Speed Tracking Research Articles

Particle Filter Based on Harris Hawks Optimization Algorithm for Underwater Visual Tracking

Due to the complexity of the underwater environment, tracking underwater targets via traditional particle filters is a challenging task. To resolve the problem that the tracking accuracy of a traditional particle filter is low due to the sample impoverishment caused by resampling, in this paper, a new tracking algorithm using Harris-hawks-optimized particle filters (HHOPF) is proposed. At the same time, the problem of particle filter underwater target feature construction and underwater target scale transformation is addressed, the corrected background-weighted histogram method is introduced into underwater target feature recognition, and the scale filter is combined to realize target scaling transformation during tracking. In addition, to enhance the computational speed of underwater target tracking, this paper constructs a nonlinear escape energy using the Harris hawks algorithm in order to balance the exploration and exploitation processes. Based on the proposed HHOPF tracker, we performed detection and evaluation using the Underwater Object Tracking (UOT100) vision database. The proposed method is compared with evolution-based tracking algorithms and particle filters, as well as with recent tracker-based correlation filters and some other state-of-the-art tracking methods. By comparing the results of tracking using the test data sets, it is determined that the presented algorithm improves the overlap accuracy and tracking accuracy by 11% compared with other algorithms. The experiments demonstrate that the presented HHOPF visual tracking provides better tracking results.

Active-Feedback 3D Single-Molecule Tracking Using a Fast-Responding Galvo Scanning Mirror.

Real-time three-dimensional single-particle tracking (RT-3D-SPT) allows continuous detection of individual freely diffusing objects with high spatiotemporal precision by applying closed-loop active feedback in an optical microscope. However, the current tracking speed in RT-3D-SPT is primarily limited by the response time of the control actuators, impeding long-term observation of fast diffusive objects such as single molecules. Here, we present an RT-3D-SPT system with improved tracking performance by replacing the XY piezoelectric stage with a galvo scanning mirror with an approximately 5 times faster response rate (∼5 kHz). Based on the previously developed 3D single-molecule active real-time tracking (3D-SMART), this new implementation with a fast-responding galvo mirror eliminates the mechanical movement of the sample and allows a more rapid response to particle motion. The improved tracking performance of the galvo mirror-based implementation is verified through simulation and proof-of-principle experiments. Fluorescent nanoparticles and ∼1 kB double-stranded DNA molecules were tracked via both the original piezoelectric stage and new galvo mirror implementations. With the new galvo-based implementation, notable increases in tracking duration, localization precision, and the degree to which the objects are locked to the center of the detection volume were observed. These results suggest that faster control response elements can expand RT-3D-SPT to a broader range of chemical and biological systems.

A Comprehensive Review of Recent Maximum Power Point Tracking Techniques for Photovoltaic Systems under Partial Shading

To operate photovoltaic (PV) systems efficiently, the maximum available power should always be extracted. However, due to rapidly varying environmental conditions such as irradiation, temperature, and shading, determining the maximum available power is a time-varying problem. To extract the maximum available power and track the optimal power point under these varying environmental conditions, maximum power point tracking (MPPT) techniques are proposed. The application of MPPT for extracting maximum power plays a crucial role in developing efficient PV systems. These MPPT techniques face several issues and limitations, particularly during partial shading conditions caused by non-uniform environmental conditions. Researchers have been focusing more on mitigating the partial shading condition in PV systems for the last few years due to the need to improve power output and efficiency. This paper provides an overview of MPPTs proposed in the literature for uniform and non-uniform environmental conditions broadly categorized as MPPT-based and circuit-based methods. The MPPT-based methods are classified as conventional, soft computing, and hybrid techniques. A critical analysis of each approach regarding tracking speed, algorithm complexity, and dynamic tracking under partial shading is discussed. The literature shows hybrid strategies provide fast-tracking speed and are efficient with a tracking efficiency of around 99% compared to conventional methods; however, their design and practical implementation are complex. This comprehensive review of MPPT methods aims to provide power utilities and researchers with a reference and guideline to select the best MPPT method for normal operation and partially shaded PV systems based on their effectiveness and economic feasibility.

