State Tracking Research Articles

Ultra-local model based prescribed performance adaptive control of overhead cranes under uncertainties

In this paper, a model-free adaptive control scheme is proposed to address the problem of system performance variations due to inaccurate models of overhead cranes, and external disturbances. First, an adaptive fast integral terminal sliding mode controller with a prespecified performance setting is proposed to accelerate the convergence of the sliding mode surface. In addition, an improved form of performance function is introduced to ensure the high accuracy convergence of state errors, while effectively enhance response performance and state tracking accuracy. Second, a prespecified performance nonlinear disturbance observer is introduced to estimate the lumped dynamics part of the ultra-local model, which can guarantee effective model-free properties. Finally, the stability of the closed-loop control scheme is analyzed by means of Lyapunov stability theory, and the effectiveness and superiority of the proposed method is proved by means of numerical simulation results.

Related Work on Few-shot Method: A Review

Abstract. As Natural Language Processing (NLP) technologies continue to expand into novel domains, few-shot learning emerges as a pivotal approach to addressing the challenge of data scarcity. Traditional neural networks, due to their strong reliance on abundant data, have posed limitations in their applicability to new domains. Consequently, there is a pressing need to introduce fresh research perspectives and solutions within this realm, aiming to propel its development towards greater practicality and efficiency. Firstly, solving the zero or few-shot Dialogue State Tracking problem has become necessary as the demand for deploying such systems in new domains continues to grow. This article explores the performance of D-REPTILE, a meta learner for Destination Address (DST) problems, in unknown domains. Then PET is extensively studied. Real-world tests on RAFT show cue-based learning works in low-sample settings, reinforcing the importance of instruction-based learning for human-like few-shot capabilities. Thirdly, to address the overfitting problem, this paper also explores the LA- UCL model, as well as its application, development, and challenges, which enables the LA-UCL model to enhance the Large Language Model data expansion effect through two modules. Finally, CausalCollab is introduced, which uses Incremental Stylistic Effects (ISE) as a guiding estimator for assessing the effectiveness of LM-human cooperation tactics through time.

Deep Reinforcement Learning-Assisted Teaching Strategy for Industrial Robot Manipulator

This paper introduces an innovative algorithm aimed at enhancing robot learning using dynamic trajectory modeling and time-dependent state analysis. By integrating reinforcement learning (RL) and trajectory planning, the proposed approach enhances the robot’s adaptability in diverse environments and tasks. The framework begins with a comprehensive analysis of the robot’s operational space, focusing on Cartesian coordinates and configuration systems. By modeling trajectories and states within these systems, the robot achieves sequential tracking of arbitrary states, facilitating efficient task execution in various scenarios. Experimental results demonstrate the algorithm’s efficacy in manipulation tasks and path planning in dynamic environments. By integrating dynamic trajectory modeling and time-dependent state analysis, the robot’s adaptability and performance improve significantly, enabling precise task execution in complex environments. This research contributes to advancing robot learning methodologies, particularly in human–robot interaction scenarios, promising applications in manufacturing, healthcare, and logistics.

State Tracking in Nonadiabatic Molecular Dynamics Using Only Forces and Energies.

A new algorithm for the identification of unavoided (trivial) crossings in nonadiabatic molecular dynamics calculations is reported. The approach does not require knowledge of wave functions or wave function time overlaps and uses only information on state energies and gradients. In addition, a simple phase consistency correction algorithm for time-derivative nonadiabatic couplings is proposed for situations in which wave function time overlaps are not available. The performance of the two algorithms is demonstrated using several state crossing models. The approaches work best for systems with localized nonadiabatic coupling regions but may have difficulties for those with extended regions of nonadiabatic coupling. It is found that state tracking alone is not sufficient for producing correct population dynamics and that nonadiabatic coupling phase correction is required.

Single-Task Joint Learning Model for an Online Multi-Object Tracking Framework

Multi-object tracking faces critical challenges, including occlusions, ID switches, and erroneous detection boxes, which significantly hinder tracking accuracy in complex environments. To address these issues, this study proposes a single-task joint learning (STJL) model integrated into an online multi-object tracking framework to enhance feature extraction and model robustness across diverse scenarios. Employing cross-dataset training, the model has improved generalization capabilities and can effectively handle various tracking conditions. A key innovation is the refined tracker initialization strategy that combines detection and tracklet confidence, which significantly reduces the number of false positives and ID switches. Additionally, the framework employs a combination of Mahalanobis and cosine distances to optimize data association, further improving tracking accuracy. The experimental results demonstrate that the proposed model outperformed state-of-the-art methods on standard benchmark datasets, achieving superior MOTA and reduced ID switches, confirming its effectiveness in dynamic and occlusion-heavy environments.

