Tracking Scheme Research Articles

Dual-loop PID control strategy for ramp tracking and ramp disturbance handling for unstable CSTRs

Dual-loop PID control strategy for ramp tracking and ramp disturbance handling for unstable CSTRs

Red flags for potential serious pathologies in people with neck pain: a systematic review of clinical practice guidelines.

We conducted a systematic review of clinical practice guidelines to identify red flags for serious pathologies in neck pain mentioned in clinical practice guidelines, to evaluate agreement in red flag recommendations across guidelines, and to investigate the level of evidence including what study type the recommendations are based on. We searched for guidelines focusing on specific and nonspecific neck pain in MEDLINE, EMBASE, and PEDro up to June 9, 2023. Additionally, we searched for guidelines through citation tracking strategies, by consulting experts in the field, and by checking guideline organization databases. We included 29 guidelines, 12 of which provided a total of 114 red flags for fracture (n = 17), cancer (n = 21), spinal infection (n = 14), myelopathy (n = 15), injury to the spinal cord (n = 1), artery dissection (n = 7), intracranial pathology (n = 3), inflammatory arthritis (n = 2), other systemic disease (n = 6), or unrelated to a specific condition (n = 19). Overall, there is very little agreement (median Fleiss' kappa of 0) between guidelines on the red flags to screen for serious pathologies. Red flags were mainly supported by expert opinions. We also observed a general lack of consensus among guidelines regarding which red flags to endorse. Considering the current limitations of the evidence, specific recommendations on which red flags to use cannot be provided, except for using the Canadian C-Spine rule for screening posttraumatic fractures.

Adaptive connectivity control in networked multi-agent systems: A distributed approach.

Effective communication is crucial for the performance and collaboration within cooperative networked multi-agent systems. However, existing literature lacks comprehensive solutions for dynamically monitoring and adjusting communication topologies to balance connectivity and energy efficiency. This study addresses this gap by proposing a distributed approach for estimating and controlling system connectivity over time. We introduce a modified consensus protocol where agents exchange local assessments of communication link quality, enabling the estimation of a global weighted adjacency matrix without requiring centralized information. The system's connectivity is measured using the second smallest eigenvalue of the communication graph Laplacian, commonly referred to as algebraic connectivity. Additionally, we enhance the consensus protocol with an adaptive mechanism to expedite convergence, irrespective of system size or structure. Furthermore, we present an analytical method for connectivity control based on the Fiedler vector approximation, facilitating the addition or removal of communication links. This method adjusts control parameters to accommodate minor variations in link quality while reconfiguring the network in response to significant changes. Notably, it identifies and eliminates energy-consuming yet non-contributory links, improving long-term connectivity efficiency. Simulation experiments across diverse scenarios and the number of agents validate the efficacy of our proposed algebraic connectivity estimation and tracking strategy. Results demonstrate robust connectivity maintenance against external disturbances and agent failures, underscoring the practical utility of our approach for real-world multi-agent systems.

Discrete‐Time Integral Fast Terminal Sliding Mode Predictive Control for Dynamic Positioning Ships With Lumped Uncertainties and Input Constraints

ABSTRACTThis paper proposes a robust discrete‐time integral fast terminal sliding mode predictive control (DIFTSMPC) scheme for DP ship trajectory tracking under the consideration of unmodeled dynamics, environmental disturbance, and input constraints. An improved single closed‐loop control strategy is designed by adopting the earth‐fixed reference velocity as the virtual velocity. The improved nonlinear PID discrete‐time integral fast terminal sliding mode is designed to achieve faster convergence. The unknown nonlinear dynamics and the environmental disturbance are combined as a lumped uncertainty term and estimated using the one‐step delay observation method. Then the corrected sliding model state is optimized to track the reference sliding reaching law in the prediction horizon. The lumped uncertainty estimation is integrated into the sliding mode prediction to guarantee the robustness of the control scheme. The stability of the proposed scheme has been verified. Comparison results demonstrate the effectiveness and superiority in chattering suppressing and constraint handling of the proposed scheme.

