Velocity Error Research Articles

A distributed model predictive control method combined with delay compensator for multiple vehicle platoons

With an increasing number of vehicles on the road, multiple-vehicle platoons have become a new solution under communication constraints. However, the existing studies seldom consider the effect of the tail vehicle on the information transfer and platoon control over long distances, and thus the control performance under time-varying delays is unacceptable. In this study, a hierarchical multi-platoon control architecture considering the backbone platoon and sub-platoons is proposed under non-ideal communication conditions, where the backbone platoon layer communication topology is designed to achieve backbone platoon control and appropriate coordination among sub-platoons. Distributed model predictive controllers (DMPCs) for backbone- and sub-platoons that integrate delay compensation have been proposed. Buffers and delay compensators in the proposed DMPC are designed to reduce the interference caused by non-ideal communications. The asymptotic stability is analyzed at the sub- and multi-platoon levels. The results show that compared with traditional DMPC without a compensator, the proposed DMPC can eliminate the velocity lag and steady-state spacing errors and maintain the maximum velocity and spacing errors within 0.11 m/s and 0.17 m, respectively, under certain operating conditions. Thus, the proposed method guarantees the stability of multiple platoons under both the constant and time-varying delay conditions.

Sensitivity of flame structure and flame speed in numerical simulations of laminar aluminum dust counterflow flames

Aluminum dust counterflow flames are investigated numerically using a two-stage model including the effects of interphase heat transfer, phase change, heterogeneous surface reaction (HSR), oxide cap growth, homogeneous combustion and radiation. The numerical model is first validated by simulating the aluminum dust counterflow flames of McGill University [Julien et al., 2017]. The results show that the particle velocity profile is consistent with the experimental results, and the error in the gas phase velocity caused by the use of aluminum particles as tracers in the experiment is analyzed. Then, further validation is conducted by comparison of the flame speed, and the predicted results agree well with the measurements. The flame structure of the aluminum dust counterflow flame is analyzed, and intermediate product (AlO) is observed to present a discrete distribution in space due to the nature of heterogeneous combustion. Different terms in the particle energy equation are analyzed, and the results indicate that the HSR plays an important role in the late preheating stage, while the interphase heat transfer dominates the rest of the conversion process. Under the assumption of unity Lewis number, the interphase heat/mass transfer models are found to have a great impact on the flame for a particle size smaller than 10 µm. With increasing particle sizes, the flame speed decreases, and most particles with a diameter of 12 µm cannot be fully burnt in the flame under the studied conditions. The effect of the strain rate on the flames is investigated with different particle sizes and unstretched reference flame speeds are obtained by linear extrapolation of the predicted results. Finally, a sensitivity analysis of the parameters of the heterogeneous surface reaction model is conducted.

Autonomous Navigation of Unmanned Aircraft Using Space Target LOS Measurements and QLEKF.

An autonomous navigation method based on the fusion of INS (inertial navigation system) measurements with the line-of-sight (LOS) observations of space targets is presented for unmanned aircrafts. INS/GNSS (global navigation satellite system) integration is the conventional approach to achieving the long-term and high-precision navigation of unmanned aircrafts. However, the performance of INS/GNSS integrated navigation may be degraded gradually in a GNSS-denied environment. INS/CNS (celestial navigation system) integrated navigation has been developed as a supplement to the GNSS. A limitation of traditional INS/CNS integrated navigation is that the CNS is not efficient in suppressing the position error of the INS. To solve the abovementioned problems, we studied a novel integrated navigation method, where the position, velocity and attitude errors of the INS were corrected using a star camera mounted on the aircraft in order to observe the space targets whose absolute positions were available. Additionally, a QLEKF (Q-learning extended Kalman filter) is designed for the performance enhancement of the integrated navigation system. The effectiveness of the presented autonomous navigation method based on the star camera and the IMU (inertial measurement unit) is demonstrated via CRLB (Cramer–Rao lower bounds) analysis and numerical simulations.

