FPGA. The DNN model was designed and trained based on the behavior of the traditional MPC controller. The performance of the DNN model is evaluated compared to the performance of the designed MPC which already proved its merit in automated driving task. A new automatic intellectual property generator based on the Xilinx system generator (XSG) has been developed, not only to perform the deployment but also to optimize it. The performance was evaluated based on the ability of the controllers to drive the lateral deviation and yaw angle of the vehicle to be as close as possible to zero. The DNN model was implemented on FPGA using two different data types, fixed-point and floating-point, in order to evaluate the efficiency in the terms of performance and resource consumption. The obtained results show that the suggested DNN model provided a satisfactory performance and successfully imitated the behavior of the traditional MPC with a very small root mean square error (RMSE = 0.011228 rad). Additionally, the results show that the deployments using fixed-point data greatly reduced resource consumption compared to the floating-point data type while maintaining satisfactory performance and meeting the safety conditions

Assessing Closed-Loop Data-Driven Wind Farm Control Strategies within a Wind Tunnel

This paper introduces a framework to assess a hybrid data-driven wake steering strategy combining the advantages of an analytical model for the initial data gathering together with a data-driven model for modelling error correction. The main building blocks of the control strategy (required input domain, data gathering and model generation) are evaluated within the wind tunnel, utilising a newly developed setup that can simulate dynamic wind direction variations and which was specifically designed to examine wake steering control strategies. The control strategy employs a machine-learning algorithm to generate a turbine and wake model based on the measurement data, allowing this approach to be extrapolated for an arbitrarily large wind farm. The machine-learning model is used to estimate the optimal yaw angle in a closed-loop format. To implement the developed control strategy, steady measurements are analysed first to determine the required input variables. This is followed by an investigation of the potential power gain and the impact of utilising a grid-search procedure. The procedure minimises adverse conditions as it collects the required data points, still achieving a power gain.

Responsiveness and Precision of Digital IMUs under Linear and Curvilinear Motion Conditions for Local Navigation and Positioning in Advanced Smart Mobility.

Sensors based on MEMS technology, in particular Inertial Measurement Units (IMUs), when installed on vehicles, provide a real-time full estimation of vehicles' state vector (e.g., position, velocity, yaw angle, angular rate, acceleration), which is required for the planning and control of cars' trajectories, as well as managing the in-car local navigation and positioning tasks. Moreover, data provided by the IMUs, integrated with the data of multiple inputs from other sensing systems (such as Lidar, cameras, and GPS) within the vehicle, and with the surrounding information exchanged in real time (vehicle to vehicle, vehicle to infrastructure, or vehicle to other entities), can be exploited to actualize the full implementation of "smart mobility" on a large scale. On the other hand, "smart mobility" (which is expected to improve road safety, reduce traffic congestion and environmental burden, and enhance the sustainability of mobility as a whole), to be safe and functional on a large scale, should be supported by highly accurate and trustworthy technologies based on precise and reliable sensors and systems. It is known that the accuracy and precision of data supplied by appropriately in-lab-calibrated IMUs (with respect to the primary or secondary standard in order to provide traceability to the International System of Units) allow guaranteeing high quality, reliable information managed by processing systems, since they are reproducible, repeatable, and traceable. In this work, the effective responsiveness and the related precision of digital IMUs, under sinusoidal linear and curvilinear motion conditions at 5 Hz, 10 Hz, and 20 Hz, are investigated on the basis of metrological approaches in laboratory standard conditions only. As a first step, in-lab calibrations allow one to reduce the variables of uncontrolled boundary conditions (e.g., occurring in vehicles in on-site tests) in order to identify the IMUs' sensitivity in a stable and reproducible environment. For this purpose, a new calibration system, based on an oscillating rotating table was developed to reproduce the dynamic conditions of use in the field, and the results are compared with calibration data obtained on linear calibration benches.