Flatness-based nonlinear internal model control with disturbances compensation via improved extended state observer for remotely operated vehicle

This paper proposes an improved Active Disturbance Rejection Control (ADRC) scheme for nonlinear control-affine systems, achieving superior robustness/ performance trade-offs. By leveraging the flatness property within a closed-loop structure, Nonlinear Internal Model Control (NLIMC) is employed as a model-based controller utilises partial plant knowledge to enhance control performance and eliminate static tracking errors. Subsequently, an improved Extended State Observer (IESO) is incorporated with NLIMC to address unmodelled dynamics, disturbances, and parameter uncertainties in addition to the full state's estimation. The IESO's added hyperbolic function dynamic ensures higher sensitivity and accuracy compared to conventional linear ESOs, especially in case of large errors. Control performances are evaluated through a Remotely Operated Vehicle (ROV) application. Numerical simulations have been carried out and the results confirm its effectiveness and superiority compared to Ocean Disturbance Observer-based Double-loop Sliding Mode Controller (ODSMC) and the linear ADRC.

Improving Freight Train Wheel Monitoring with Smart Sensors

Onboard monitoring of freight car axleboxes enhances safety, reduces maintenance costs, and improves track conditions by preventing secondary damage. Installing wireless sensors on freight cars without a nearby power source should be cost-effective, given the large quantities involved. To address this, a new wireless smart sensor node has been deployed. The sensor automatically recognizes stable operating conditions, detects wheel rotational speed from vibrations, performs real-time condition monitoring, and transmits the results to the cloud. This study outlines the smart sensor concept and the pilot field test conducted with real freight cars. The results demonstrate the ability to estimate wheel rotational speed from vibrations and the potential for detecting wheel out-of-roundness (OOR) using a newly developed condition indicator for low-power real-time operations.

Estimation of genetic parameters for racing time and ranking in Thoroughbred horses

This study aimed to estimate genetic parameters for race time in seconds and final ranking, as well as to analyze the genetic trends associated with race time. The study utilized a dataset consisting of 23,290 records of race times and final ranks at distances of 1,000, 1,600, and 2,000 m from 6,213 Thoroughbred horses from the São Paulo Jockey Club. Our model considered the year of the run, animal sex, race class, track conditions, the linear effect of horse weight and age, and the quadratic effect of age as fixed covariates. Random effects included direct additive genetic, animal permanent environmental, and residual effects. Heritability estimates ranged from 0.01±0.03 to 0.17±0.04 (race time); 0.04±0.03 to 0.09±0.04 (final ranking), and repeatability from 0.19±0.02 to 0.37±0.02 (race time) and 0.15±0.02 to 0.23±0.04 (final ranking). Pearson's correlations of estimated breeding values between race time and ranking ranged from -0,027±0.16 to 0.84±0.01. When analyzing the genetic trend for races at a distance of 1,000 m, we observed a decrease in race time over time. However, for races at 1,600 and 2,000 m, a clear pattern of time reduction was not apparent. Race time offers a more promising response to selection, particularly in shorter races. Selecting for race time is expected to have a greater indirect impact on final rank. Genetic trends have revealed limited genetic progress over the years; therefore, it is advisable to reevaluate the current selection process.

Cascade Model Predictive Control for Enhancing UAV Quadcopter Stability and Energy Efficiency in Wind Turbulent Mangrove Forest Environment

Unmanned aerial vehicle (UAV) modelling and control, particularly in quadrotors with high position-orientation coupling, present significant challenges for practical applications, such as environmental monitoring missions in windy mangrove forests. Conventional control strategies like the PID controller, often employed in simulations due to their simplicity, often underperform in real-world scenarios due to their linear assumptions. This research proposes a novel hierarchical cascaded model predictive control system for altitude, attitude, and battery efficiency for quadrotor in mangrove area. This control system addresses computational complexity by decomposing the overall MPC strategy into two distinct schemes, one for translational displacements and another for rotational movements, enhancing the UAV's resilience to wind turbulence, a significant disturbance factor in mangrove environments. Rigorous simulation and experiment test flight involving complex trajectory tracking and windy conditions demonstrate the proposed controller's superior performance compared to conventional PID controller, particularly in terms of stability, disturbance rejection, underscoring its potential for UAV applications in challenging environments.

