Velocity Tracking Research Articles

Networked Predictive Trajectory Tracking Control for Underactuated USV with Time-Varying Delays

This study explores the control framework for the trajectory tracking problem concerning unmanned surface vessels (USVs) in the presence of time-varying communication delays. To address the aforementioned problem, a novel networked predictive sliding mode control architecture is proposed by integrating a discrete sliding mode control technique and predictive control scheme. By leveraging a first-order forward Euler discretization approach, a discrete-time model of USVs was initially formulated. Then, a virtual velocity controller was developed to convert the position tracking into expected velocity tracking, which was achieved by utilizing a sliding mode control. Subsequently, a networked predictive control technique was performed to compensate for the time-varying delays. Finally, theoretical analysis and extensive comparative simulation tests demonstrated that the proposed control scheme guaranteed complete compensation for time-varying delays while ensuring the stability of the closed-loop system.

Analysis and Optimisation of Obstacle‐Crossing Performance of Electric Shovel Based on DEM‐MBD Coupling Method

ABSTRACTTo study and enhance the obstacle‐crossing performance of the electric shovel, an obstacle‐crossing model that employs a coupling methodology integrating the discrete element method (DEM) and multi‐body dynamics (MBD) is constructed. Secondly, the influence of grouser height (GH), track velocity (TV), slope inclination (SI) and slope height (SH) on obstacle‐crossing performance is investigated through DEM‐MBD simulation, with the objective of obtaining an obstacle‐crossing surrogate model through the Kriging method and Box‐Behnken experimental design. On this basis, two optimisation solutions for the obstacle‐crossing performance of the electric shovel are proposed based on a genetic algorithm (GA), and the corresponding obstacle‐crossing performances are analysed. The results demonstrate that the coupling effect between SI and SH exerts a considerable influence on the ground pressure coefficient (GPC), power and disturbance potential energy (DPE). When the optimal TV and GH are set at 0.1 m/s and 9.38 mm, the GPC, power and disturbance kinetic energy (DKE) are observed to diminish to varying degrees, thereby indicating that the obstacle‐crossing performance of the electric shovel has been enhanced.

Sliding Mode Control Approach for Vision-Based High-Precision Unmanned Aerial Vehicle Landing System Under Disturbances

Unmanned aerial vehicles (UAVs) face significant challenges when landing on moving targets due to disturbances, such as wind, that affect landing precision. This study develops a system that leverages global navigation satellite system (GNSS) signals and UAV visual data to enable real-time precision landings, and incorporates a sliding mode controller (SMC) to mitigate external disturbances throughout the landing process. To this end, a reference-model-based SMC is proposed, which defines reference values for each state to enhance the steadiness and safety of the velocity control system, thereby improving velocity state tracking and accuracy. The stability of the proposed controller is demonstrated using the Lyapunov method and comparing its performance against other controllers, including backstepping, linear-quadratic regulator (LQR), and proportional–integral–derivative (PID). The experimental results reveal a 75% reduction in maximum velocity tracking error and an 80% reduction in maximum landing error with the proposed controller. Finally, extensive real-flight tests confirm the stability and feasibility of the system.

Laser Preparation and Underwater Drag-Reduction Performance of Secondary Fractal–V Groove Composite Structures on the Surface of Equal-Diameter Revolution Bodies

The reduction of drag for both aircraft and underwater equipment has the potential to reduce their overall energy consumption. Consequently, research into the drag-reducing performance of metal surfaces has significant practical applications. However, there has been more research on the machining of grooves on flat surfaces and inside tubes and less research on the structure of drag-reducing grooves on the outside of circular rods. This paper presents a study in which laser etching technology is employed to machine a range of secondary fractal topologies and V-groove composite structures on the surface of equal-diameter stainless-steel bodies of revolution. The influence of different parameters on the surface properties of stainless-steel materials is analysed through the use of auxiliary positioning tools, adjustments to laser processing parameters and scanning path schemes, as well as the characterisation of the surface morphology of the processed stainless steel using super-depth microscopy, scanning electron microscopy, and other techniques. Subsequently, an underwater drag-reduction tester is employed to assess the drag-reduction efficacy of the optimised secondary fractal composite structure on the surface of the stainless-steel equal-diameter body of revolution. Subsequently, particle image velocity (PIV) tracking technology is employed to assess the surface flow field velocity and overall velocity average of the secondary fractal composite structure. The findings indicate that the secondary fractal composite structure exhibited a drag-reduction effect on the surface of the stainless-steel body of revolution only when the primary main groove had a width of 0.1 mm. Furthermore, an increase in the Reynolds number Re within the range of 4000 to 7000 resulted in a notable enhancement in the drag-reduction efficacy of the secondary fractal composite structure on the surface of the stainless-steel body of revolution. At Re values of 5000, 6000, and 7000, the corresponding drag-reduction rates were observed to be 5.15%, 5.28%, and 5.40%, respectively.

