Reference Velocity Research Articles

A Hierarchical Data-Driven Predictive Control of Image-Based Visual Servoing Systems With Unknown Dynamics.

In this article, a hierarchical predictive control (PC) algorithm is designed for visual servoing mobile robot systems. At the kinematic level, the image-based visual servoing model of a wheeled mobile robot is established. By defining the corresponding performance index of the PC, an iterative linear quadratic regulator (iLQR) is used to obtain the velocity controller and to provide reference velocity for dynamics. In dynamics, a data-driven PC controller based on the Gaussian process (GP) is proposed to obtain the torque controller with unknown dynamics. The input-to-state practical stability (ISpS) of the system based on the proposed data-driven PC method is proved by introducing reasonable assumptions. The corresponding theorem also analyzes the maximum upper bound of GP inference error. Finally, the effectiveness of the proposed hierarchical controller is verified by simulations and experiments.

A variational data assimilation approach for sparse velocity reference data in coarse RANS simulations through a corrective forcing term

The Reynolds-averaged Navier–Stokes (RANS) equations provide a computationally efficient method for solving fluid flow problems in engineering applications. However, the use of closure models to represent turbulence effects can reduce their accuracy. To address this issue, recent research has explored data-driven techniques such as data assimilation and machine learning.An efficient variational data assimilation (DA) approach is presented to enhance steady-state eddy viscosity based RANS simulations. To account for model deficiencies, a corrective force term is introduced in the momentum equation. In the case of only velocity reference data, this term can be represented by a potential field and is divergence-free. The DA implementation relies on the discrete adjoint method and approximations for efficient gradient evaluation.The implementation is based on a two-dimensional coupled RANS solver in OpenFOAM, which is extended to allow the computation of the adjoint velocity and pressure as well as the adjoint gradient. A gradient-based optimizer is used to minimize the difference between the simulation results and the reference data. To evaluate this approach, it is compared with alternative data assimilation methods for canonical stationary two-dimensional turbulent flow problems. For the data assimilation, sparsely distributed reference data from averaged high-fidelity simulation results are used.The results suggest that the proposed method achieves the optimization goal more efficiently compared to applying data assimilation for obtaining the eddy viscosity, or a field modifying the eddy viscosity, directly. The method works well for different reference data configurations and runs efficiently by leveraging coarse meshes.

Effect of Relative Mass on Ion Velocity Cross-Correlations in Ionic Liquids and Molten Salts: Different Perspectives in Different Reference Frames.

In electrolytes, the self- and interdiffusion coefficients, transport numbers, and electrical conductivity can be used to determine velocity cross-correlation coefficients (VCC) that are also accessible through molecular dynamics simulations. In an ionic liquid or molten salt, there are only three, corresponding to correlations between the velocities of distinct ion pairs (cation-anion, cation-cation, and anion-anion) averaged over both the ensemble and time, calculable from experimental ion self-diffusion coefficients and the electrolyte conductivity. Most usually, the mass-fixed frame of reference (with velocities relative to that of the center of mass of the system) is used to discuss the VCC and the distinct diffusion coefficients (DDC) derived from them. Recent work in the literature has suggested a dependence of the DDC on the ratio of the anion to cation mass. Here, we demonstrate, using our own and literature transport property data for a large number of ionic liquids and molten salts, that the trends observed depend on the particular choice of velocity reference frame, mass-, number-, or volume-fixed. The perception of ion-ion interactions may be distorted in the mass- and volume fixed frames when the co-ions have very different masses or volumes, particularly for systems containing light lithium ions.