Adaptive energy harvesting approach for smart wearables towards human-induced stochastic oscillations

Harvesting diverse renewable energy sources from humans offers an effective approach to achieving cleaner production and sustainable development. The integration of self-powered smart wearables enables continuous power supply for long-term operation of outdoor civilian and military electronics, especially in situations with limited access to conventional power sources. Resonant energy harvesting schemes working at the dominant frequencies of human movement can trigger large mechanical oscillations and significantly enhance the generation of electrical power. This study presents the development of a nonlinear two-degree-of-freedom (2DOF) resonant energy harvesting system with a maximum power point tracking (MPPT) algorithm that adapts to individual human differences and stochastic motion patterns. Through an analysis of the system characteristics based on equivalent circuit modeling, it is found that the electrical damping of the harvester plays a crucial role in determining the actual power output, and the relationship between its optimal value and the oscillation frequency is unique. Thereby, an energy harvesting circuit with an adaptive MPPT algorithm is developed to optimize the electrical damping in real time by applying a novel Perturb and Observe technique based on the average power over each time period. In addition, a variable-step search method and a special convergence judgment are proposed to enhance the tracking speed. The sensitivity analysis of algorithm parameters indicates that the time period and power tolerance need to be reasonably selected to strike a balance between search speed and accuracy. The results of constant motion pattern tests show that the proposed algorithm with a variable-step search method exhibits superior search speed and accuracy over fixed-step configurations. The results of variable motion pattern tests reveal an increase in the actual system power output of up to 194% compared to the conventional fractional open-circuit voltage (FOCV) algorithm. Finally, the system adaptability test is conducted to validate the MPPT efficiency in four resonant systems. The results showcase an impressive MPPT efficiency of 93%–99%, demonstrating its reliability and robustness in various application scenarios.

Nonlinear speed-tracking control with overcurrent protection for uncertain permanent magnet synchronous motor systems

For the speed-tracking control of permanent magnet synchronous motor (PMSM) systems, fast dynamic response and high reference speed require a large transient current to provide sufficient electromagnetic torque. However, the excessive transient current may damage the drive circuit and threaten the system safety. Besides, it is also important to pay attention to the influence of multiple disturbances including system parameter uncertainties and unknown load torque variation. To solve these problems, we propose a nonlinear speed-tracking control approach for uncertain PMSM systems. More specifically, based on the cascade control structure and by introducing auxiliary smooth function, we first develop a nonlinear current-constrained controller (CCC) to guarantee the speed tracking, disturbance rejection, and overcurrent protection simultaneously. In order to improve the robustness of the desired performances, we further implement extended state observer (ESO) techniques to propose an ESO-based CCC (ESO-CCC). Finally, comparative experiments are implemented in a 5.5 kW PMSM platform to verify the performance of the proposed two controllers.

HDT network: A high‐resolution range profile multi‐target detection and tracking method based on neural network

AbstractIn radar target detection and tracking tasks, the detection algorithm and data association algorithm are the primary technologies. The accuracy of detection, stability of tracking and processing speed are the key points to accomplishing effective high‐resolution range profile (HRRP) multi‐target tracking (MTT). Classic HRRP target tracking algorithms conduct the detection by extracting handcraft features and conduct tracking based on the data association algorithm. With the development of neural network methods, deep learning methods have been widely applied in target detection and tracking. An HRRP detection and tracking (HDT) network based on neural networks is proposed, which includes three steps: Firstly, the HRRP signal is processed by a convolutional neural network detector to extract the feature of the origin signal and generate the detection measurement. Secondly, a predicted measurement is generated by a Kalman filter, which estimates the current state of the target based on its previous states and the detection measurement. Finally, the ReID network calculates the cosine similarity between the tracks and measurements to associate them and a linear sum assignment operation is used to match the tracks and measurements. By using the proposed algorithm, the HDT network is capable of detecting and tracking multiple targets in complex cluttered environments with high accuracy. Experimental results on an HRRP dataset collected by a moving radar platform show the outstanding performance of the HDT network, including detection accuracy, track integrity and real‐time MTT.

Familiarization with Mixed Reality for Individuals with Autism Spectrum Disorder: An Eye Tracking Study.