DSiam-CnK: A CBAM- and KCF-Enabled Deep Siamese Region Proposal Network for Human Tracking in Dynamic and Occluded Scenes.

Despite the accuracy and robustness attained in the field of object tracking, algorithms based on Siamese neural networks often over-rely on information from the initial frame, neglecting necessary updates to the template; furthermore, in prolonged tracking situations, such methodologies encounter challenges in efficiently addressing issues such as complete occlusion or instances where the target exits the frame. To tackle these issues, this study enhances the SiamRPN algorithm by integrating the convolutional block attention module (CBAM), which enhances spatial channel attention. Additionally, it integrates the kernelized correlation filters (KCFs) for enhanced feature template representation. Building on this, we present DSiam-CnK, a Siamese neural network with dynamic template updating capabilities, facilitating adaptive adjustments in tracking strategy. The proposed algorithm is tailored to elevate the Siamese neural network's accuracy and robustness for prolonged tracking, all the while preserving its tracking velocity. In our research, we assessed the performance on the OTB2015, VOT2018, and LaSOT datasets. Our method, when benchmarked against established trackers, including SiamRPN on OTB2015, achieved a success rate of 92.1% and a precision rate of 90.9%. On the VOT2018 dataset, it excelled, with a VOT-A (accuracy) of 46.7%, a VOT-R (robustness) of 135.3%, and a VOT-EAO (expected average overlap) of 26.4%, leading in all categories. On the LaSOT dataset, it achieved a precision of 35.3%, a normalized precision of 34.4%, and a success rate of 39%. The findings demonstrate enhanced precision in tracking performance and a notable increase in robustness with our method.

Highly maneuvrable quadrotor unmanned aerial vehicle control with unknown disturbances

For the problems of highly maneuverable quadrotor unmanned aerial vehicle (UAV), such as drastic attitude changes, unknown model disturbances, and accurate velocity tracks, the paper proposes new model compensation control based on SO(3) according to the idea of model compensation control (MCC) theory and SO(3) theory. Accurate estimation for unknown disturbances and precise extraction of the reference signal are achieved by introducing compensation function observer and high-order differentiator. For the attitude solution, the desired attitude solutions based on acceleration are designed, which guarantees the range of accelerations is safely limited. At the same time, the relationship between attitude control, velocity control, and the desired attitude solutions is given. The proposed method not only has the global stability of SO(3) and the anti-disturbance stability of MCC but also improves the maneuverability of UAV. Through verifications of simulations and experiments, the proposed method not only has no loss of anti-disturbance performance but also significantly reduces overshooting and response time compared to traditional MCC methods. Providing a control strategy for highly maneuverable UAV.

Modelling of Mass Transport in Fractured Crystalline Rock Using Velocity Interpolation and Cell-Jump Particle Tracking Methods

In this study, two particle tracking methods, velocity interpolation, and cell-jump, were employed to simulate tracer transport in fractured crystalline rock. The models, belonging to DECOVALEX-2023 Task F1, included one considering only the influence of deterministic (major) fractures, and another considering both deterministic and stochastic (background) fractures. The simulations involved converting fracture properties into equivalent hydraulic parameters for each three-dimensional grid, simulating steady-state flow fields, and evaluating transport parameters using particle tracking methods. Using transport parameters, one-dimensional transport pathways were simulated for evaluating mass transport of tracers considering non-reactive, decay, and adsorption. Moment analysis was then utilized to quantify breakthrough curves and compare the performance of the two particle tracking methods. The conclusion is that the cell-jump method, despite facing issues with numerical dispersion that results in a broader distribution of particle trajectories, demonstrates advantages in providing relative shorter mean breakthrough times and less temporal spreading compared to the velocity interpolation (VI) method in cases involving stochastic background fractures. Both methods are limited by the issue of particles entering the matrix due to the application of non-zero permeability for numerical convenience.