Flocking control for Cucker–Smale model under denial‐of‐service attacks

AbstractIn this article, the issue of flocking control for Cucker–Smale model under denial‐of‐service (DoS) attacks is discussed. It is assumed that there is an active stage and an asleep stage for each period of the DoS. Malicious attacks partially or completely destroy the network communication links in the active stages, and replenish energy without any attacks in the dormant stages. By constructing the agents' position and velocity errors, the C–S model under DoS attacks is equivalently transformed into error systems. By applying the approach of products convergence for infinite sub‐stochastic matrices, a sufficient condition for producing the flocking behavior is obtained. An upper bound of relative errors for agents' final positions is established, which depends on initial states, the topology structure and the weight function. Finally, simulation experiments verify the effectiveness of the theoretical results.

ESO-based event-triggered attitude control of spacecraft with unknown actuator faults

An event-triggered control strategy based on extended state observer (ESO) is proposed for the attitude tracking problem of small plug-and-play spacecraft with uncertain inertia parameters, external disturbances, and actuator faults. A simplified controller is developed based on the angular velocity and the general disturbances estimated by the provided ESO using the information of the system inputs and the angular velocities. In the designed event-triggered sampling mechanism, a state-dependent event-triggered strategy determines the triggering instant of the controller to reduce the frequency of information transmission between the controller and the actuator. In comparison with the previous literature, this paper considers uncertain inertia parameters, external disturbances, and actuator faults as general disturbances estimated by ESO, especially for the actuator faults. The inputs of ESO are the error of the angular velocities, which can simplify the controller design. Moreover, the designed ESO can effectively attenuate the influence of measured noises generated by the gyroscopes. The proposed event-triggered policy balances the performance of event-triggering and the control stability performance, which reduces the final state convergence regions without increasing more triggering times compared to existing studies. Furthermore, the investigated policy achieves Zeno-free triggering. Numerical simulations verify theoretical results.

Reconstruction of Motion Images from Single Two-Dimensional Motion-Blurred Computed Tomographic Image of Aortic Valves Using In Silico Deep Learning: Proof of Concept

The visualization of motion is important in the diagnosis and treatment of aortic valve disease. It is difficult to perform using computed tomography (CT) because of motion blur. Existing research focuses on suppressing or removing motion blur. The purpose of this study is to prove the feasibility of inferring motion images using motion information from a motion-blurred CT image. An in silico learning method is proposed, to infer 60 motion images from a two-dimensional (2D) motion-blurred CT image, to verify the concept. A dataset of motion-blurred CT images and motion images was generated using motion and CT simulators to train a deep neural network. The trained model was evaluated using two image similarity evaluation metrics, a structural similarity index measure (0.97 ± 0.01), and a peak signal-to-noise ratio (36.0 ± 1.3 dB), as well as three motion feature evaluation metrics, maximum opening distance error between endpoints (0.7 ± 0.6 mm), maximum-swept area velocity error between adjacent images (393.3 ± 423.3 mm2/s), and opening time error (5.5 ± 5.5 ms). According to the results, the trained model can successfully infer 60 motion images from a motion-blurred CT image. This study demonstrates the feasibility of inferring motion images from a motion-blurred CT image under simulated conditions.

Compensatory saccades differ between those with vestibular hypofunction and multiple sclerosis pointing to unique roles for peripheral and central vestibular inputs.