Mixed Reality (MR) technology is experiencing significant growth in the industrial and healthcare sectors. The headset HoloLens 2 displays virtual objects (in the form of holograms) in the user's environment in real-time. Individuals with Autism Spectrum Disorder (ASD) exhibit, according to the DSM-5, persistent deficits in communication and social interaction, as well as a different sensitivity compared to neurotypical (NT) individuals. This study aims to propose a method for familiarizing eleven individuals with severe ASD with the HoloLens 2 headset and the use of MR technology through a tutorial. The secondary objective is to obtain quantitative learning indicators in MR, such as execution speed and eye tracking (ET), by comparing individuals with ASD to neurotypical individuals. We observed that 81.81% of individuals with ASD successfully familiarized themselves with MR after several sessions. Furthermore, the visual activity of individuals with ASD did not differ from that of neurotypical individuals when they successfully familiarized themselves. This study thus offers new perspectives on skill acquisition indicators useful for supporting neurodevelopmental disorders. It contributes to a better understanding of the neural mechanisms underlying learning in MR for individuals with ASD.

Real-time myocardial landmark tracking for MRI-guided cardiac radio-ablation using Gaussian Processes

Objective. The high speed of cardiorespiratory motion introduces a unique challenge for cardiac stereotactic radio-ablation (STAR) treatments with the MR-linac. Such treatments require tracking myocardial landmarks with a maximum latency of 100 ms, which includes the acquisition of the required data. The aim of this study is to present a new method that allows to track myocardial landmarks from few readouts of MRI data, thereby achieving a latency sufficient for STAR treatments. Approach. We present a tracking framework that requires only few readouts of k-space data as input, which can be acquired at least an order of magnitude faster than MR-images. Combined with the real-time tracking speed of a probabilistic machine learning framework called Gaussian Processes, this allows to track myocardial landmarks with a sufficiently low latency for cardiac STAR guidance, including both the acquisition of required data, and the tracking inference. Main results. The framework is demonstrated in 2D on a motion phantom, and in vivo on volunteers and a ventricular tachycardia (arrhythmia) patient. Moreover, the feasibility of an extension to 3D was demonstrated by in silico 3D experiments with a digital motion phantom. The framework was compared with template matching—a reference, image-based, method—and linear regression methods. Results indicate an order of magnitude lower total latency (<10 ms) for the proposed framework in comparison with alternative methods. The root-mean-square-distances and mean end-point-distance with the reference tracking method was less than 0.8 mm for all experiments, showing excellent (sub-voxel) agreement. Significance. The high accuracy in combination with a total latency of less than 10 ms—including data acquisition and processing—make the proposed method a suitable candidate for tracking during STAR treatments. Additionally, the probabilistic nature of the Gaussian Processes also gives access to real-time prediction uncertainties, which could prove useful for real-time quality assurance during treatments.

Additive-state-decomposition-based model predictive tracking control for PMSM servo system with multiple disturbances

This paper presents an additive-state-decomposition-based model predictive tracking control and disturbance rejection method for a permanent magnet synchronous motor (PMSM) servo system subject to unknown parameter perturbations, unmodeled dynamics, and time-varying load torque. The basic idea of this method is to equivalently decompose the original system into a primary system for handling the tracking control subproblem and a secondary system for dealing with the robust stabilization subproblem. A model predictive controller is designed for the primary system to achieve high-accuracy tracking of the reference speed. As for the secondary system, a novel high-order generalized extended state observer (HGESO) is constructed to estimate the multiple disturbances simultaneously, and a state feedback control law incorporating a disturbance compensator is developed to eliminate the adverse effect of the multiple disturbances on the system output. By combining the control inputs of the two subsystems together, the control objectives of the original system can be achieved. Both the stability criterion and design procedure of the closed-loop control system are developed. Finally, hardware-in-the-loop-based comparative experiments are conducted to demonstrate that the proposed method effectively suppresses the influence of the multiple disturbances on motor speed tracking accuracy and that the control system has both satisfactory dynamic performance and robustness.