Time-varying damping ratios and velocities in a high-rise during earthquakes and ambient vibrations from coda wave interferometry

Coda wave interferometry is applied to data from Community Seismic Network MEMS accelerometers permanently installed on nearly every floor of a 52-story steel moment-and-brace frame building in downtown Los Angeles. Wavefield data from the 2019 M7.1 Ridgecrest, California earthquake sequence are used to obtain impulse response functions, and time-varying damping ratios and shear-wave velocities are computed from them. The coda waves are used because of their increased sensitivity to changes in the building’s properties, and the approach is generalized to show that a building’s nonlinear response can be monitored through time-varying measurements of representative pseudo-linear systems in the time domain. The building was not damaged, but temporary nonlinear behavior observed during the strong motions provides a unique opportunity to test this method’s ability to map time-varying properties. Reference damping parameters and velocities are obtained from a month-long period during which no significant seismic activity had occurred. Damping ratios measured over narrow frequency bands increase by up to a factor of 4 over short time durations spanning the main shock, as well as M > 4.5 aftershocks and a foreshock. The largest damping ratio increases occur for the highest frequencies, and the increase is attributed to friction associated with structural and non-structural surface discontinuities which experience relative motion and impact during shaking, resulting in energy loss. Shear-wave velocities in the building’s east–west and north–south directions are found by applying a waveform stretching method to the direct and coda waves. The broadband velocities are reduced by as much as 10% during building shaking, and their restoration to pre-earthquake levels is found to be a function of shaking amplitudes. Until recently, these techniques had been limited by temporal and spatial sparsity of measurements, but in this study, variations of the impulse response functions are resolved over time scales of tens of seconds and on a floor-by-floor spatial scale.

Improved Position Sensorless Resonant Frequency Tracking Control Method for Linear Oscillatory Machine

To improve the reliability of control system and obtain the maximum output efficiency, it is very necessary to carry out the position sensorless piston stroke control and the resonance frequency tracking control for linear oscillatory machine (LOM) at the same time. The conventional resonant frequency tracking control method based on model reference adaptive system (MRAS) has the problem of low estimation accuracy caused by inaccurate model parameters. In order to obtain more accurate resonant frequency, an improved position sensorless resonant frequency tracking control method is proposed in this article. In this method, the adjustable model is constructed based on the mechanical dynamics equation of LOM to track the system resonant frequency. A hybrid terminal sliding-mode observer (HTSMO) is designed to replace the conventional reference model, which can obtain more accurate velocity reference signal for adjustable model by introducing the current error feedback link, thereby further improving the resonant frequency tracking accuracy. Moreover, a second-order generalized integrator (SOGI) is introduced to filter the estimated velocity signal of HTSMO. The filtered velocity signal is not only used for the adjustable model to track the resonant frequency, but also used to obtain the smooth piston stroke signal for the stroke closed-loop control. Comprehensive simulation and experimental results have proved the effectiveness and the superiority of the proposed method.

Safe Region Multi-Agent Formation Control With Velocity Tracking

This paper provides a solution to the problem of safe region formation control with reference velocity tracking for a second-order multi-agent system without velocity measurements. Safe region formation control is a control problem where the agents are expected to attain the desired formation while reaching the target region and simultaneously ensuring collision and obstacle avoidance. To tackle this control problem, we break it down into two distinct objectives: safety and region formation control, to provide a completely distributed algorithm. Region formation control is modeled as a high-level abstract objective, whereas safety and actuator saturation are modeled as a low-level objective designed independently, without any knowledge of the former, and being minimally invasive. Our approach incorporates connectivity preservation, actuator saturation, safety considerations, and absence of velocity measurement from other agents with second-order system dynamics which are important constraints in practical applications. Both internal safety for collision avoidance among agents and external safety for avoiding unsafe regions are ensured using exponential control barrier functions. We provide theoretical results for asymptotic convergence and numerical simulation to show the approach’s effectiveness.

