Motion Model Research Articles

Design of air-breathing supersonic vehicle guidance law for intercepting the high maneuvering target with impact angle constraint

Abstract To achieve interception vehicles that can intercept high maneuvering targets with impact angle constraint, and improve the strike effectiveness of the vehicle, based on the two-dimensional planar motion model of the vehicle and the target, this article proposes a sliding mode control vehicle guidance law. The stability of the guidance law is verified through the Lyapunov function, and an extended state observer is set to check for disturbances. After simulation verification, the guidance law can intercept the target with predetermined terminal angle constraints and has good tracking performance.

Geometric Asian power option pricing with transaction cost under the geometric fractional Brownian motion with [formula omitted] sources of risk in fuzzy environment

In this paper, we obtain an explicit formula to calculate the geometric Asian power option price with floating strike price and transaction cost under the fractional geometric Brownian motion model with w sources of risk and fuzzy parameters. First, by considering the Leland and Kabanov theorems, we derive a non-linear PDE with the transaction cost formula to obtain the option price. Then, using the Green function find a closed form solution for the PDE and achieve the price of the option under different amounts of the model and option parameters. Next, we consider the model’s parameters as fuzzy numbers and acquire a general formula to obtain intervals for the option price under different belief degrees and power option parameter amounts.

Anti-windup design for supercavitating vehicle based on sliding mode control combined with RBF network

During the longitudinal motion of a supercavitating vehicle, the stability control problem is complicated because of the nonlinear planing force on the tail part. The dynamic model of a supercavitating vehicle in longitude plane is nonlinear, simultaneously, the control instructions of a supercavitating vehicle may exceed the physical limits of an actuator. Therefore, designing a longitudinal stability control system for a supercavitating vehicle, not only the treatment of nonlinear planing force, but also the physical constraints of the actuator should be considered. For the longitudinal motion model of supercavitating vehicle, a cascade model is proposed, which decomposes the longitudinal motion of supercavitating vehicle into two subsystems. Sliding mode control based on RBF neural network compensation is adopted in the controller design process, and RBF neural network is exploited to approach the deviation caused by actuator saturation. The proposed control method can effectively compensate the performance degradation caused by control variable saturation, and has strong robustness.

Can software-defined vehicles never roll over: A perspective of active structural transformation

The revolution of physical structure is highly significant for future software defined vehicles (SDV). Active structural transformation is a promising feature of the next generation of vehicle physical structure. It can enhance the dynamic performance of vehicles, thus providing safer and more comfortable ride experiences, such as the ability to avoid rollover in critical situations. Based on the active structural transformation technology, this study proposes a novel approach to improve the dynamic performance of a vehicle. The first analytical motion model of a vehicle with active structural transformation capability is established. Then, a multi-objective optimization problem with the adjustable parameters as design variables is abstracted and solved with an innovative scenario specific optimization method. Simulation results under different driving scenarios revealed that the active transformable vehicle applying the proposed method could significantly improve the handling stability without sacrificing the ride comfort, compared with a conventional vehicle with a fixed structure. The proposed method pipeline is defined by the software and supported by the hardware. It fully embodies the characteristics of SDV, and inspires the improvement of multiple types of vehicle performance based on the concept of “being defined by software” and the revolution of the physical structure.

Identification of Dynamic Models of Vertical Motion for a Multi-rotor Flying Platform

The article presents the results of identifying dynamic models for an unmanned multi-rotor platform. Due to such an object’s highly complex mathematical model, it was decided to identify dynamic models based on experimental data. The identification concerns vertical movement parameters, but also energy consumption when performing maneuvers, which is a key factor for autonomous aircraft.

Maximum likelihood phylogeographic inference of cell motility and cell division from spatial lineage tracing data.