Individuals with peripheral or central vestibular dysfunction recruit compensatory saccades (CSs) in response to high acceleration, yaw head impulses. Although CSs have been shown to be an effective strategy for reducing gaze position error (GPE) in individuals with peripheral hypofunction, for individuals with central vestibular dysfunction, the effectiveness of CS is unknown. The purpose of our study was to compare the effectiveness of CS, defined as the ability to compensate for head velocity and eye position errors, between persons with central and peripheral vestibular dysfunction. We compared oculomotor responses during video head impulse testing between individuals with unilateral peripheral vestibular deafferentation, a disorder of the peripheral vestibular afferents, and individuals with multiple sclerosis, a condition affecting the central vestibular pathways. We hypothesized that relative to individuals with peripheral lesions, individuals with central dysfunction would recruit CSs that were delayed and inappropriately scaled to head velocity and GPE. We show that CSs recruited by persons with central vestibular pathology were not uniformly deficient but instead were of a sufficient velocity to compensate for reductions in VOR gain. Compared to those with peripheral vestibular lesions, individuals with central pathology also recruited earlier covert CS with amplitudes that were better corrected for GPE. Conversely, those with central lesions showed greater variability in the amplitude of overt CS relative to GPE. These data point to a unique role for peripheral and central vestibular inputs in the recruitment of CS and suggest that covert CSs are an effective oculomotor strategy for individuals with multiple sclerosis.NEW & NOTEWORTHY Compensatory saccades (CSs) are recruited by individuals with unilateral vestibular deafferentation (UVD) to compensate for an impaired vestibulo-ocular reflex (VOR). The effectiveness of CS in multiple sclerosis (MS), a central vestibular impairment, is unknown. We show that in UVD and in MS, covert CSs compensate for reduced VOR gain and minimize gaze position error (GPE), yet in >50% of individuals with MS, overt CS worsened GPE, suggesting unique roles for peripheral and central vestibular inputs.

Development of depth-averaged nonlinear flow model for fractures considering heterogeneity and significant inertial effects

Fluid flow through fractures is central to many engineering applications and geophysical processes. Although the intertwined effects of aperture heterogeneity and inertial forces on fluid flow have been interrogated in last few decades, it remains challenging to accurately predict fracture flow field at high pressure gradient conditions when inertial effects are significant. To address this challenge, we rigorously derived a depth-averaged nonlinear flow model by introducing a quadratic velocity term into the linear flow model (i.e., Local Cubic Law that describes a linear relationship between pressure gradient and fluid flux) at the mechanistic level. The supporting laboratory experiments and direct numerical simulations through six rough-walled fractures with varying roughness further confirmed the robustness of the proposed model. We found that the predictive model outperforms existing alternatives with acceptable relative errors in mean velocity (<7%) considering a broad range of Reynolds number (0.01–167), suggesting its superior performance of handling pronounced inertial effects. Lastly, we parameterized the new model by establishing an empirical function that related parameter (ω) in the model to measurable fracture properties (i.e., aperture); this makes the predictive model viable and straightforward for predicting local velocity fields in pressurized engineered environments.

Correction to: Mitigating Velocity Errors in Least-Squares Imaging Using Angle-Dependent Forward and Adjoint Gaussian Beam Operators

Correction to: Mitigating Velocity Errors in Least-Squares Imaging Using Angle-Dependent Forward and Adjoint Gaussian Beam Operators

Streamline penetration, velocity error, and consequences of the feedback immersed boundary method

This paper presents a study on streamline penetration, velocity error, and consequences of a fluid–structure interaction (FSI) solver based on the feedback immersed boundary method (IBM). In the FSI solver, the fluid dynamics is solved by the lattice Boltzmann method; the solid structure deformation is solved by the finite difference method and the finite element method for two- and three-dimensional cases, respectively; and the feedback IBM is used to realize the interaction between the fluid and the structure. The IBM is implemented in non-iterative and iterative ways. For the non-iterative version, two types of integration are discussed: without and with velocity prediction step. Five benchmark cases are simulated to study the performance of the three implementations: a uniform flow over a cylinder, flow-induced vibration of a flexible plate attached behind a stationary cylinder in a channel, flow through a two-dimensional asymmetric stenosis, a one-sided collapsible channel, and a three-dimensional collapsible tube. Results show that both the IBM with prediction step, the iterative IBM, and one iteration IBM with proper feedback coefficients can suppress the spurious flow penetration on the solid wall. While the velocity error does not significantly affect the force production and structure deformation for external flows, reducing it significantly improves the prediction of the force distribution and structure deformation for internal flows. In addition, the iterative IBM with smaller feedback coefficient has better numerical stability. This work will provide an important guideline for the correct use of the feedback IBMs.