Framework for abnormal event detection and tracking based on effective sparse factorization strategy

The idea of tracking video objects has evolved to facilitate the area of surveillance systems. However, most current research efforts lie in speedy abnormal event detection and tracking of objects of interest tracking. However, the primary challenge is dealing with complex video structures' inherent redundancy. The existing research models for video tracking are more inclined towards improving accuracy. In contrast, the consideration of a more significant proportion of mobile object dynamics, e.g. abnormal events, in motion over the crowded video frame sequence is mainly overlooked, which is essential to study a specific movement pattern of the object of interest appearing in the frame sequence concerning the cost of computation factors. The study thereby introduces a unique strategy of speedy abnormal event detection and tracking, which facilitates video tracking to assess a specific pattern of object of interest movement over complex and crowded video scenes, considering a unique learning-based approach. The extensive simulation outcome further shows that the proposed tracking model accomplishes better tracking accuracy yet retains an optimized computation cost compared to the baseline studies. The computation of video tracking also accomplishes higher detection rates even in the challenging constraints of partial/complete occlusion, illumination variation and background clutter.

A Clustering Scheduling Strategy for Space Debris Tracking

The number of spacecraft and space debris, particularly in low Earth orbit, is rapidly escalating, heightening the demand for rational and effective satellite allocation strategies to alleviate the strain on satellite monitoring and tracking systems. In this paper, a clustering scheduling strategy is proposed for satellites equipped with sensors tasked with monitoring a substantial amount of space debris. To address the challenge of excessive space debris, a dynamic and static clustering division scheme is introduced, leveraging target characteristics in conjunction with the nearest neighbor clustering algorithm. Furthermore, to enhance scheduling efficiency, four performance indicators are proposed to guide the generation of a resource allocation scheme using Genetic Algorithm and Simulated Annealing. Simulation results demonstrate that the clustering scheduling strategy effectively clusters a large number of space debris, while the scheduling scheme demonstrates better balance, comprehensiveness, and efficiency. The Genetic Algorithm and Simulated Annealing exhibit superior performance compared to their counterparts across various orbital inclinations and altitudes. Notably, in a specific simulation scenario related to constellations, the Genetic Algorithm and Simulated Annealing achieve a performance enhancement of 13.48% compared to the Genetic Algorithm and an improvement of 8.73% compared to Simulated Annealing.

Optimal parameter identification of adaptive fuzzy logic MPPT based-bald eagle search optimization algorithm to boost the performance of PEM fuel cell

The output power of proton exchange membrane (PEM) fuel cell is dependent on operating temperature and the membrane water content. Changing the operation condition changes the location maximum power point (MPP) on the nonlinear curve of power versus the current. Therefore, a robust MPP tracking strategy is obligatory to grantee the work of PEM fuel cell around or close the MPP and boost the harvested energy. The suggested tracker is based on the bald eagle search (BES) optimization algorithm to identify the best values to entirely advantage the inherent flexibility of fuzzy logic control (FLC) system furthermore to accomplish rapid and precise tracking. Throughout the optimization procedure, the gains of membership functions of FLC are assigned to be decision variables, while the integral of error is the objective function. To approve the advantage of BES, a comparison with gradient-based optimizer (GBO), osprey optimization algorithm (OOA), particle swarm optimization (PSO), pelican optimization algorithm (POA), blue monkey algorithm (BMA) and henry gas solubility optimization (HGS) is performed. The BES has the highest performance compared with other optimizers; it achieves the best value of 1.7638 flowed by GBO (1.7667) whereas the worst value of 1.7987 is obtained by POA. Additionally, the lowest STD of 0.0046 is achieved by BES followed by GBO (0.0078) whereas the worst value of STD of 0.0243 is obtained by HGS. Finally, the optimized FLC-MPPT-based BES is compared with commonly MPPTs including classical FLC and perturb & observe in terms of dynamic and steady state conditions. The comparison demonstrated that the suggested FLC-MPPT-based BES has fast tracking speed and diminishes the oscillations around MPP in steady state.