Hierarchical Velocity Optimization for Connected Automated Vehicles With Cellular Vehicle-to-Everything Communication at Continuous Signalized Intersections

The rapid development of intelligent connected technologies and cellular vehicle-to-everything communication (C-V2X) provide new opportunities to solve the connected automated vehicle (CAV) traffic problem for eco-driving at continuous signalized intersections. With C-V2X, a hierarchical velocity optimization design based on hybrid model predictive control technique (HVO-HMPC) is presented to reduce the fuel consumption and pollution emission. First, a distance–domain velocity optimization problem, with distance as the independent variable, was constructed. Second, a hybrid MPC scheme was developed by combining the multiple shooting method and MPC technique to calculate the optimal velocity profile of a high-level controller, which acts as the reference velocity in a low-level controller. Then, a car-following model was built, the low-level controller tracked the reference velocity with the predictive control as the backbone, and the optimal velocity was calculated while ensuring that the safety velocity constraint is satisfied. Next, the proposed HVO-HMPC was tested in Prescan, and the effect comparisons with different control methods in terms of fuel consumption, pollution emission, braking time, and number of braking applications were studied under different driving scenarios. Results show that once the maximal speed is limited to 40 km/h under short-period signals and 20 km/h under long-period signals, the HVO-HMPC effectively reduces fuel consumption by 27.21%, 25.89%, and pollution emissions by 25.3%, 25.97%, respectively, while achieving best performance. Finally, an experimental prototype is built to confirm the validity of the HVO-HMPC.

Dual analysis of stability in plane Poiseuille channel flow with uniform vertical crossflow

In this paper, we investigate the effect of uniform vertical crossflow on the plane Poiseuille channel flow. The derivation and linearization of the Navier–Stokes equations are performed to enable numerical solution through the fourth-order Orr–Sommerfeld equation. The Chebyshev collocation method is employed for this purpose. A dual approach is employed to examine the basic velocity profile, involving both reference velocity analysis (z = 0) and maximum streamwise velocity analysis (z = zmax). The two approaches provide distinct perspectives on the flow and may yield different stability predictions, depending on the values of the parameters used. Modal analysis is conducted to comprehend the asymptotic behavior of the system, achieved through the plotting of eigenspectrum, neutral stability curves, and growth rate curves for disturbances. Accurate values of critical triplets are obtained, aligning with the existing literature. The non-modal analysis is performed to understand the short-term behavior of the system, aided by pseudospectra, evolutionary patterns of energy amplification of the disturbances G(t) over time, and delineation of regions, indicating stability, potential instability, and instability. The collective results from both analyses reveal that the crossflow serves as a dual agent, contributing to both the stabilization and destabilization of the system.

Learning a Tracking Controller for Rolling μbots.

Micron-scale robots (μbots) have recently shown great promise for emerging medical applications. Accurate control of μbots, while critical to their successful deployment, is challenging. In this work, we consider the problem of tracking a reference trajectory using a μbot in the presence of disturbances and uncertainty. The disturbances primarily come from Brownian motion and other environmental phenomena, while the uncertainty originates from errors in the model parameters. We model the μbot as an uncertain unicycle that is controlled by a global magnetic field. To compensate for disturbances and uncertainties, we develop a nonlinear mismatch controller. We define the model mismatch error as the difference between our model's predicted velocity and the actual velocity of the μbot. We employ a Gaussian Process to learn the model mismatch error as a function of the applied control input. Then we use a least-squares minimization to select a control action that minimizes the difference between the actual velocity of the μbot and a reference velocity. We demonstrate the online performance of our joint learning and control algorithm in simulation, where our approach accurately learns the model mismatch and improves tracking performance. We also validate our approach in an experiment and show that certain error metrics are reduced by up to 40%.