Recently developed spatial lineage tracing technologies induce somatic mutations at specific genomic loci in a population of growing cells and then measure these mutations in the sampled cells along with the physical locations of the cells. These technologies enable high-throughput studies of developmental processes over space and time. However, these applications rely on accurate reconstruction of a spatial cell lineage tree describing both past cell divisions and cell locations. Spatial lineage trees are related to phylogeographic models that have been well-studied in the phylogenetics literature. We demonstrate that standard phylogeographic models based on Brownian motion are inadequate to describe the spatial symmetric displacement (SD) of cells during cell division. We introduce a new model-the SD model for cell motility that includes symmetric displacements of daughter cells from the parental cell followed by independent diffusion of daughter cells. We show that this model more accurately describes the locations of cells in a real spatial lineage tracing of mouse embryonic stem cells. Combining the spatial SD model with an evolutionary model of DNA mutations, we obtain a phylogeographic model for spatial lineage tracing. Using this model, we devise a maximum likelihood framework-MOLLUSC (Maximum Likelihood Estimation Of Lineage and Location Using Single-Cell Spatial Lineage tracing Data)-to co-estimate time-resolved branch lengths, spatial diffusion rate, and mutation rate. On both simulated and real data, we show that MOLLUSC accurately estimates all parameters. In contrast, the Brownian motion model overestimates spatial diffusion rate in all test cases. In addition, the inclusion of spatial information improves accuracy of branch length estimation compared to sequence data alone. On real data, we show that spatial information has more signal than sequence data for branch length estimation, suggesting augmenting lineage tracing technologies with spatial information is useful to overcome the limitations of genome-editing in developmental systems. The python implementation of MOLLUSC is available at https://github.com/raphael-group/MOLLUSC.

A dynamic scale-mixture model of motion in natural scenes.

Some of the most important tasks of visual and motor systems involve estimating the motion of objects and tracking them over time. Such systems evolved to meet the behavioral needs of the organism in its natural environment, and may therefore be adapted to the statistics of motion it is likely to encounter. By tracking the movement of individual points in movies of natural scenes, we begin to identify common properties of natural motion across scenes. As expected, objects in natural scenes move in a persistent fashion, with velocity correlations lasting hundreds of milliseconds. More subtly, but crucially, we find that the observed velocity distributions are heavy-tailed and can be modeled as a Gaussian scale-mixture. Extending this model to the time domain leads to a dynamic scale-mixture model, consisting of a Gaussian process multiplied by a positive scalar quantity with its own independent dynamics. Dynamic scaling of velocity arises naturally as a consequence of changes in object distance from the observer, and may approximate the effects of changes in other parameters governing the motion in a given scene. This modeling and estimation framework has implications for the neurobiology of sensory and motor systems, which need to cope with these fluctuations in scale in order to represent motion efficiently and drive fast and accurate tracking behavior.

When a Plateau Suppresses a Plume: Disappearance of the Samoan Plume Under the Ontong Java Plateau

AbstractThe Samoan hotspot generated an age‐progressive volcanic track that can be traced back to 24 Ma at Alexa Bank, but the trace of the older portion (>24 Ma) of the hotspot track is unclear. We show that six seamounts located in and around the Magellan Seamount chain—north of the Ontong‐Java Plateau (OJP)—have ages (87–106 Ma), geochemistry, and locations consistent with absolute plate motion model reconstructions of the Samoan hotspot track in the late Cretaceous, and three additional seamounts have geochemistry and locations consistent with a Samoan origin. However, a large segment of the Samoan hotspot (24–87 Ma) remains unidentified. Absolute plate motion models show that, from ∼60 to 30 Ma, the OJP passed over the Samoan plume. The exceptional thickness of the OJP lithosphere may have largely suppressed Samoan plume melting because the inferred volcanic trace of the Samoan hotspot wanes, and then disappears, on the OJP. Fortunately, 44 Ma volcanism at Malaita Island, located on the southern margin of the OJP, has a location, age, and geochemistry consistent with a Samoan plume origin, and provides a “missing link” bridging the younger and older segments of the Samoan hotspot. Our synthesis of geochemical, geochronological, and plate motion model evidence reveals that Samoa exhibits a clear hotspot age progression for over 100 Myr. Passage of ancient plateaus over young plumes—here called “plume‐plateau” interaction—may be relatively common: the OJP also passed over the putative Rarotonga hotspot, and the Society and Pitcairn hotspots were overtopped by the Manihiki Plateau.