Optimal control of wind farm power output with delay compensated nested-loop extreme seeking control

In this paper, we propose to enhance the nested-loop extremum seeking control (NLESC)-based wind farm control strategy with the predictor-based delay compensation in order to improve its convergence characteristics under fluctuating wind. Earlier work has shown the effectiveness of NLESC for region-2 wind farm operation, i.e., maximizing the total power output of cascaded wind turbine array, while its convergence speed is highly limited by the delay of power output for downstream turbines due to wake propagation along the wind direction. By utilizing the delay compensated ESC proposed by Oliveira and Krstic, the delay compensated NLESC (DCNLESC) wind farm control is proposed, allowing the dither frequencies to be of similar magnitude as that in the single-turbine ESC. This can significantly improve the convergence speed of optimum tracking for real-time wind farm control. The wake propagation delay is estimated from turbine power outputs using cross correlation and proper filtering. Using the SimWindFarm platform, the proposed DCNLESC strategy is simulated with both a single-column three-turbine array and a 2 × 3 turbine array, under different wind speeds. The results show that the convergence speed toward the calibrated optimum is significantly improved over the NLESC operation. The convergence time for the upstream turbines' torque gain is reduced by 55%–14% in terms of integral time-weighted absolute error, while the impact on turbine fatigue loads is as low as no more than 3.5% increase on turbine tower and shaft.

Disturbance Rejection Speed Control Based on Linear Extended State Observer for Isokinetic Muscle Strength Training System

Isokinetic muscle strength training is a type of rehabilitation therapy that can effectively improve the motor dysfunction of limbs and enhance joint muscle strength. In active isokinetic training, how to plan the speed reasonably and improve the performance of speed control to fit individualized needs is the core problem of isokinetic muscle strength training equipment control. In this study, a control strategy for isokinetic muscle strength training equipment is proposed, which identifies the user&#x2019;s motion intention and performs speed planning according to the user&#x2019;s motion participation so that the equipment produces a dynamic damping effect. This ensures active interaction between the user and the equipment and meets the force-speed requirements of elbow joint muscle training. To handle the uncertain disturbance in the training process, a linear extended state observer is used to estimate the disturbance term, and a tracking differentiator and nonlinear error feedback control are used to improve the speed tracking performance. The experimental results show that the proposed control scheme realizes effective speed control in isokinetic muscle strength training. <i>Note to Practitioners</i>&#x2014;The bottleneck of isokinetic muscle strength training equipment control system lies in how to plan the speed according to the patient&#x2019;s movement intention reasonably, and how to improve the performance of speed control in the process of rehabilitation training. This paper designs a speed auto disturbance rejection control strategy for isokinetic muscle strength training system with external interference and model uncertainty in practical application. The experimental results show that the performance of the controller is better than the traditional PI controller, and it can realize isokinetic muscle training. This study provides a method for practitioners interested in developing control systems for isokinetic rehabilitation training equipment.

Dual-Triggered Adaptive Torque Control Strategy for Variable-Speed Wind Turbine Against Denial-of-Service Attacks

The increasing utilization of wind energy necessitates dedicated attentions to identify vulnerabilities, raise awareness, and formulate strategies for cybersecurity defense, response, and future protection. As a potential cyber attack, denial-of-service (DoS) attacks that block the communication between the rotor speed sensor and the controller of the wind turbine may overload the drive-train and reduce the power generation efficiency. This paper attempts to propose a dual-triggered resilient torque control strategy for the variable-speed wind turbine (VSWT) against DoS attacks. Therein, the mathematical models including the dynamics of the WT and the DoS attack are established, and a neural network observer is designed for the WT system to compensate for the signal losses caused by DoS attack. To guarantee the optimal rotor speed tracking of the WT, an adaptive torque control method based on dual-triggered mechanism is developed, and the attack tolerance capability of the WT system is evaluated. The proposed control strategy can largely enhance the attack resilience capability of the WT, while saving the resources of computation and communication. Extensive studies on a 1.5 MW WT illustrate that the proposed adaptive resilient control scheme can effectively mitigate the impact of the DoS attacks and guarantee the optimal rotor speed tracking performance for the WT system.

Trajectory Guided Robust Visual Object Tracking With Selective Remedy

Siamese trackers have received a lot of attentions due to their promising performance and real-time high speed. However, the robustness is generally limited especially in challenging conditions, such as occlusion. Motivated by that tracking failure does not always occur and the target movement tends to follow certain patterns, in the paper, we propose a generic, fast and flexible approach to improve the robustness of Siamese trackers with two light-load novel modules: Trajectory Guidance Module (TGM) and Selective Refinement Module (SRM). Specifically, TGM encourages to pay a soft attention on possible target location based on short-term historical trajectory. SRM selectively remedies the tracking results at the risk of failure with little impact on the speed. The proposed algorithm can be easily establish upon state-of-the-art Siamese trackers and obtains better performance on seven benchmarks with high real-time tracking speed. The code is available at https://github.com/TJUMMG/TGSR.