Integral Sliding Mode Output Feedback Control for Unmanned Marine Vehicles Using T–S Fuzzy Model with Unknown Premise Variables and Actuator Faults

This paper addresses integral sliding mode output feedback fault-tolerant control (FTC) of unmanned marine vessels (UMVs) with unknown premise variables and actuator faults. Due to the complexity of the marine environment, the presence of uncertainties in the yaw angle renders the premise variables in the Takagi–Sugeno (T–S) fuzzy model of UMVs unknown. Consequently, traditional integral sliding mode techniques become infeasible. To address this issue, a control strategy combining integral sliding mode based on output feedback with a compensator utilizing switching mechanisms is proposed. First, a radial basis function neural network is used to approximate the nonlinear terms in the UMV T–S fuzzy model. In addition, an integral sliding mode surface is constructed based on fault estimation information and membership function estimation. On this basis, an FTC scheme based on integral sliding mode output feedback is developed to ensure that the UMV system is asymptotically stable and satisfies the prescribed H∞ performance index. Finally, simulation results are provided to demonstrate the effectiveness of the presented control strategy.

Path Tracking Control Based on T-S Fuzzy Model for Autonomous Vehicles with Yaw Angle and Heading Angle

Existing vehicle-road models used for road tracking do not take into account the side slip angle, which leads to a reduction in road tracking accuracy in scenarios where the vehicle is at a large side slip angle, such as an emergency lane change. Consequently, this study presents a path-tracking control technique based on the T-S fuzzy model of heading angle vehicle autonomy. In this paper, based on the yaw angle-based vehicle tracking model, a heading angle-based tracking model considering the side slip angle is constructed. Second, since the vehicle speed varies with time, this paper selects the membership function of the vehicle speed to establish the T-S fuzzy model of autonomous vehicle based on the yaw angle and heading angle, respectively, and ensures the robustness and stability over the whole parameter space by the linear parameter variation robust H∞ controller. Then, cost functions based on the yaw angle and heading angle augmented error systems are created separately to optimize the system’s overall performance. Ultimately, simulation and experimentation confirm that the algorithm for control, which is based on the fuzzy model of the heading angle vehicle, has superior autonomous trajectory performance.

RANSE simulations for unmanned sailboat performance prediction: Coupling aerodynamics and hydrodynamics in self-sailing

During the design phase of sailboats, naval architects typically employ hydrodynamic and aerodynamic models of the sailboat, utilizing a Velocity Prediction Program (VPP) to solve the equations of motion, thus determining the sailboat's speed and performance. Traditional methods for solving sailboat motion equations often employ simplified empirical formulas or costly experimental procedures, which yield less accurate results. This study presents the RANSE-based CFD software STAR-CCM + to comprehensively model the sailboat's hull-sail-rudder assembly. By implementing overset grid technology and the Volume of Fluid (VOF) model, the paper aims to couple the solution of aerodynamic and hydrodynamic forces under wind influence and introduces PID rudder control technology to maintain a balance of forces and convergence of motion state. This approach enables the prediction of the sailboat's speed and performance, such as yaw angle, heel, and rudder angle. Results demonstrate minimal variations in sailboat speed (<0.05 m/s) and heading (<0.1°) in upwind, downwind and reaching sailing conditions, underscoring the method's feasibility and advocating for further research into self-sailing performance.

The influence of crosswinds and leg positions on cycling aerodynamics

This work investigated the influence of crosswinds and leg positions on the aerodynamics of an articulated cycling mannequin on a track bicycle. Force, wake total pressure and wake velocity measurements were made in a low-speed sports wind tunnel of closed-circuit type. The freestream velocity and wheel speeds were kept at 15m/s\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$15\\, \\hbox {m/s}$$\\end{document}. The crank angle of the mannequin was varied across a pedal cycle. Yaw angles from 0∘\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$0^{\\circ }$$\\end{document} to 20∘\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$20^{\\circ }$$\\end{document} were examined. The experimental results reveal that the leg position significantly affects the aerodynamic performance of a cyclist. At high yaw angles, the aerodynamic drag on the cyclist showed noticeable deviations between most leg positions and their 180∘\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$180^{\\circ }$$\\end{document}-apart pairs. A wake analysis technique effectively captured the influence of leg positions on drag. The total pressure deficit contributes dominantly to the overall drag. The wake pressure contours demonstrate how leg-wheel interaction affects the total pressure distribution and drag under crosswinds. This study offers valuable insights into the flow behavior and drag generation around a cyclist with varying leg positions under crosswinds.