Spatially variable seismic underground motions in layered porous seabed medium induced plane wave

In ocean engineering anti-seismic filed, the marine environment is usually regarded as the coupling field of seawater-layered porous seabed-elastic bedrock. This paper presents a theoretical method to study the spatially variable seismic underground motions in stratified seabed system. Based on the potential flow theory, Biot's porous model and random vibration theory, the transfer function and coherence function model of the underground seismic motions in the marine coupling field are derived. On the basis of the above theoretical derivation, the program of the spatial variability seismic underground motions of the coupling field is simulated and developed, and its accuracy and effectiveness are verified. Moreover, the effects of soil stiffness and overlying seawater level on the seismic response are also analyzed. Numerical results show that spatially variable seismic underground motions are significantly different from those on the ocean bottom. The simulation of spatially variable seismic underground motions for layered seabed field is significant for both theoretical study and ocean engineering application.

Temporal variations of the ‘in-situ’ nonlinear behaviour of shallow sediments during the 2016 Kumamoto Earthquake sequence

SUMMARY Strong ground shaking has the potential to generate significant dynamic strains in shallow materials such as soils and sediments, thereby inducing nonlinear site response resulting in changes in near-surface materials. The nonlinear behaviour of these materials can be characterized by an increase in wave attenuation and a decrease in the resonant frequency of the soil; these effects are attributed to increased material damping and decreased seismic wave propagation velocity, respectively. This study investigates the ‘in-situ’ seismic velocity changes and the predominant ground motion frequency evolution during the 2016 Kumamoto earthquake sequence. This sequence includes two foreshocks (Mw 6 and Mw 6.2) followed by a mainshock (Mw 7.2) that occurred 24 hr after the last foreshock. We present the results of the seismic velocity evolution during these earthquakes for seismological records collected by the KiK-net (32 stations) and K-NET (88 stations) networks between 2002 and 2020. We analyse the impulse response and autocorrelation functions to investigate the nonlinear response in near-surface materials. By comparing the results of the impulse response and autocorrelation functions, we observe that a nonlinear response occurs in near-surface materials. We then quantify the velocity reductions that occur before, during, and after the mainshock using both approaches. This allows us to estimate the ‘in-situ’ shear modulus reduction for different site classes based on V$_{S30}$ values (V$_{S30}\lt 360$ m s−1, $360\lt $V$_{S30}\lt 760$ m s−1 and V$_{S30}\gt 760$ m s−1). We also establish the relationships between velocity changes, shear modulus reduction, variations in predominant ground motion frequencies and site characteristics (V$_{S30}$). The results of this analysis can be applied to site-specific ground motion modelling, site response analysis and the incorporation of nonlinear site terms into ground motion models.

An Accurate and Rapid Docking Algorithm for Four-Way Shuttle in High-Density 3D Warehousing Environment

High-density 3D warehousing is the future cornerstone of modern logistics storage. It aims to efficiently manage and store various types of goods by utilizing 3D space and automation technology. The accurate and fast docking algorithm of the four-way shuttles, as a core method of carrying the goods in high-density 3D warehousing, has become an increasingly important technical challenge. Inaccurate docking will result in a failure to change direction, while long docking time will affect the operation efficiency of the four-way shuttle. To overcome these obstacles, this paper presents an accurate and rapid docking algorithm for four-way shuttles. Firstly, the deceleration and jerk of a four-way shuttle in the braking stage are initialized according to the motion parameter set calculated by the docking motion model. Then, the four-way shuttle starts to move until the stop signal has been detected. If the system fails to detect the stop signal, the distance compensation is needed to ensure that the four-way shuttle can arrive at the stop point. Finally, the four-way shuttle stops immediately by way of amplifying the deceleration when reaching the stop point. The result shows that the accuracy of rapid docking is superior to that of direct docking. Compared with crawling docking, rapid docking reduces the braking time by 3.99s and stops at a speed lower than the crawling speed. The rapid docking algorithm not only enhances the throughput of high-density 3D warehousing but also improves the service life of four-way shuttles.