An Accurate Wind Retrieval Method Based on Observing Hydrogen Balloons with Multi-Theodolite Measurements

Abstract The hydrogen balloon is widely used for wind sensing by tracking it with optical theodolites. The traditional theodolite observation (single- and double-theodolite) methods assume that the balloon is a perfect tracer of the background wind and it rises with a constant speed during the whole observation period, but these assumptions may not hold well in complex wind circumstances. In this paper, an accurate wind field retrieval method based on multi-theodolite measurement is proposed. The extended Kalman filter algorithm is used to filter the angle data observed by the theodolites in order to accurately estimate the trajectory of the balloon, and the motion equation is used to correct the velocity difference between the background wind and the balloon. As a result, not only the horizontal velocity but also the vertical velocity can be accurately retrieved by this method. Numerical simulation and field experiments show that the multi-theodolite observation method excels the traditional single-theodolite method, and the velocity errors can be reduced by even more than 40% in comparison with the single-theodolite method for complex wind cases. Significanace Statement In the meteorological community, hydrogen balloon tracking is a widely used wind retrieval method, but the accuracy is limited, especially under complex wind conditions. In this paper, a new method based on tracking the hydrogen balloon with multi-theodolite is proposed, which uses the extended Kalman filter and the motion equation to get an accurate estimation of the balloon’s velocity and fix the inertia effect of balloon, respectively. Simulation and field experiment show that the new method can reduce the velocity error by more than 40% compared with the traditional method.

Distributed Adaptive Fixed-Time Fault-Tolerant Control for Multiple 6-DOF UAVs With Full-State Constraints Guarantee

In contrast with most existing results concerning unmanned aerial vehicles (UAVs) wherein material points or only attitude/longitudinal dynamics are considered, this article proposes a distributed fixed-time fault-tolerant control methodology for networked fixed-wing UAVs whose dynamics are six-degree-of-freedom with twelf-state-variables subject to actuator faults and full-state constraints. More precisely, state transformations with the scaling function are devised to keep the involved velocity and attitude within their corresponding constraints. The fixed-time property is obtained in the sense of guaranteeing that the settling time is lower bounded by a positive constant, which is independent of initial states. The actuator faults as well as the network induced errors are handled via the bound estimation approach and well-defined smooth functions. By strict Lyapunov arguments, all closed-loop signals are proved to be semiglobally uniformly ultimately bounded, and the tracking errors of velocity and attitude converge to the residual sets around origin within a fixed time.

An Improved Dynamic Surface Sliding Mode Method for Autonomous Cooperative Formation Control of Underactuated USVS with Complex Marine Environment Disturbances

Abstract In this paper, a novel dynamic surface sliding mode control (DSSMC) method, combined with a lateral velocity tracking differentiator (LVTD), is proposed for the cooperative formation control of underactuated unmanned surface vehicles (USVs) exposed to complex marine environment disturbances. Firstly, in view of the kinematic and dynamic models of USVs and the design idea of a virtual control law in a backstepping approach, the trajectory tracking control problem of USVs’ cooperative formation is transformed into a stabilisation problem of the virtual control law of longitudinal and lateral velocities. Then, aiming at the problem of differential explosion caused by repeated derivation in the process of backstepping design, the first-order low-pass filter about the virtual longitudinal velocity and intermediate state quantity of position is constructed to replace differential calculations during the design of the control law, respectively. In order to reduce the steady-state error when stabilising the virtual lateral velocity control law, the integral term is introduced into the design of the sliding mode surface with a lateral velocity error, and then the second-order sliding mode surface with an integral is structured. In addition, due to the problem of controller oscillation and the role of the tracking differentiator (TD) in active disturbance rejection control (ADRC), the LVTD is designed to smooth the state quantity of lateral velocity. Subsequently, based on the dynamic model of USV under complex marine environment disturbances, the nonlinear disturbance observer is designed to observe the disturbances and compensate the control law. Finally, the whole cooperative formation system is proved to be uniformly and ultimately bounded, according to the Lyapunov stability theory, and the stability and validity of the method is also verified by the simulation results.