Head-locked, world-locked, or conformal diminished-reality? An examination of different AR solutions for pedestrian safety in occluded scenarios

Many collisions between pedestrians and cars are caused by poor visibility, such as occlusion by a parked vehicle. Augmented reality (AR) could help to prevent this problem, but it is unknown to what extent the augmented information needs to be embedded into the world. In this virtual reality experiment with a head-mounted display (HMD), 28 participants were exposed to AR designs, in a scenario where a vehicle approached from behind a parked vehicle. The experimental conditions included a head-locked live video feed of the occluded region, meaning it was fixed in a specific location within the view of the HMD (VideoHead), a world-locked video feed displayed across the street (VideoStreet), and two conformal diminished reality designs: a see-through display on the occluding vehicle (VideoSeeThrough) and a solution where the occluding vehicle has been made semi-transparent (TransparentVehicle). A Baseline condition without augmented information served as a reference. Additionally, the VideoHead and VideoStreet conditions were each tested with and without the addition of a guiding arrow indicating the location of the approaching vehicle. Participants performed 42 trials, 6 per condition, during which they had to hold a key when they felt safe to cross. The keypress percentages and responses from additional questionnaires showed that the diminished-reality TransparentVehicle and VideoSeeThrough designs came out most favourably, while the VideoHead solution caused some discomfort and dissatisfaction. An analysis of head yaw angle showed that VideoHead and VideoStreet caused divided attention between the screen and the approaching vehicle. The use of guiding arrows did not contribute demonstrable added value. AR designs with a high level of local embeddedness are beneficial for addressing occlusion problems when crossing. However, the head-locked solutions should not be immediately dismissed because, according to the literature, such solutions can serve tasks where a salient warning or instruction is beneficial.

Influence of the contraction section configuration and deflector layout density on the wind-snow flow characteristics inside the ice snow wind tunnel for railway vehicles

The significant accumulation of snow and ice in the bogie region severely compromises operational quality and poses a safety hazard to high-speed trains. To enhance testing capabilities for assessing ice and snow accumulation within bogie regions, this study conducts numerical investigations into the influence of contraction section configuration and deflector layout density on wind-snow flow quality within an ice and snow wind tunnel. The predictive accuracy of the numerical method is comprehensively validated against wind-snow wind tunnel tests, involving a comparison of airflow velocity in the test section and snow accumulation thickness on the channel's floor. The results demonstrate that the coupled numerical method of Improved Delayed Detached Eddy Simulation (IDDES) and Discrete Phase Model (DPM) is a reliable tool for analyzing wind-snow flow characteristics within an ice and snow wind tunnel. Furthermore, compared to the vickers curve and bicubic curve, quintic curves applied to the contraction section consistently compress and accelerate airflow. This contributes to a more uniform flow characterized by lower stream-wise fluctuation levels, pitch angles, and yaw angles in the test section, thereby significantly enhancing the overall flow quality of wind and snow. Thus, the quintic curves or smoother curves are recommended for the design of the contraction section in ice and snow wind tunnels. Moreover, increasing the deflector number leads to a noticeable improvement in flow quality inside the test section, while resulting in increased snow accretion on the windward surfaces of deflectors. Hence, a medium-level deflector density is suggested for designing ice and snow wind tunnels to achieve a better balance between flow quality and the additional snow accretion inside the channel.

Research on Stability Control of Distributed Drive Vehicle with Four-Wheel Steering

The four-wheel steering distributed drive vehicle is a novel type of vehicle with independent control over the four-wheel angle and wheel torque. A method for jointly controlling the distribution of the wheel angle and torque is proposed based on this characteristic. Firstly, the two-degrees-of-freedom model and ideal reference model of four-wheel steering vehicle are established; then, the four-wheel steering controller and torque distribution controller are designed. The rear wheel angle is controlled by the feedforward controller and the feedback controller. The feedforward controller takes the side slip angle of the center of mass as the control target, and the feedback controller takes the yaw angle as the control target. Torque is controlled by two control layers, the additional yaw moment of the upper layer is calculated by the vehicle motion state and fuzzy control theory, and the lower layer distributes wheel torque through the road adhesion coefficient and wheel load. Finally, a simulation platform is established to verify the effectiveness of the proposed control algorithm.