Development of Decentralized Speed Controllers for a Differential Drive Wheel Mobile Robot

This research presents a comprehensive mathematical model and a control-oriented model for a differential drive wheel mobile robot (DDWMR). The components and their interconnection within the robot are thoroughly modeled. The proposed mathematical models are used to analyze the characteristics of the robot. The control-oriented model is a simplified one which will be used to design speed controllers. The performance of the proposed system is evaluated through four case studies, focusing on both stand-alone motor systems and the entire DDWMR. The tracking performance is evaluated using metrics such as absolute error integration and root mean square error. Simulation results show that under ideal step command, the pole-zero cancellation PI speed controller achieves the best tracking performance, while PID controllers obtained from Matlab’s Auto-tuning App perform best for standalone motor systems. For motors in a DDWMR, the PID controller with parameters derived from the Ziegler-Nichols tuning rules provides optimal tracking performance. These simulation results show that there is no universal controller that can achieve the best performance in all situations; rather, each controller is suited to specific circumstances. Nonetheless, in all case studies, better motor speed tracking leads to improved trajectory tracking performance for the robot.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium provided the original work is properly cited.

A safe and efficient decision algorithm for ramp merging based on a hierarchical planner and model predictive control

Due to highly dynamic interactions between vehicles, ramp merging decision is extraordinarily challenging in dense traffic. To deal with the non-convexity and inter-coupling of the decision-making problem, a top-level behavioural planner is proposed, which considers both the overall traffic situation and the individual characteristics of other interacting drivers. To ensure the implementability and efficiency of planning, a bottom-level motion planner is further designed with the guide of the top-level behavioural planner. The validation simulation using a naturalistic driving dataset shows that the proposed planning algorithm can achieve a success rate of 97.65% and has similar gap selection decision as human drivers. Then to track the planned vehicle motion, a nonlinear model predictive controller considering actuator delay and lag characteristics is proposed. Finally, the proposed planning and control modules are deployed in a turbocharged test vehicle, with satisfactory vehicle lateral and speed tracking errors, which validate the implementability of the proposed ramp merging decision algorithms.

Dynamic positioning for semi-submersible platform using stable fuzzy model predictive control

More and more semi-submersible platforms are being employed in the deep-sea industry as a result of the offshore oil and gas industry’s rapid growth, which also raises the bar for the positioning capabilities of semi-submersible platforms’ positioning systems. Model predictive control is frequently used in dynamic positioning control because it is effective at addressing a variety of limitations. Yet, there are still issues to be resolved regarding how to enhance model predictive control’s robustness under various working settings and how to offer workable solutions for model predictive control under stable conditions. The approach of stable fuzzy model predictive control for the dynamic placement of a semi-submersible platform is suggested in this research based on this. This method increases the tracking accuracy and tracking speed of dynamic positioning by incorporating the T-S fuzzy algorithm into model predictive control and allowing the coefficients in the weight matrix of the objective function to be modified adaptively. In addition, the solution is limited to the feasible domain by the addition of the control Lyapunov function, ensuring the stability and efficiency of the entire control procedure. A semi-submersible platform’s fixed-point control and trajectory tracking can be accomplished using the proposed technique, which has better trajectory tracking effects than the conventional model predictive control, according to comprehensive simulation findings.

SiamCAF: Complementary Attention Fusion-Based Siamese Network for RGBT Tracking

The tracking community is increasingly focused on RGBT tracking, which leverages the complementary strengths of corresponding visible light and thermal infrared images. The most well-known RGBT trackers, however, are unable to balance performance and speed at the same time for UAV tracking. In this paper, an innovative RGBT Siamese tracker named SiamCAF is proposed, which utilizes multi-modal features with a beyond-real-time running speed. Specifically, we used a dual-modal Siamese subnetwork to extract features. In addition, to extract similar features and reduce the modality differences for fusing features efficiently, we designed the Complementary Coupling Feature fusion module (CCF). Simultaneously, the Residual Channel Attention Enhanced module (RCAE) was designed to enhance the extracted features and representational power. Furthermore, the Maximum Fusion Prediction module (MFP) was constructed to boost performance in the response map fusion stage. Finally, comprehensive experiments on three real RGBT tracking datasets and one visible–thermal UAV tracking dataset showed that SiamCAF outperforms other tracking methods, with a remarkable tracking speed of over 105 frames per second.