Double-level prescribed performance control for rigid spacecraft attitude tracking under actuator faults

The problem of prescribed performance attitude tracking control for rigid spacecraft with external disturbance, inertia uncertainties and actuator faults on special orthogonal group SO(3) is investigated in this paper. With the aid of a novel Lyapunov function, a double-level prescribed performance controller is devised to ensure both attitude and angular velocity tracking errors converge within preset performance boundaries. By applying the suggested controller, the setting time, overshoot and steady-state error of both attitude and angular velocity tracking errors can be regulated by preset performance boundaries getting rid of the initial conditions of the control system. Distinguished from the exist prescribed performance controllers, a stricter performance boundary for angular velocity tracking error can be achieved. For the lumped disturbance which stems from the unknown external disturbance, inertia uncertainties and actuator faults, an extended state observer with linear feedback functions is initially designed to provide an estimation with simple structure. Rigorous proofs within a Lyapunov framework and comparison simulations are presented to demonstrate the effectiveness and superiority of the suggested control strategy.

Enhanced Safety‐Critical Control of Hypersonic Vehicles With Multiple Constraints

ABSTRACTThis paper proposes an enhanced safety‐critical control scheme for hypersonic vehicles (HSVs) with multiple constraints, where both safety constraints (e.g., actuator saturations and time‐varying angle‐of‐attack [AOA] limitation) and transient performance constraints are considered. In this scheme, the extended exponential control barrier function (EECBF) is constructed by incorporating extended states and robust terms to deal with time‐varying constraint boundaries and uncertainties simultaneously. An adaptive programming for optimal transient performance envelopes is conducted to make a trade‐off between safety and transient performance constraints. It is shown that the proposed scheme for HSVs can confine AOA to time‐varying safety boundaries in the presence of uncertainties while the velocity and height tracking errors can always maintain within the desired transient performance envelopes. Simulations and comparisons are provided to verify the effectiveness of our scheme.

The velocity tracking problem for Navier–Stokes equations with pointwise-integral control constraints in time-space

In this work, we study the velocity tracking control problem of the evolutionary two-dimensional Navier–Stokes system. The regularizing Tikhonov term is not involved in the cost functional and pointwise-integral control constraints in time-space are considered. First- and second-order optimality conditions are established using a suitable extended cone for the sufficient ones. We also address the numerical approximation of the control problem, proving the convergence of the discrete problems and establishing error estimates between the optimal discrete and continuous states.

Adaptive active disturbance rejection control for velocity tracking trajectory tasks

This work reports a Proportional plus Adaptive Desired Disturbance Observer (P+ADDOB) controller for a perturbed servo system aimed to track time-varying references. The controller is composed of three parts. The first part is an adaptive feedforward term, the second part corresponds to a proportional action, and a term aimed to compensate the disturbances affecting the closed-loop system corresponds to the third part. The structure of the ADDOB is inspired in the Ohnishi Disturbance Observer, however, instead resorting on measurements of the velocity and acceleration, the ADDOB employs their desired values thus reducing the impact of measurement noise in the closed loop system. The ADDOB is computed using the servo system parameter estimates obtained by means of a gradient algorithm endowed with a parameter projection mechanism. A stability analysis allows concluding boundedness of all the closed-loop signals. Real-time experiments assess the effectiveness of the proposed controller.

Characterizing surface pressure and wind fields of typhoons approaching Hong Kong

In this paper, 17 severe typhoons that have affected Hong Kong are simulated using an advanced numerical atmospheric simulation system - Weather Research and Forecasting model (WRF). The simulated surface pressure and wind fields of these typhoons are validated against a wide range of field observations. Then azimuth-dependent models for the radius of maximum winds and the Holland parameter are established statistically at the surface level. It is observed that the shape parameter of the Holland pressure model is smaller at the surface than that at the gradient level. And the Holland wind field model cannot well reproduce the simulated radial wind profiles due to the complexities of nonuniform surface conditions and typhoon dynamics. It is found that the modified Rankine model provides satisfactory estimates of typhoon wind speeds in Hong Kong. Additionally, wind field asymmetries of typhoons approaching Hong Kong are highly correlated with the typhoon track velocity, vertical wind shear and the angle between them. The proposed statistical models and identified characteristics of wind field asymmetries of typhoons will provide useful information for rapidly assessing typhoon wind hazards.