Machine learning approach for GNSS geodetic velocity estimation

This study aimed to investigate the performance of machine learning (ML) algorithms in determining horizontal velocity at specific points using the current Global Navigation Satellite System (GNSS) velocity field. To achieve this objective, the analysis utilized the most comprehensive velocity field available for Turkey, where 70% of the GNSS velocities was allocated for training the ML algorithms, while the remaining 30% was used for testing. Contrary to the previous research, the significance of considering the tectonic structure within the study area was emphasized at this point. To determine the tectonic structure of the horizontal velocity field in the region, a preliminary clustering procedure was conducted. Subsequently, distinct ML algorithms were trained using velocity fields associated with different tectonic plates. Moreover, to investigate the impact of the tectonic domain, the entire velocity field was also tested using ML algorithms without considering the tectonic structure. Four different ML algorithms, namely, Gradient Boosting Machines (GBM), LightGBM, Random Forest (RF), and eXtreme Gradient Boosting Machines (XGBoost), were employed to estimate the horizontal velocities (east and north components). The findings imply that incorporating the tectonic structure improved the performance of machine learning predictions, as indicated by the GBM algorithm's decreased root-mean-square error values. In addition, when the tectonic structure was taken into account, the accuracy assessment values for the RF and XGBoost algorithms in the east component decreased significantly. In terms of predicting GNSS velocities, the RF algorithm exhibited the lowest root-mean-square error values compared to other algorithms. The horizontal velocity differences between averages of the reference velocity field and the RF velocity estimates are maximum 0.4 mm/yr.

Path Following for Autonomous Mobile Robots with Deep Reinforcement Learning.

Autonomous mobile robots have become integral to daily life, providing crucial services across diverse domains. This paper focuses on path following, a fundamental technology and critical element in achieving autonomous mobility. Existing methods predominantly address tracking through steering control, neglecting velocity control or relying on path-specific reference velocities, thereby constraining their generality. In this paper, we propose a novel approach that integrates the conventional pure pursuit algorithm with deep reinforcement learning for a nonholonomic mobile robot. Our methodology employs pure pursuit for steering control and utilizes the soft actor-critic algorithm to train a velocity control strategy within randomly generated path environments. Through simulation and experimental validation, our approach exhibits notable advancements in path convergence and adaptive velocity adjustments to accommodate paths with varying curvatures. Furthermore, this method holds the potential for broader applicability to vehicles adhering to nonholonomic constraints beyond the specific model examined in this paper. In summary, our study contributes to the progression of autonomous mobility by harmonizing conventional algorithms with cutting-edge deep reinforcement learning techniques, enhancing the robustness of path following.

Group consensus tracking of second‐order nonlinear multi‐agent systems with time‐varying reference velocities

SummaryThis paper investigates group consensus tracking problems with time‐varying reference velocities. The multiple agents are described as second‐order nonlinear dynamics in directed information exchange settings. For the multi‐agent networks formed by finite agent groups, novel distributed protocols with/without time‐varying transmission delays are designed to solve the group consensus tracking problems. The problems are first transformed into the asymptotical stability of consensus error systems and the input‐to‐state stability of tracking error systems, and then sufficient conditions are derived to guarantee the agents realizing the group consensus tracking based on Lyapunov stability theories. Finally, several simulations are given to verify the effectiveness of the theoretical results.

Multitask control of aerial manipulator robots with dynamic compensation based on numerical methods

This paper presents a control scheme for aerial manipulators which allows to solve different motion problems: end-effector position control, end-effector trajectory tracking control and path-following control. The scheme has two cascaded controllers: i) the first controller is a minimum norm controller based on numerical methods, it solves the three motion control problems just by modifying the controller references. Also, since the aerial manipulator robot is a redundant system, i.e., it has extra degrees of freedom to accomplish the task, it is possible to set other control objectives in a hierarchical order. As a secondary objective of the control it is proposed to maintain a desired configuration for the robotic arm during the task. ii) The second cascade controller is designed to compensate the dynamics of the system which main objective is to drive the velocity errors to zero. The coupled dynamic model of the robotic system (hexarotor and robotic arm) is presented. This model is usually developed as a function of the forces and torques. However, in this work, it is written as a function of reference velocities which are usual references for these vehicles. The proposed control algorithms are given with the corresponding stability and robustness analysis. Finally, to validate the control scheme, experimental tests are performed in a partially structured environment with an aerial manipulator conformed by an aerial platform and a 3DOF robotic arm.