Mechanism and experimental investigation on the formation of micro‐triangle stepped jet in composite spinning solution

AbstractIn the field of nanotechnology, rotary jet spinning technology has garnered attention due to its unique advantages in fabricating composite nanofibers. The composite nanofibers prepared using this technology exhibit exceptional purity, uniformity, and consistency, thereby enhancing reliability and controllability in practical applications. During the process of rotary jet spinning, the spinning solution flows into the nozzle and converges at a micro‐triangle under the combined influence of centrifugal force, viscous force, surface tension, and gravity. Stepped jet refers to the flow phenomenon occurring within a channel with a gradually decreasing cross‐sectional area. In this process, an increase in the flow velocity of the composite spinning solution aids in stabilizing stretching at the micro‐triangle which is crucial for forming continuous jets. This article analyzes cone formation mechanisms for composite spinning solutions and stepped jet principles while establishing corresponding models that reflect relationships between solution velocity magnitude and parameters such as rotational speed, concentration, and nozzle diameter. Finally, polyethylene oxide (PEO)/polyvinylpyrrolidone (PVP) composite nanofibers were prepared using a rotary jet spinning device where experimental investigation focused on examining how parameters such as rotational speed, solution concentration, and nozzle diameter impact motion of spinning solution within micro‐triangle.Highlights Preparation of PEO/PVP composite nanofibers by rotary jet spinning. Presentation of cone formation mechanisms for composite solutions. The stepped injection principle was proposed. Modeling of composite solution motion. Rotational speed, solution concentration, and nozzle diameter impact motion.

TF-TS: Track-to-Track association via track fusion and track segmentation in distributed multi-sensor systems

Track-to-track association (T2TA) aims at unifying batch numbers of tracks, reducing redundancy, and clarifying the situation. It is the precondition and foundation of track fusion, situation awareness, and traffic control. Existing methods based on statistical reasoning or fuzzy math bring some problems that are difficult to solve simultaneously, such as unreasonable assumptions, unsuitable models, uncertain thresholds, and long association times. In the light of the above problems, in this paper, we proposed a T2TA method via Track Fusion and Track Segmentation (TF-TS). The track fusion module fuses and extracts track features from several tracks to reduce the dependency on prior assumptions, motion models, and thresholds. The track segmentation mapping module transforms track tensors into association matrices directly to improve association efficiency. With four kinds of constraints, the association matrices inferred by TF-TS are close to the real one. TF-TS can reduce the dependence on the assumptions, motion models, and thresholds. It can also reduce the traversal calculation of tracks and increase the association efficiency. The global AIS tracks are used to train and test our model, and association results demonstrate that the proposed method can associate tracks in complex scenarios. Moreover, the efficiency is improved and the demand for the real-time association is satisfied.

A 2D equivalent linear inversion model of bedrock motions in a layered transversely isotropic half-space

Inversion is the process that evaluates input motion on the bedrock from surface motions, primarily for use as input excitation for site seismic response or soil-structure interaction analyses. The paper presents a two-dimensional (2D) equivalent linear inversion model for bedrock motion in a multi-layered transversely isotropic (TI) half-space. Based on the exact dynamic stiffness matrices of TI soil layer and bedrock half-space, an inversion objective function is established from both horizontal and vertical surface acceleration records, and the optimization method is adopted to update the control parameters for the inversion process. Moreover, an equivalent linear method is used to account for the TI soil nonlinearity, thus realizing the nonlinear inversion for bedrock motion and incident angle. The inversion procedure is verified by using a single-layered TI soil profile model with three different materials. The numerical results demonstrate that the bedrock motion and the incident angle in the TI medium can be accurately inversed according to the surface motions and the TI site characteristics, indicating that the inversion method can be used for reliable engineering applications in the TI medium.