Adaptive Fault-Tolerant Fast Finite-Time Consensus Protocols for Multiple Mechanical Systems With Output Constraints

In this article, we discuss fast finite-time consensus (FFTC) problems for uncertain nonlinear multiple mechanical systems with actuator faults and time-varying asymmetric output constraints. In order to guarantee the constraints are satisfied, an appropriate nonlinear mapping (NM) is employed to transform the original system with output constrains into an corresponding unconstrained one. Combining neural networks technology, graph theory, fast finite-time control theory, and backstepping technology, the actuator faults are considered to propose a distributed adaptive finite-time consensus (FTC) protocol, which can guarantee the position errors as well as the velocity errors reaching a region in finite time. Finally, an illustrative example is presented to support the obtained theoretical results.

Adaptive appointed-time formation tracking control for multiple spacecraft with collision avoidance under a dynamic event-triggered mechanism

This paper focuses on the appointed-time formation control problem for multiple spacecraft with limited communication and control resources. The control objective is to make each spacecraft move along its reference trajectory with guaranteed tracking performance while avoiding collision with each other. To this end, we attempt to propose an event-triggered formation tracking control protocol to achieve this objective. Firstly, a sliding mode manifold containing formation tracking and velocity errors is designed. Then, a group of prescribed performance constraints are imposed on the transient and steady-state behaviors of the newly defined sliding mode manifold, to derive a formation controller with performance guarantees and appointed-time convergence. By integrating the artificial potential function, a collision-free control term is devised, which is plus after the foregoing formation tracking controller to avoid the collision between the neighboring spacecraft. Furthermore, to reduce unnecessary data transmission and improve resource utilization, a dynamic event-triggered strategy is proposed to determine when the developed controller updates. In this case, the threshold parameter in triggering condition is dynamically changed over time rather than being fixed, in order to achieve a desired balance between communication frequency and system performance. Compared with the existing works, the major advantage of the proposed control protocol is that the formation tracking performance, the collision avoidance and the communication transmission preservation can be assured simultaneously. Finally, comparative simulations illustrate the effectiveness of the proposed protocol.

Monitoring Subsidence over the Planned Jakarta–Bandung (Indonesia) High-Speed Railway Using Sentinel-1 Multi-Temporal InSAR Data

Investigation of subsidence situations is important for the design of railway lines. Traditional geological investigations are expensive. Multi-temporal InSAR (MT-InSAR) analysis has shown great potential for detecting subsidence areas along large-scale manmade linear features (LMLFs). The disadvantage of applying high-resolution SAR data such as that from TerraSAR-X and COSMO-SkyMed data is the high cost. By contrast, the free and open data from Sentinel-1 A/B offer an unprecedented opportunity for InSAR applications. Despite these advantages, research investigating the potential for monitoring subsidence along LMSFs using multi-temporal Sentinel-1 InSAR data at the design stage of LMSFs is limited. This paper explores the monitoring ability of MT-InSAR analysis with Sentinel-1 A/B multi-temporal data in an experiment along the planned Jakarta-Bandung high-speed railway in Bandung, Indonesia. Subsidence patterns along the route of the planned high-speed railway were analyzed in detail. By means of validation using 12 leveling points, the root mean square error (RMSE) of the average displacement velocity that was obtained from the MT-InSAR results was found to be 10.31 mm/yr. This paper demonstrates the potential of Sentinel-1 multi-temporal INSAR data to detect potential subsidence areas along planned LMLFs (e.g., the planned high-speed railway that is the focus of this study), serving as a reference for further planning of monitoring subsidence over planned LMLFs.

A Self-Aided Strapdown inertial navigation method based on maneuver constraints and incremental observation

In some special cases, SINS becomes the last mean of navigation for AUVs. For SINS, navigation errors will accumulate with time. Aiming to address this problem, a SINS self-aided method is designed in this paper. This method first makes the AUV perform periodic maneuvers and uses a digital filter to obtain a set of high precision velocities as auxiliary information for Kalman-based information fusion. However, when the velocity signal is passed through the high-pass filter, the constant term caused by both the driving force and ocean current is filtered out along with the low-frequency SINS error. To address this problem, this method constructs a virtual velocity by introducing incremental observations from SINS velocity and aided velocity to decouple the missing constant values in auxiliary velocity and SINS velocity error. The results of mathematical simulation and semi-physical test show that the method can realize the self-aided calibration and is immune to the ocean current.