A study of multi-model identification and fusion control algorithms for rotary limit devices

In order to improve the control performance of rotating limit device on the motion range of rotating parts of mechanical equipment, the multi model identification and fusion control algorithm of rotating limit device is studied. The angle sensor and speed sensor are used to collect the rotation angle and speed of rotating machinery equipment. Taking the collected data as the input of multiple support vector machines, the D-S evidence theory is selected to fuse the status of rotating machinery equipment identified by multiple models and determine the final status of rotating machinery equipment. Set the deviation between the target angle and the actual angle of the rotating mechanical equipment as the input of the PID controller. The PID controller determines the limit control quantity of the rotating limit device to the rotating machinery equipment, and realizes the rotating limit control of the rotating machinery equipment. The rotary table is selected as the mechanical equipment controlled by the rotary limit device. The experimental results show that the rotary limit device can control the rotation of the rotary table in the ideal angle range, achieve smooth tracking of the rotation angular velocity, and has excellent control performance.

WITHDRAWN: Surging dynamics of unnamed glaciers at the Abi Gamin Peak in the central Himalayas

Surge-type glaciers in the Himalayas have been extensively documented, however, understanding of the mechanisms behind glacier surges remain limited. Herein, based on multi-source remote sensing data, we systematically investigated the characteristics and subglacial processes of the unnamed glacier at Abi Gamin Peak in central Himalayas. Our approach integrated optical stereo photogrammetry, feature tracking, visual interpretation, and glacier velocity decomposition. Our study reveals that the glacier surge was initiated in August 2019 and lasted for less than six months, with rapid acceleration and deceleration phases. During the surge, the glacier transported approximately 0.233 km3 of mass from higher to lower elevations, leading to a thickness increase of >70 m at the glacier terminus. The glacier terminus advanced by >800 m, accompanied by significant expansion of the crevasses. Furthermore, we quantitatively revealed the evolution of basal stresses, strain rates, and sliding velocities during the surge, indirectly characterising the influence of water on it. We contend that the surge in this glacier was primarily driven by subglacial sliding induced by glacier surface meltwater. Significant changes in regional climate serve as external factors that perturb the glacial dynamic equilibrium. The observed characteristics of the unnamed glacier at Abi Gamin Peak indicate that its surge was controlled by a hydrological switch. Notably, our work contributes to an enhanced understanding of glacier surge mechanisms in High Mountain Asia.

Testing the volume integrals of travel-time sensitivity kernels for flows

Context. Helioseismic inversions largely rely on sensitivity kernels, in which 3D spatial functions describe how the changes in the solar interior translate into the change in helioseismic observables. These sensitivity kernels in most cases come from the forward modelling that is used in the most advanced solar models. Aims. We aim to test the sensitivity kernels by comparing their volume integrals with measured values from helioseismic travel times. Methods. By manipulating the tracking rate, we mimicked the additional zonal velocity in the Dopplergram datacubes. These datacubes were then processed by a standard travel-time measurements pipeline. We investigated the dependence of the east-west travel time averaged over a box around the disc centre on the implanted tracking velocity. The slope of this dependence is directly proportional to the total volume integral of the sensitivity kernel that corresponds to the travel-time geometry that is used. Results. The agreement between measurements and models for travel times that are computed with a ridge filtering is very good to acceptable. The dependence we sought to determine indeed resembles a linear function, and its slope agrees with the expected volume integral from the forward-modelled sensitivity kernel. The agreement is poorer for the phase-speed filtered datacubes. The disagreement is particularly strong for the slowest phase speeds (filters td1–td4). For the higher phase speeds, our result indicates that the measured kernel integrals are systematically larger than expected from the forward modelling. We admit our testing procedure may not be appropriate for high phase speeds and higher radial modes.

A Deep Reinforcement Learning Approach to Injection Speed Control in Injection Molding Machines with Servomotor-Driven Constant Pump Hydraulic System

The control of the injection speed in hydraulic injection molding machines is critical to product quality and production efficiency. This paper analyzes servomotor-driven constant pump hydraulic systems in injection molding machines to achieve optimal tracking control of the injection speed. We propose an efficient reinforcement learning (RL)-based approach to achieve fast tracking control of the injection speed within predefined time constraints. First, we construct a precise Markov decision process model that defines the state space, action space, and reward function. Then, we establish a tracking strategy using the Deep Deterministic Policy Gradient RL method, which allows the controller to learn optimal policies by interacting with the environment. Careful attention is also paid to the network architecture and the definition of states/actions to ensure the effectiveness of the proposed method. Extensive numerical results validate the proposed approach and demonstrate accurate and efficient tracking of the injection velocity. The controller’s ability to learn and adapt in real time provides a significant advantage over the traditional Proportion Integration Differentiation controller. The proposed method provides a practical solution to the challenge of maintaining accurate control of the injection speed in the manufacturing process.