A comprehensive framework of the decomposition-based hybrid method for ultra-short-term wind power forecasting with on-site application

Ultra-short-term wind power forecasting (UWPF) is crucial for the large-scale grid connection of wind energy. The state grid in China has strict multi-step forecasting requirements, which pose challenges to on-site efficiency and accuracy. This paper proposes a comprehensive framework of the hybrid method for UWPF with on-site application, consisting of data decomposition, model prediction, and post-processing. In the first stage, rolling decomposition and feature reconstruction are employed to decompose the wind power data into sub-components without future information leakage. The feature extraction and model matching processes are then performed to make full use of different machine learning prediction models and distinct characteristics of wind power components. Finally, a novel error tracking strategy is proposed to enable real-time error correction for multi-step forecasting by capturing the fluctuation characteristics of wind power data. The proposed framework is evaluated on two field wind power datasets through comparative experiments with five benchmark methods and nine reference methods. The experimental results demonstrate that (a) Each module of the proposed method effectively contributes to improving forecasting accuracy. (b) The proposed method significantly outperforms traditional machine learning methods in both single-step and multi-step forecasts, indicating its capability to handle practical UWPF tasks effectively.

Steer-by-wire control algorithm using a dual-layer closed-loop model

Steer-by-wire (SBW) is a crucial chassis electronic control system. Accurate and rapid wheel angle control is key to enhancing system performance. This paper first establishes a dynamics model of the steering actuator. Then, a co-simulation vehicle model with CarSim and Simulink is built, and the feasibility of the SBW model is experimentally verified. Based on this, a front wheel angle compensation difference tracking strategy is proposed using the front wheel steering angle control method. This strategy compensates for the difference between the actual and ideal front wheel steering angles to achieve the desired front wheel angle progressively. A dual-layer controller is designed in the Simulink module and integrated with the CarSim module to achieve closed-loop feedback control of the front wheel angle. The upper layer controller employs a fuzzy PID control method to execute the front wheel angle compensation strategy, enabling two corrections of the front wheel angle and yaw rate. The lower layer controller corrects wheel angle information to improve control effects. Simulation results under double lane change and single lane change conditions show that the fuzzy PID controller with front wheel angle compensation significantly enhances vehicle steering stability. Notably, at high speeds, the peak yaw rate is lower, convergence speed is faster, and response time is shorter.

Finite-time trajectory tracking control of quadrotor UAVs based on neural network disturbance observer and command filter

The paper proposes a novel finite-time control strategy for quadrotor UAV trajectory tracking using a neural network disturbance observer and a command filter. This method is used to address input saturation and disturbances, ensuring that the UAV can accurately follow the desired trajectory in finite time. The neural network disturbance observer is crucial for approximating external disturbance signals within a finite time, while the finite-time backstepping scheme accelerates the convergence of tracking errors. The command filtering technique is employed to avoid the complex derivation of virtual control laws, simplifying the controller design. The importance of this method lies in its ability to achieve fast, disturbance-resistant trajectory tracking for UAVs, making the control system more robust in practical applications. Simulations were conducted, showing that the proposed control strategy enables the quadrotor UAV to track its desired trajectory effectively, with improved anti-jamming capability. Both filtering and observation errors converged to the equilibrium point, validating the effectiveness of the approach. However, internal factors like actuator failure were not considered, pointing to future work in refining the method and applying it in real-world UAV experiments.