Vision-Based Hand Detection and Tracking Using Fusion of Kernelized Correlation Filter and Single-Shot Detection

Hand detection and tracking are key components in many computer vision applications, including hand pose estimation and gesture recognition for human–computer interaction systems, virtual reality, and augmented reality. Despite their importance, reliable hand detection in cluttered scenes remains a challenge. This study explores the use of deep learning techniques for fast and robust hand detection and tracking. A novel algorithm is proposed by combining the Kernelized Correlation Filter (KCF) tracker with the Single-Shot Detection (SSD) method. This integration enables the detection and tracking of hands in challenging environments, such as cluttered backgrounds and occlusions. The SSD algorithm helps reinitialize the KCF tracker when it fails or encounters drift issues due to sudden changes in hand gestures or fast movements. Testing in challenging scenes showed that the proposed tracker achieved a tracking rate of over 90% and a speed of 17 frames per second (FPS). Comparison with the KCF tracker on 17 video sequences revealed an average improvement of 13.31% in tracking detection rate (TRDR) and 27.04% in object detection error (OTE). Additional comparison with MediaPipe hand tracker on 10 hand gesture videos taken from the Intelligent Biometric Group Hand Tracking (IBGHT) dataset showed that the proposed method outperformed the MediaPipe hand tracker in terms of overall TRDR and tracking speed. The results demonstrate the promising potential of the proposed method for long-sequence tracking stability, reducing drift issues, and improving tracking performance during occlusions.

Stand-alone Micro Grid based on Artificially Intelligent Neural Network (AI-NN)

INTRODUCTION: Hybrid stand-alone Small Wind Solar Energy System offers a feasible solution in remote areas where grid connectivity is either financially or physically unavailable. A small wind turbine (SWT) and a solar photovoltaic system are part of the hybrid energy system, which is effectively employed to meet the energy needs of rural household loads. OBJECTIVE: This research suggests an effective analysis of wind solar hybrid system controllers taking energy demands into account. The controller should be designed in such a way as to intelligently monitor the availability of wind energy and solar energy and store the energy without spilling it out. METHODS: In order to cope with the challenging factors involved in designing the controller, intelligent power tracking with an artificially intelligent neural network (AI-NN) is designed. Added to that, the whole process has been designed and analysed with the MATLAB SIMULINK tool. RESUSTS: The results of the simulation, infer that AI-NN achieved the regression value of 0.99 when compared with the Perturb &amp; Observe algorithm (P&amp;O), and the Fuzzy Logic Control (FLC) algorithm, and has a higher tracking speed. Also, the AI-NN attained 2.62kW whereas the P&amp;O has attained 2.52kW and Fuzzy logic has attained 2.43W of power which is 3.89% higher than P&amp;O algorithm and 7.52% higher than fuzzy MPPT algorithm. CONCLUSION: The designed controller module enhances the system by artificially intelligent algorithm. The AI-NN attains the better power performance with lesser tracking time and higher efficiency. Thus, it is evident that AI-NN MPPT suits well for the hybrid system.

Simulation of The System of Provision of Target Braking of Rolling Stock

This research presents a framework simulation model that can be used to simulate target braking of rail rolling stock, with a particular focus on target braking in metro systems. The model takes into account various factors that might affect stopping accuracy, including errors in speed measurement and track sensors' positioning. The research considered the possibility to minimize the required equipment for the target braking system, with its conclusions providing insights for possible solutions that would require minimal additional equipment or infrastructure modifications. Through simulation in MATLAB®/Simulink® environment, this study investigates the individual impact of these factors on the target braking precision of rail rolling stock. By identifying these critical factors that affect braking accuracy and quantifying their impact, this research provides valuable insights into strategies for optimizing braking performance and enhancing passenger safety and comfort.