Application of Gray Wolf Particle Filter Algorithm Based on Golden Section in Wireless Sensor Network Mobile Target Tracking

In order to address the issue of low tracking accuracy caused by particle depletion in the particle filter, a mobile target tracking algorithm tailored for wireless sensor networks (WSNs) is presented. This algorithm, based on the golden-section gray wolf particle filter (PF), represents a novel approach to target tracking. The algorithm’s originality lies in its ability to guide the particle swarm toward regions of higher weights, thereby striking a balance between global and local exploration capabilities. This not only alleviates issues related to sample depletion and local extrema but also enhances the diversity of the particle swarm, significantly improving tracking performance. To assess the effectiveness of the proposed algorithm, a series of simulation experiments were conducted, comparing it with the extended Kalman filter (EKF) and the standard PF algorithm. The experiments employed a constant velocity circular motion model (CM) for filtering and tracking. The root mean square error metric demonstrated a significant reduction in error of 57% and 37% in comparison to the extended Kalman filter (EKF) and the particle filter (PF), respectively. This serves to illustrate the superiority of our method in enhancing tracking accuracy.

Adaptive sliding mode attitude control of two-wheeled robots for planetary auxiliary: From theory to applications

The value of two-wheeled robots in planetary auxiliary is significant, but their attitude control presents challenges due to their inherent characteristics and external environment conditions. To address this issue, this paper presents an adaptive sliding mode (SMC) control scheme of two-wheeled robots for planetary auxiliary. Firstly, a motion model specifically for two-wheeled robots that focuses on attitude control is established. Secondly, an adaptive reaching law was proposed based on the study (Wang et al. 2014). It improves upon the previous adaptive gain, and it can ensure stable operation when the improved adaptive gain converges to a small neighborhood of the origin. This effectively avoids the chattering phenomenon and guarantees that the sliding mode surface converges within a finite time. On this basis, an adaptive SMC attitude controller is developed, and its stability is proven using Lyapunov stability theory. Finally, comparative simulation experiments are conducted to verify the effectiveness, adaptability, superiority and robustness of the proposed control scheme. (A virtual simulation video generated by Simulink 3D Animation can be viewed onhttps://pan.baidu.com/s/1JxRR7vMyq9Cm-23YHFPnYA?pwd=5lf1)

Optimization of steady spin prediction by nonlinear iterative inversion algorithm

PurposeSince the inception of aircraft, the phenomenon of spin has persistently accompanied aircraft, and research into spin has been ongoing. This paper aims to introduce an optimization technique to enhance the traditional geometric method for predicting steady spin, aiming to achieve more precise predictive outcomes.Design/methodology/approachTo begin with, the force and moment equations used for motion analysis are initially presented, followed by the establishment of the motion model. Subsequently, the forward problem is set up, and the equilibrium solutions for the left and right spins of the aircraft are determined using the geometric method under the basic and wingtip configurations, thus solving the forward problem. In the final stage, nonlinear inversion is applied, and the inversion objective function is formulated based on the least squares approach. Through iterative processes, the measured data are interpolated, leading to the acquisition of the accurate equilibrium solution.FindingsThe findings indicate that the utilization of the nonlinear iterative inversion algorithm has effectively optimized the geometric method, yielding favorable outcomes. Postoptimization, the prediction accuracy has been enhanced, and the error has significantly diminished when compared to the preoptimization results.Originality/valueThe nonlinear inversion algorithm is used to refine the steady spin prediction for general aviation aircraft. This approach significantly mitigates the precision issues inherent in the forward problem. As demonstrated through the simulations provided, the application of the nonlinear iterative algorithm to resolve the inversion function yields promising optimization outcomes.