Compilation of a Comprehensive Earthquake Catalog and Relocations in the Caucasus Region

Abstract Instrumental seismic monitoring has a long history in the Caucasus and started in 1899 when the first seismograph was installed in Tbilisi, Georgia. Much of the analog paper records from this time period are preserved in the Tbilisi archives because Georgia served as the regional data center. In the 1990s, due to the collapse of the Soviet Union and the political turmoil in the region, the analog networks and the communication between the newly formed national networks deteriorated. In Georgia, for the next 13 yr, the seismic network coverage was poor until the 2002 Tbilisi earthquake. Following this earthquake, the first permanent digital seismic station in Georgia was established in Tbilisi in 2003. The digital era progressively improved the ability to collect and archive data and today more than a hundred broadband seismic stations (including temporary arrays) are operating in the southern Caucasus. Until recently, the region lacked a coordinated effort to catalog all analog and digital era data collected by different countries into a single repository. As a result of collaboration between Lawrence Livermore National Laboratory, the Ilia State University, and the Republican Seismic Survey Center of Azerbaijan, a comprehensive earthquake catalog was compiled for the Caucasus and neighboring areas as part of a broader probabilistic seismic hazard assessment project. This project digitized Soviet-era paper bulletins, compiled a unified earthquake catalog from regional bulletins, developed 1D reference velocity model, and used it to relocate the events. The final catalog contains 16,963 events with magnitudes 3.7 and above, bringing together all the available data sets in the Caucasus region from 1900 to 2015, significantly improving locations, and generating the most complete earthquake catalog in the region, temporally and geographically.

Direct electromagnetic force control of linear induction motor with end effects using fuzzy controller

In this article is a present dynamic model of a linen induction motor with end effects, occurs when the electromagnetic system of the motor is open. Instead of the angular speed and electromagnetic torque as a rotation motor, in the linear induction motor we have linear speed and linear electromagnetic force, which are sufficiently influenced by the open electromagnetic system of the primary and secondary elements of the linear motor. The choice of an appropriate control system for a linear induction motor is of particular importance considering the use of this type of motor in manufacturing. There are different types of control schemes for standard rotary motors that have their own advantages and disadvantages. When choosing a motor control scheme, we strive to achieve the following parameters, such as smoothness of regulation, ability to regulate in all four quadrants, accuracy of regulation, range of regulation and last but not least, simplicity of regulation. One such method that meets these criteria for rotary motors is the direct torque control method with space vector modulation. The control system of the linear induction motor proposed in this paper is based on this method, taking into account the open electromagnetic system and the occurrence of the end effects of the linear induction motor. Instead of the angular reference velocity as a rotary motor, in this case we have a linear reference velocity to the input, which is compared to the current linear velocity of the motor. The present paper also uses the vector modulation method by analogy with direct torque control with space vector modulation of the rotary motor. For the conversion of a linear velocity to a linear electromagnetic force, instead of a standard PI controller, it is using a fuzzy controller to convert a linear velocity to a reference electromagnetic force.

MPC Path Following with Macroscopic Shape Adjustment for AIAUVs

This paper treats the problem of adjusting the macroscopic shape of an articulated robotic system following a path, such that the shape fits the path curve as closely as possible. We propose a way of quantifying the fit of the robot's shape to the path through the errors of the individual links, and investigate a kinematic MPC solution to the problem. The MPC generates position and velocity reference trajectories, which respect the constraints of the system, to be fed to a low-level controller. The scheme is evaluated in a kinematic level simulation study with constant irrotational ocean currents.