A novel pulsar-based template-independent navigation method

AbstractBecause of the high photon flux, the Crab nebula pulsar is widely used as the observation target for X-ray pulsar-based navigation. The built profile of the Crab pulsar will change over time, however, which means that the pre-calibrated template cannot be used for the long term. In this paper, a novel pulsar-based template-independent navigation method is proposed. The detected phase propagation model is given as a term of position of the vehicle, taking the orbital motion into account. A different method of time-of-arrival process between the recovered profiles is introduced. With the aid of orbital transition matrix, a measurement model is derived to be a term of velocity error of the vehicle varying with time. The state errors of the vehicle are transformed into velocity errors by performing multi-segment observations to achieve the navigation system observability. The navigation equations of the system are then established and can be solved directly. Some simulations are performed to verify the method and suggest that the proposed method is feasible, effective and easy to implement. The precise orbit information of the vehicle can be determined. The state estimation accuracy is basically consistent with the traditional filtering algorithms, and the computational cost is still very low.

Composite error-based intelligent smooth sliding mode control of a dual parallel robot for automobile electro-coating conveying

Different coating technological requirements of automobile electro-coating control system have different desired trajectories. For a dual parallel robot used for automobile electro-coating conveying (DPRAEC), an intelligent tracking smooth sliding mode synchronization control method (ITSSMSC) is proposed to achieve better tracking control performance with high synchronization and flexibility for the system under different desired trajectories. According to the dual parallel and symmetrical structure of the robot, the sliding surface is designed based on a composite error. A deep neural network with multi-hidden layer is trained by deep learning algorithms. Then, any given trajectories caused by different coating technological requirements could be tracked without tuning additional parameters and the synchronous performance could be guaranteed. The convergence of the network training process and the stability of the ITSSMSC are proved theoretically. Compared with the synchronization proportion-derivative control, the sliding mode control based on constant velocity reaching law without synchronization control and the smooth sliding mode synchronization control without training, simulation and experimental results demonstrate the effectiveness of the proposed approach. Compared with synchronization proportion-derivative control, the maximum tracking errors of constant velocity reaching law without synchronization control among each active joint are, respectively, reduced 92.2%, 94.7%, 98.8%. The maximum tracking errors of smooth sliding mode synchronization control and ITSSMSC in [Formula: see text] direction are, respectively, 9.06×10−5m and 1.01×10−9m. The maximum tracking errors of smooth sliding mode synchronization control and ITSSMSC in [Formula: see text] angle are, respectively, 4.50×10−3rad and 1.45×10−4rad. Therefore, the proposed control method could improve the tracking control performance and synchronization performance simultaneously under different desired trajectories.

Three-dimensional wind field reconstruction using tucker decomposition with optimal sensor placement

Short-term wind prediction is of great significance for wind power trading strategy, equipment protection, and effective control. Physical models can provide accurate and detailed wind speed data but require a large amount of time for calculation. To solve this problem, based on Tucker decomposition, a sensor and CFD (Computational Fluid Dynamics) data fusion technique is proposed, which allows us to readily obtain an estimation of the ‘actual’ three-dimensional wind field from sensor observations. Moreover, a new greedy algorithm based on minimum condition is developed to stabilize the inverse process and derive optimal sensor placement. In the simulation, the ‘actual’ wind field can be reconstructed with reasonable accuracy for different wind shear characteristics, and the relative errors of absolute velocity is less than 0.2%. Additionally, the reconstruction time is much less than ultra-short-term forecasting and accounting for only 0.2%–3.2% of the CFD calculation time. The experimental results show that the optimal placement is much more effective than random placement in terms of minimizing the relative error (the relative error can be controlled within 6%). In general, the innovative approach combines the advantages of both statistical and physical approaches while compensating for their shortcomings, making it potentially valuable in short-term wind forecasting.