Trajectory Tracking Control of Fixed-Wing Hybrid Aerial Underwater Vehicle Subject to Wind and Wave Disturbances

The hybrid aerial underwater vehicle (HAUV) could operate in the air and underwater might provide a great convenience for aerial and underwater exploration, and the fixed-wing HAUV (FHAUV) has time, space and cost advantages in future large-scale applications, while the large difference between the aerial and underwater environments is a challenge to control, especially in the air/water transition. However, for FHAUV control, there is a lack of research on phenomena or problems caused by large changes in the air/water transition. In addition, the effects of wind, wave, other factors and conditions on motion control are not investigated. This paper presents the first control study on the above issues. The motion model of FHAUV is developed, with the effects of wind and wave disturbances. Then, this paper improves a cascade gain scheduling (CGS) PID for different media environments (air and water) and proposes a cascade state feedback (CSF) control strategy to address the convergence problem of FHAUV control caused by large speed change in the air/water transition. In the comparisons of the two control schemes in various tracking cases including trajectory slopes, reference speeds, wind and wave disturbances, CSF has a better control effect, convergence rate and robustness; the key factors and conditions of the air/water transition are investigated, the critical relations and feasible domains of the trajectory slopes and reference speeds that the FHAUV must meet to successfully exit the water and enter the air are obtained, the critical slope decreases as the reference speed increases, and the feasible domain of CSF is larger than that of CGS, revealing that CSF is superior than CGS for exiting the water.

Development of state space models for underwater gliders equipped with embedded actuators in vertical and horizontal planes

Nowadays, underwater gliders (UGs) are known as efficiency vehicles to observe and quantify water properties concerning marine environments, seabed characteristics, and water depths. The development of appropriate dynamic and state space models is necessary for designing an effective control system. This study aims to develop state space models for UGs equipped with embedded actuators based on a non-linear dynamic model that takes into account the hydrodynamic characteristics of the body and wings. The method involves utilizing sensitivity analysis and assumptions to construct two distinct state space models, along with the linearization of the non-linear model in horizontal and vertical plane motions. The presented models are compared with simulation and experiment results to enhance validity. Furthermore, the linear models, when compared with the non-linear model and experimental tests, exhibit suitable performance. The results indicate the maximum observed differences of −9.7% in the period of the first cycle, −12.02% in the yaw angle, and 15.5% in the advance length between linear and non-linear models during zigzag, course-changing, and helical maneuvers, respectively. Through the presented linear models, LQR controllers are designed for the SUT glider and are subjected to testing in a real-world environment, experiencing suitable performance.

Model of Electric Locomotive Simulator Cabin Excitations

Striving to increase the speed of rail vehicles and thus improve the comfort of traveling passengers at the same time, undertakes activities in the sphere of ensuring an appropriate level of safety of rail, passenger, and freight transport. One of the elements of activities in this area is the training of train drivers. Until recently, this training consisted of a theoretical and practical part on the vehicle, alongside an experienced train driver. Considering the increasing level of automation of railway traffic control systems and locomotive equipment, as well as training costs and requirements related to the introduction of TSI, it is becoming an increasingly common requirement to conduct practical training on railway vehicle traffic simulators, while the conditions in the simulator cabin and the trainee’s feelings should correspond to the actual driving conditions. A locomotive driving simulator is a system consisting of a cabin of a suitable type of locomotive or EMU, mapped in 1:1 scale, coupled with a motion excitation system and computer programs connected together forming the software of the cab visualization and dynamics system. The basic program simulating the dynamics and kinematics of the cabin’s motion is a program containing a motion dynamics model that generates signals forcing the movement of the exciters on which the cabin’s platform is mounted. The correct operation of the simulation model depends on the created mathematical model, which can be built in several ways. This article presents the issue of building a mathematical model describing the dynamics of the rail vehicle motion, which can then be used in the simulation model of the simulator cabin motion. Two ways of proceeding in the process of approaching the construction of a mathematical model of rail vehicle motion dynamics will be presented, with the possibility of later use in creating a simulation model of the motion of the locomotive simulator cabin. One of the possible routes was used in the past in the construction of the EP09 locomotive simulator.