Study of the origin and correction of compass measurement errors in Doppler current meters

Surface and subsurface currents are two of the Essential Climate Variables (ECVs) defined by the Global Climate Observing System (GCOS). In situ current measurements can be made by Eulerian methods with instruments on moorings fixed in space. These methods require the determination of two metrological quantities: the speed and the direction of the motion. Their measurement and calibration require the determination of reference velocities and the measure of the angular movement of seawater in relation to the measuring device, as well as of the measuring device in relation to a reference direction given by the magnetic North. This reference direction is determined by electronic compasses integrated into current meters and current profilers. Compasses are sensitive to their magnetic environment, and, therefore, to the objects and instruments that surround them. This publication describes experiments conducted with current meters and current profilers to measure the influence of different devices on the accuracy of their compass measurements. It gives some explanations about the origin of measurement errors and proposes solutions to correct or attenuate the defaults in direction measurements and the measured deviations. Correction formulas are given that can be applied to measured data. They allow the reduction of errors of several tens of degrees for data to be within the instrument’s specifications.

Distributed parameter identification for the Navier–Stokes equations for obstacle detection

We present a parameter identification problem for a scalar permeability field and the maximum velocity in an inflow, following a reference profile. We utilize a modified version of the Navier–Stokes equations, incorporating a permeability term described by the Brinkman’s Law into the momentum equation. This modification takes into account the presence of obstacles on some parts of the boundary. For the outflow, we implement a directional do-nothing condition as a means of stabilizing the backflow. This work extends our previous research published in (Aguayo et al 2021 Inverse Problems 37 025010), where we considered a similar inverse problem for a linear Oseen model with do-nothing boundary conditions on the outlet and numerical simulations in 2D. Here we consider the more realistic case of Navier–Stokes equations with a backflow correction on the outflow and 3D simulations of the identification of a more realistic tricuspid cardiac valve. From a reference velocity that could have some noise or be obtained in low resolution, we define a suitable quadratic cost functional with some regularization terms. Existence of minimizers and first and second order optimality conditions are derived through the differentiability of the solutions of the Navier–Stokes equations with respect to the permeability and maximum velocity in the inflow. Finally, we present some synthetic numerical test based of recovering a 2D and 3D shape of a cardiac valve from total and local velocity measurements, inspired from 2D and 3D MRI.

A High-Precision Continuous Scan and Step Scan System for Compact Spectrometer Applications

In this article, a dual-stage positioning system for precisely tracking continuous scan and step scan profiles to meet the high-precision dynamic positioning requirements of space optical instruments is reported. A piezoelectric walking stage provides a long-stroke platform to which a short-stroke voice coil-actuated shuttle is mounted. The shuttle corrects for submicrometer deviations in the walking stage position and is designed with a low resonant frequency to passively reject disturbances produced by the walking stage. Both the walking stage and shuttle are instrumented with high-resolution interferometric encoders and a feedback system regulates the shuttle's position to achieve less than 7 nm RMS position error for constant velocity references of up to 0.5 mm/s. The shuttle is also capable of tracking step scan profiles with 10 ms settling times and 1.1 nm RMS position errors for steps up to 500 nm.

Measurement Method for the Flight Parameters of a Small Flying Object Using a Multi-Dimensional LED Detection Array

This article proposes a measurement method using a multi-dimensional LED detection array, which can be used to obtain the flight parameters (i.e., impact coordinates, flight velocity, and incident angle) of a small flying object. Firstly, the composition of the proposed system and its detection principle are described in detail. Then, a calculation model is derived according to the geometrical relationship between the different LED detection arrays, which can calculate the above flight parameters. Furthermore, numerical simulations are performed to analyze the change trend in the measurement error of the proposed system, and it can be verified that its measurement performance meets the related requirements in theory. Finally, we use the proposed system and a high-speed camera system to carry out comparison experiments with two different reference velocities of 900 m/s and 700 m/s, and the results show that the maximum deviation in the measured absolute velocity is always less than 1 m/s and that the maximum deviations of the measured coordinates of x and y are not more than 2 mm within the effective measurement range. Therefore, the proposed measurement method is feasible and effective, and it can also meet the requirements of the measurement system.