Data-driven constrained reinforcement learning algorithm for path tracking control of hovercraft

This paper presents a novel Constrained Backstepping Reinforcement Learning (CBRL) approach designed for path tracking of hovercraft. The approach addresses the challenges posed by complex modeling, multiple constraints, and large sideslip angles during high-speed maneuvers of underactuated hovercraft. Initially, a unique state constraint function is formulated, incorporating constraint boundaries related to navigation speed and path curvature. Additionally, transformations are applied to the yaw angular velocity and virtual yaw control law, ensuring that the yaw angular velocity remains within safety limits. Subsequently, a data-driven backstepping reinforcement learning optimal approach, coupled with a line-of-sight guidance law, is employed to guide the hovercraft along the intended path by regulating its yaw angle. Simultaneously, the backstepping reinforcement learning optimal control scheme is used to regulate the surge speed of the hovercraft above the resistance peak speed, thereby preventing excessive sideslip angles during path tracking. Neural network observers are integrated into the design process of both the yaw and surge controllers to monitor system dynamics in the presence of interference. Through simulation, the effectiveness and feasibility of the proposed CBRL algorithm are demonstrated.

Three-dimensional path planning with enhanced gravitational search algorithm for unmanned aerial vehicle

Abstract Path planning for the unmanned aerial vehicle (UAV) is to assist in finding the proper path, serving as a critical role in the intelligence of a UAV. In this paper, a path planning for UAV in three-dimensional environment (3D) based on enhanced gravitational search algorithm (EGSA) is put forward, taking the path length, yaw angle, pitch angle, and flight altitude as considerations of the path. Considering EGSA is easy to fall into local optimum and convergence insufficiency, two factors that are the memory of current optimal and random disturbance with chaotic levy flight are adopted during the update of particle velocity, improving the balance between exploration and exploitation for EGSA through different time-varying characteristics. With the identical cost function, EGSA is compared with seven peer algorithms, such as moth flame optimization algorithm, gravitational search algorithm, and five variants of gravitational search algorithm. The experimental results demonstrate that EGSA is superior to the seven comparison algorithms on CEC 2020 benchmark functions and the path planning method based on EGSA is more valuable than the other seven methods in diverse environments.

Mechanism and performance of a passive auxiliary steering device for low-floor light rail vehicle

For low-floor light rail vehicles, a passive auxiliary steering device (ASD) is developed aiming at relieving the independently rotating wheelset (IRW) excessive wear and improving IRW bogie system running safety on curve line. The ASD can be briefly seen as some longitudinal springs arranged between a carbody with IRW and a carbody ahead or behind it to increase the vehicle horizontal flexural stiffness. Consequently, the vehicle will possess the characteristics of both single-type vehicle and traditional vehicle. When passing curved track, the yaw angle between the adjacent carbodies deforms the springs to generate moment which will reduce the unbalanced centrifugal force of the carbody with IRW. As a result, the centering ability of the IRW bogie is improved. The vehicle-track coupled dynamics model for a 6-module single-type low-floor light rail vehicle is developed and the simulations of vehicle passing through tangent and curve lines with and without ASD are carried out. The results indicate that the ASD can improve the IRW restoring capability on tangent track and the IRW negotiation performance on medium and large curved tracks. Besides, larger spring preload, larger steering stiffness and smaller steering clearance are preferred to guarantee the higher performance of this ASD.

Buffeting characteristics of cable-stayed box beam bridge based on pressure measurement test of full bridge aeroelastic model

The aim of this study is to explore the buffeting characteristics of cable-stayed box beam bridge based on pressure measurement test of full bridge aeroelastic model. The study includes the influence of turbulence integral length scale and wind yaw angle on the magnitude of buffeting force, spatial distribution of buffeting force, and buffeting displacement response. Under larger turbulence integral length scale, the buffeting force of the box beam and it’s spanwise correlation are larger, which verifies the three-dimensional effect of the buffeting force. It is worth mentioning that, unlike traditional section models, there are differences in the pressure values of the pressure strips in the aeroelastic model at the flow separation point, which may be influenced by the cables. The experimental results of different wind yaw angles show that there is little difference in the buffeting force of the box beam section under the condition of 0°∼15° wind yaw angle, indicating that the maximum buffeting response will occur in the range of 0°∼15° wind yaw angle, which is consistent with the displacement results measured by the aeroelastic model. In addition, by fixing the model with a steel wire, the static and vibration states of the model were achieved. However, due to the high stiffness of the model, it can only generate small buffeting displacement, and the self-excited force component can be almost ignored. There is no significant difference in the results between the static and vibration states.