Accounting for power take-off efficiency in optimal velocity tracking control of wave energy converters

There has been much research into how Wave Energy Converters (WECs) can capture energy from the waves and convert it into kinetic energy in the mechanical system (mechanical energy). However, because WECs typically generate power at high force and with low speed, the conversion of the mechanical energy into electrical energy can be inefficient. If Power Take-Off (PTO) efficiency is not accounted for in controller design then the efficiency of the WEC from wave energy to electrical energy can be poor. In this work, an adaptation of Optimal Velocity Tracking (OVT) control is presented, termed OVT-E, that accounts for PTO efficiencies in the controller formulation such that it converts more energy from the waves to electricity than traditional OVT approaches, termed OVT-M. Results show that, for an example WEC and PTO system in a simulation environment, OVT-E produces more efficient electrical energy conversion than OVT-M with lower forces through the drive-train. OVT-E is also shown to convert more electrical energy than an adaptive linear damping method. The methodology for designing OVT-E could have further applications when coupled with other controller implementations or if used alongside machine-learning for control co-design purposes.

Model predictive control based integrated fault tolerant controller for autonomous vehicles with four-wheel independent steering, driving and braking systems

This paper presents a fault-tolerant control algorithm for path and velocity tracking of an autonomous vehicle based on four-wheel independent steering, driving, and braking systems. The proposed algorithm is designed to distribute the control effort to normal actuators in the occurrence of faults. The proposed algorithm consists of three modules. The reference velocity and yaw rate to track the reference path are determined by the reference state decision module. The model predictive control (MPC)-based fault-tolerant controller determines the optimal inputs to compensate for the effect of the faulty actuator and maintain the tracking performance. A faulty-factor estimator is proposed based on a recursive covariance estimator to evaluate the performance degradation due to the failure of each actuator. The estimated faulty factors are applied to the objective function of the MPC such that the control input is distributed based on the level of the fault. The performance of the proposed algorithm was verified via performing simulations using MATLAB/Simulink and CarSim. The simulations proved that the proposed controller maintained the control performance of the normal case even in multiple actuator failures.

KF-PEV: a causal Kalman filter-based particle event velocimetry

Event-based pixel sensors asynchronously report changes in log-intensity in microsecond-order resolution. Its exceptional speed, cost effectiveness, and sparse event stream make it an attractive imaging modality for particle tracking velocimetry. In this work, we propose a causal Kalman filter-based particle event velocimetry (KF-PEV). Using the Kalman filter model to track the events generated by the particles seeded in the flow medium, KF-PEV yields the linear least squares estimate of the particle track velocities corresponding to the flow vector field. KF-PEV processes events in a computationally efficient and streaming manner (i.e., causal and iteratively updating). Our simulation-based benchmarking study with synthetic particle event data confirms that the proposed KF-PEV outperforms the conventional frame-based particle image/tracking velocimetry as well as the state-of-the-art event-based particle velocimetry methods. In a real-world water tunnel event-based sensor data experiment performed on what we believe to be the widest field view ever reported, KF-PEV accurately predicted the expected flow field of the SD7003 wing, including details such as the lower velocity in the wake and the flow separation around the underside of an angled wing.

Motion/force coordinated trajectory tracking control of nonholonomic wheeled mobile robot via LMPC-AISMC strategy

Nonholonomic constrained wheeled mobile robot (WMR) trajectory tracking requires the enhancement of the ground adaptation capability of the WMR while ensuring its attitude tracking accuracy, a novel dual closed-loop control structure is developed to implement this motion/force coordinated control objective in this paper. Firstly, the outer-loop motion controller is presented using Laguerre functions modified model predictive control (LMPC). Optimised solution condition is introduced to reduce the number of LMPC solutions. Secondly, an inner-loop force controller based on adaptive integral sliding mode control (AISMC) is constructed to ensure the desired velocity tracking and output driving torques by combining second-order nonlinear extended state observer (ESO) with the estimation of dynamic uncertainties and external disturbances during WMR travelling process. Then, Lyapunov stability theory is utilised to guarantee the consistent final boundedness of the designed controller. Finally, the system is numerically simulated and practically verified. The results show that the double-closed-loop control strategy devised in this paper has better control performance in terms of complex trajectory tracking accuracy, system resistance to strong interference and computational timeliness, and is able to realise effective coordinated control of WMR motion/force.