CF-SOLT: Real-time and accurate traffic accident detection using correlation filter-based tracking

Traffic accident detection using video surveillance is valuable research work in intelligent transportation systems. It is useful for responding to traffic accidents promptly that can avoid traffic jam or prevent secondary accident. In traffic accident detection, tracking occluded vehicles in real-time and accurately is one of the major sticking points for practical applications. In order to improve the tracking of occluded vehicles for traffic accident detection, this paper proposes a simple online tracking scheme with correlation filters (CF-SOLT). The CF-SOLT method utilizes a correlation filter-based auxiliary tracker to assist the main tracker. This auxiliary tracker helps prevent target ID switching caused by occlusion, enabling accurate vehicle tracking in occluded scenes. Based on the tracking results, a precise traffic accident detection algorithm is developed by integrating behavior analysis of both vehicles and pedestrians. The improved accident detection algorithm with the correlation filter-based auxiliary tracker can provide shorter response time, enabling quick identification and detection of traffic accidents. The experiments are conducted on the VisDrone2019, MOT-Traffic and Dataset of accident to evaluate the performances metrics of MOTA, IDF1, FPS, precision, response time and others. The results show that CF-SOLT improves MOTA and IDF1 by 5.3% and 6.7%, accident detection precision by 25%, and reduces response time by 56 s.

Surrogate gating strategies for the Elekta Unity MR-Linac gating system.

MRI-guided adaptive radiotherapy can directly monitor the anatomical positioning of the intended target during treatment with no additional imaging dose. Elekta has recently released its comprehensive motion management (CMM) solution that enables automatic radiation beam-gating on the Unity MR-Linac. Easily visualized targets that are distinct from the surrounding anatomy can be used to drive automatic gating decisions from the MRI cine imaging. However, poorly visualized targets can compromise the tracking and gating capabilities and may require surrogate tracking structures. This work presents strategies to generate robust tracking surrogates for a variety of treatment sites, enabling a wider application of CMM. Surrogate tracking strategies were developed from a cohort of patients treated using the CMM system on the Unity MR-Linac for treatment sites of the lung, pancreas, liver, and prostate. These sites posed challenging visualization or tracking of the primary target thereby compromising the tracking accuracy. Surrogate structures were developed using site-specific strategies to improve the imaging textured detail within the tracking volume while avoiding the dynamic overwhelming hypo- or hyper-intense anatomical structures. These surrogate volumes were applied within the anatomical positioning monitoring system as a proxy that drove the CMM gating decisions on the treatment unit. Robust site-specific surrogate structures were developed. Surrogate tracking structures for centrally located thoracic targets were created by expanding the target peripherally away from the heart and great vessels and into the lung. Pancreas surrogates required a vertically expanded column intersecting with the inferior liver edge. For the liver and prostate, surrogate structures consisted of a uniform expansion of the target, with liver surrogates intersecting the proximal liver edge or diaphragm while avoiding nearby ribs. These surrogate strategies have enabled the gating of complex moving targets among different treatment sites at our institution.

Development of system analysis code and its application in transient simulation of supercritical CO2 Brayton cycle direct cooled reactors

The supercritical carbon dioxide (s-CO2) Brayton cycle direct cooled reactor system has the characteristics of high cycle efficiency, compact equipment, and simple layout, which has attracted the attention of researchers recently. The use of s-CO2 Brayton cycle and the elimination of intermediate heat exchangers make the transient characteristics of this reactor different from light water reactors which have rich operating experience. Transient simulation and safety analysis with system code is an essential stage in reactor design and safety verification. Therefore, a system code, named NUSAS (NUclear Safety Analysis and Simulation), was developed in this study for the transient simulation of s-CO2 Brayton cycle direct cooled reactor system. This code is developed based on the advanced two-fluid two-pressure seven-equation model and has the capability of modelling the turbomachinery in s-CO2 Brayton cycle direct cooled reactor system. The transient characteristics during core power step and precooler cooling water mass flow rate step were analyzed. The load tracking and operation control strategy of the reactor was designed, and transient simulations of the wide range (100% to 50%) load tracking process were conducted. According to the simulation results of NUSAS, the transient characteristics of s-CO2 Brayton cycle direct cooled reactor were demonstrated when operating conditions change.