Boundary-layer instability on a highly swept fin on a cone at Mach 6

The growth and characteristics of linear, oblique instabilities on a highly swept fin on a straight cone in Mach 6 flow are examined. Large streamwise pressure gradients cause doubly inflected cross-flow profiles and reversed flow near the wall, which necessitates using the harmonic linearized Navier–Stokes equations. The cross-flow instability is responsible for the most-amplified disturbances, however, not all disturbances show typical cross-flow characteristics. Distinct differences in perturbation structure are shown between small ( $\sim$ 3–5 mm) and large ( $\sim$ 10 mm) wavelength disturbances at the unit Reynolds number $Re' = 11 \times 10^6$ m $^{-1}$ . As a result, amplification measurements based solely on wall quantities bias a most-amplified disturbance assessment towards larger wavelengths and lower frequencies than would otherwise be determined by an off-wall total-energy approach. A spatial-amplification energy-budget analysis demonstrates (i) that wall-normal Reynolds-flux terms dictate the local growth rate, despite other terms having a locally larger magnitude and (ii) that the Reynolds-stress terms are responsible for large-wavelength disturbances propagating closer to the wall compared with small-wavelength disturbances. Additionally, the effect of free-stream unit Reynolds number and small yaw angles on the perturbation amplification and energy budget is considered. At a higher Reynolds number ( $Re' = 22 \times 10^6$ m $^{-1}$ ), the most-amplified wavelength shrinks. Perturbations do not behave self-similarly in the thinner boundary layer, and the shift in most-amplified wavelength is due to decreased dissipation relative to the lower-Reynolds-number case. Small yaw angles produce a streamwise shift in the boundary layer and disturbance amplification. The yaw results quantify a potential uncertainty source in experiments and flight.

Two-phase flow-induced vibration of tilted curved bi-directional functionally graded nanopipe in supersonic airflow

Flow-induced vibration is known as a prevalent phenomenon in various industries. As a result of the flow, dynamic fluid forces are generated, which lead to the excitation of the system. Multiphase flow, found commonly in both natural and industrial settings, including industrial plants, is a significant source of noise and vibration. In the two-phase flow, as a particular case of multiphase flow, flow configuration plays a major role in the system’s traits. Despite recent advancements in flow-induced vibration, it is still considered an unresolved topic. This research develops a new multi-physics simulation for the stability study of a two-directional functionally graded tilted curved cylindrical nanopipe conveying a liquid-gas two-phase flow. Nonlocal stress–strain gradient theory (NSGT) with two size-dependent parameters (nonlocal and length scale) is presented to simulate the current nanostructure. Newton’s second law has been employed to govern the resultant forces that act on a control volume at a differential fluid element of fluid. Hiring the finite element (FE) approach alongside the generalized differential quadrature role (GDQR), both the eigenvector and eigenvalue problems are presented for analyzing the characteristics of the nanosystem under various situations. An exhaustive parametric analysis is also provided to make clear the role of varied parameters such as velocity of fluid flow, aerodynamic pressure, air yaw angle, and gas volume fraction coefficient on the system’s fundamental frequency.

Design and Development of an Air–Land Amphibious Inspection Drone for Fusion Reactor

This paper proposes a design method for a miniature air–land amphibious inspection drone (AAID) to be used in the latest compact fusion reactor discharge gap observation mission. Utilizing the amphibious function, the AAID realizes the function of crawling transportation in the narrow maintenance channel and flying observation inside the fusion reactor. To realize miniaturization, the mobile platform adopts the bionic cockroach wheel-legged system to improve the obstacle-crossing ability. The flight platform adopts an integrated rotor structure with frame and control to reduce the overall weight of the AAID. Based on the AAID dynamic model and the optimal control method, the control strategies under flight mode, hover mode and fly–crawl transition are designed, respectively. Finally, the prototype of the AAID is established, and the crawling, hovering, and fly–crawling transition control experiments are carried out, respectively. The test results show that the maximum crawling inclination of the AAID is more than 20°. The roll angle, pitch angle, and yaw angle deviation of the AAID during hovering are all less than 2°. The landing success rate of the AAID during the fly–crawl transition phase also exceeded 77%, proving the effectiveness of the structural design and dynamic control strategy.

A coordinated trajectory tracking method with active utilization of drag for underwater vehicle manipulator systems

UVMSs suffer severe drag owing to the manipulator while tracking a trajectory at a considerable speed. Drag, considered a negative force by other researchers, can be utilized for UVMS trajectory tracking. To this end, a novel coordinated trajectory tracking method for UVMS is proposed to actively utilize drag, which consists of three parts: a trajectory planner is developed to plan the desired velocity of the vehicle for trajectory tracking; also, a motion planner is designed to schedule the movement of the manipulator to actively utilize drag and the generating turning torque on the vehicle, making the vehicle approach the desired angular velocity; additionally, a computed torque controller (CTC) is developed to control the linear velocity and joint positions and its stability is demonstrated. Different simulations are performed, the results show that drag can be actively utilized for trajectory tracking, and the combination of the proposed method with other actuators can improve the maneuverability and tracking performance of the system. Pool experiments are conducted to verify the effectiveness of the proposed method in tracking yaw angles.

Analysis of Diffusion Characteristics and Influencing Factors of Particulate Matter in Ship Exhaust Plume in Arctic Environment Based on CFD

The gradual opening of the Arctic shipping route has made navigation possible. However, the harm caused by ship exhaust emissions is increasingly severe. Therefore, it is necessary to study the diffusion characteristics of ship exhaust plumes during Arctic navigation. The study focuses on a merchant vessel as the subject of investigation, employing computational fluid dynamics (CFD) simulation techniques to analyze the diffusion characteristics of particulate matter (PM) within ship exhaust plumes under Arctic environmental conditions. The diffusion law of ship exhaust plume PM is clarified, and the influence of three factors, synthetic wind speed, yaw angle and chimney angle, on the PM diffusion is analyzed. It was found that after the PM was discharged from the chimney, the majority of the PM dispersed directly backward along with the external flow field, while a minor fraction lingered at the stern of the ship for an extended period before eventually diffusing backward. Among them, 1235 particles were captured within a range of 200 m from the stern, with a capture rate of 0.6%. When the synthetic wind shows a yaw angle, the capture rate of PM in the interval increases rapidly with the increase of yaw angle, while other factors have less influence on the capture rate of PM. This study provides foundational guidance for predicting PM diffusion from ship exhaust plumes in Arctic environments, thereby enabling more effective strategies for managing such emissions.

Method for measuring non-stationary motion attitude based on MEMS-IMU array data fusion and adaptive filtering

The error characteristics of a typical commercial MEMS-IMU were analyzed. Using 15 ICM-42688 sensors, a 3*5 MEMS-IMU array was designed, and a weighted data fusion method based on Allan variance for the MEMS-IMU array was proposed. This effectively reduces the random measurement errors of the MEMS-IMU, providing a data foundation for the precise measurement of motion and attitude of robots, vehicles, aircraft, and other systems under low-cost conditions. The text describes a measurement method for non-stationary motion attitudes of sports vehicles based on adaptive Kalman-Mahony. Specifically, it first uses adaptive Kalman filter on array sensor data to calculate the measurement noise in real-time and adaptively adjust the filtering gain. Then, it determines the compensation coefficient of the accelerometer to the gyroscope angular velocity based on the motion state of the vehicle, and solves the attitude through complementary filtering to obtain the motion attitude quaternion. Finally, it converts it into the roll angle, pitch angle, and yaw angle of the sports vehicle. Experimental results show that the proposed MEMS-IMU array weighted data fusion method based on Allan variance has significant advantages over traditional single MEMS-IUM methods and traditional average weighting methods in reducing sensor angle random walk and zero drift instability. The proposed adaptive Kalman-Mahony attitude measurement method also shows a significant improvement in the accuracy of non-stationary motion attitude measurement compared to traditional methods.