Barrier function-based prescribed performance trajectory tracking control of wheelchair upper-limb exoskeleton robot under actuator fault and external disturbance: experimental verification

This paper presents an innovative control strategy for the trajectory tracking of wheelchair upper-limb exoskeleton robots, integrating sliding mode control with a barrier function-based prescribed performance approach to handle actuator faults and external disturbances. The dynamic model of the exoskeleton robot is first extended to account for these uncertainties. The control design is then divided into two phases. In the first phase, the sliding mode control technique is applied to ensure robust trajectory tracking by defining the tracking error between the robot’s states and desired trajectories. A sliding surface is constructed based on this error, and to further enhance tracking performance, a prescribed performance control scheme is incorporated, which ensures fast error convergence and improves transient behavior. In the second phase, an advanced barrier function technique is introduced to mitigate the impact of actuator faults and disturbances, enhancing the overall robustness of the system. Stability and tracking accuracy are rigorously verified through Lyapunov theory, ensuring the system's resilience to uncertainties. The combined approach not only guarantees rapid error convergence but also prevents performance degradation due to excessive control action, maintaining system stability. Finally, the effectiveness of the proposed method is demonstrated through extensive simulations and hardware-in-loop experiments, highlighting its practical applicability for real-world exoskeleton systems.

A non-singular predefined-time sliding mode tracking control for space manipulators

This research presents a predefined-time control strategy for the trajectory tracking of a space manipulator subjected to parametric uncertainty and time-varying disturbance. Firstly, a nonlinear disturbance observer with predefined time convergence is constructed to achieve accurate estimation of the lumped disturbance without requiring any prior information. Subsequently, a unique predefined-time terminal sliding surface containing arctan function is proposed, which avoids the singularity problem that exists in traditional terminal sliding surfaces. Afterwards, a novel robust terminal sliding mode control law is designed based on the estimated knowledge. The developed controller ensures the predefined time stability of the closed-loop system, and the convergence time’s upper bound can be explicitly defined in advance through two specific parameters for any initial conditions. Finally, numerical simulations and comparative tests verify the effectiveness and superiority of the suggested controller.

Path tracking and energy efficiency coordination control strategy for skid-steering unmanned ground vehicle

The skid steering unmanned ground vehicle (SUGV) plays an important role in extremely harsh environments. Improving the autonomous control capability and energy efficiency of SUGV is urgently needed. This article presents a skid steering-based path tracking control strategy. In the upper controller, an improved model-free sliding mode controller (APMS) is used to calculate the yaw moment for tracking control. On the lower controller, the Snow Ablation Optimizer (SAO) is used to distribute the output torque of the drive motors, taking longitudinal force, yaw moment and energy consumption into account. Finally, the designed controller is validated through simulation under different operating conditions. The results show that the proposed coordination controller achieves good control performance, increases energy efficiency and at the same time ensures tracking accuracy.

Enhancing Wind Energy Conversion Efficiency: A Novel MPPT Approach Using P&O with ADRC Controllers versus PI Controllers with Kp and Ki Optimization via Genetic Algorithm and Ant Colony Optimization

This manuscript introduces an innovative Maximum Power Point Tracking (MPPT) strategy to improve the efficiency of Wind Energy Conversion Systems (WECS) equipped with Permanent Magnet Synchronous Generators (PMSG) under variable wind conditions. The proposed approach integrates Active Disturbance Rejection Control (ADRC) with the Perturb and Observe (P&O) algorithm, effectively addressing challenges such as external disturbances and fluctuating wind environments. By combining ADRC with P&O control, the system achieves enhanced tracking performance and adaptability.To validate the added value of this approach, we compare it with a traditional P&O strategy combined with Proportional Integral (PI) control. For the PI-based method, controller parameters Kp and Ki are optimized using Genetic Algorithm (GA) and Ant Colony Optimization (ACO) to enhance control precision. The Integrated Time Absolute Error (ITAE) objective function is employed to fine-tune these parameters, further optimizing system performance. Our analysis underscores the superiority of ADRC in disturbance rejection and quick adaptability over the PI approach.The proposed strategy is tested under two distinct wind speed profiles—constant and fluctuating—through time-domain simulations in MATLAB/Simulink. Simulation results confirm the superior performance of the ADRC-P&O method, highlighting its effectiveness in maximizing power extraction from wind energy and proving its potential for real-world applications. This study offers a significant advancement in wind energy technology by providing a robust and efficient solution for MPPT in WECS.

Dynamic Current Trajectory Tracking Strategy With Optimized Overmodulation Method for Permanent Magnet Synchronous Motor Drives

Dynamic Current Trajectory Tracking Strategy With Optimized Overmodulation Method for Permanent Magnet Synchronous Motor Drives

Distributed Neuroadaptive Formation Control for Aerial Base Station-Assisted Hovercraft Systems with Mixed Disturbances

Effectively addressing the formation control of ABS-assisted hovercraft systems with heterogeneities, unavailable leaders’ convex combination states, nonlinearities, and mixed disturbances poses significant challenges. This paper proposes a distributed neuroadaptive formation tracking strategy of ABS-assisted hovercraft systems for the first time, where aerial base stations (ABSs) are composed of unmanned aerial vehicles (UAVs) for data distribution and computation offloading. Firstly, UAVs are designed to track the virtual-leader while shaping a fixed formation, and the observer is devised for each follower hovercraft to estimate the convex combination states of UAVs. Then, output regulation equations are employed to transform heterogeneous systems into a compact form via the Kronecker product, while neural networks (NNs) are introduced to compensate for model nonlinearities. Furthermore, based on random differential equations (RDEs) combined with Lyapunov theory, the noise-to-state practical stability in probability (NSPS-P) property of the error dynamics under mixed disturbances can be obtained. Finally, simulation examples demonstrate that the outputs of follower hovercrafts rapidly achieve a time-varying formation and rotate around convex combination states of leader UAVs simultaneously.

Tracking Small Extracellular Vesicles Using a Minimally Invasive PicoGreen Labeling Strategy.

Extracellular vesicles (EVs) are cell-secreted lipid bilayer delimited particles that mediate cellular communication. These tiny sacs of cellular information play an important role in cell communication and alter the physiological process under both normal and pathological conditions. As such, tracking EVs can provide valuable information regarding the basic understanding of cell communication, the onset of early malignancy, and biomarker discovery. Most of the current EV-tracking strategies are invasive, altering the natural characteristics of EVs by modifying the lipid bilayer with lipophilic dyes or surface proteins with fluorescent reporters. The invasive labeling strategies could alter the natural processes of EVs and thereby have major limitations for functional studies. Here, we report an alternative minimally invasive EV labeling strategy using PicoGreen (PG), a small molecule that fluoresces at 520 nm when bound to dsDNA. We show that PG binds to dsDNA associated with small EVs (50-200 nm), forming a stable and highly fluorescent PG-DNA complex in EVs (PG-EVs). In both 2D cell culture and 3D organoid models, PG-EV showed efficient tracking properties, including a high signal-to-noise ratio, time- and concentration-dependent uptake, and the ability to traverse a 3D environment. We further validated PG-EV tracking using dual-labeled EVs following two orthogonal labeling strategies: (1) Bioconjugation via surface amine labeling and (2) donor cell engineering via endogenously expressing mCherry-tetraspanin (CD9/CD63/CD81) reporter proteins. Our study has shown the feasibility of using PG-EV as an effective EV tracking strategy that can be applied for studying the functional role of EVs across multiple model systems.