Distributed minimum error entropy with fiducial points Kalman filter for state tracking

With the growing size of the system, this distributed Kalman filter (DKF) is widely used in multi-sensor networks. However, it is difficult for DKF to accurately estimate state values in non-Gaussian noise environments. In this paper, a regression equation is first constructed to contain all sensor node information. Then, by bringing the minimum error entropy with fiducial points (MEEF) standard into the process of information fusion, a robust algorithm named centralized MEEF KF (CMEEF-KF) is presented, which is robust to non-Gaussian noise and unusual data. Furthermore, to overcome the communication burden of CMEEF-KF in sensor networks, the distributed MEEF-KF (DMEEF-KF) is developed, which construct a framework of consensus average method for node information fusion. Specifically, each sensor only exchanges the key information with its neighborhoods. In addition, in order to make the algorithm able to cope with the nonlinear state estimation problem, the distributed MEEF extended Kalman filter is also proposed. Eventually, the effectiveness of the suggested algorithms is demonstrated by land vehicle navigation and power system tracking state estimation using a 10-node sensor network.

Truncated predictive control for delayed cyber–physical systems under deception attacks

In this work, the state tracking, input delay prediction, uncertainty and disturbance rejection problems are studied for the delayed cyber–physical systems under uncertainties, nonlinearities and external disturbances in which the addressed model is described by the fuzzy technique. More specifically, such an input-delayed system is linearized through an interval type-2 fuzzy technique accompanied by a parallel distribution compensation method. Further, the presence of constant input delays in the examined system is proficiently compensated with the assistance of a truncated predictor feedback approach. In parallel, the impacts of external disturbances and uncertainties that are occurred in the system are estimated by means of uncertainty and disturbance estimator method in an efficacious way. With the help of this estimated information, an uncertainty and disturbance estimator-based truncated predictive tracking control protocol is formulated to acquire the state tracking results of the underlying system in the presence of external disturbances and uncertainties. Remarkably, in this study, the interval type-2 fuzzy is blended with the designed reference system. Furthermore, the secure tracking control law is designed for the considered system under the random deception attacks. To address the deception attacks, the Bernoulli stochastic distribution is considered in this study. Moreover, the constraints are expressed for computing the truncated predictive feedback control gain matrices. Conclusively, the numerical simulation analysis is carried out to demonstrate the usefulness of the devised approach.

Fuel Efficiency Optimization in Adaptive Cruise Control: A Comparative Study of Model Predictive Control-Based Approaches

This work investigates the fuel efficiency potential of Adaptive Cruise Control (ACC) systems, focusing on two optimization-based control approaches for internal combustion engine (ICE) vehicles. In particular, this study compares two model predictive control (MPC) designs. In the first approach, a strictly quadratic cost is adopted, and fuel consumption is indirectly minimized by adjusting the weights assigned to state tracking and control effort. In the second approach, a fuel consumption map is explicitly included in the MPC cost function, aiming to directly minimize it. Both approaches are compared to a globally optimal benchmark obtained with dynamic programming. Although these methods have been discussed in the literature, no systematic comparison of their relative performance has been conducted, which is the primary contribution of this article. The results demonstrate that, with proper tuning, the simpler quadratic approach can achieve comparable fuel savings to the approach with explicit fuel consumption minimization, with a maximum variation of 0.5%. These results imply that the first alternative is more suitable for online implementation, due to the more favorable characteristics of the associated optimization problem.

Unsupervised domain adaptation for drive-by condition monitoring of multiple railway tracks

Monitoring railway tracks through drive-by vibration data collected by in-service trains offers a cost-effective and adaptable solution for inspecting multiple railway lines. However, numerous existing drive-by monitoring methods rely on supervised learning models, necessitating extensive labelled data for each line. In this paper, a novel framework is proposed based on Unsupervised Domain Adaptation (UDA) concept which facilitates the transfer of a geometric defects diagnosis model learned from one line to a new line without the need for any labelled data from the new line. The proposed framework learns the dynamic-based features that are sensitive to damage and also invariant to different railway tracks. It comprises three components: data pre-processing, UDA implementation, and damage diagnosis. The framework uses the data from the source domain, including corresponding labels, as well as the unlabelled data from the target domain as input. The outputs of the framework consist of the predicted labels for the target domain. The performance of the proposed framework is evaluated using a comprehensive dataset of field measurements of a high-speed train passing 4 different lines within the French high-speed rail network. The proposed UDA framework is implemented using four common UDA algorithms including Information-Theoretical Learning (ITL), Geodesic Flow Kernel (GFK), Transfer Component Analysis (TCA), and Subspace Alignment (SA). The results show that the proposed framework has a 14% increase in the anomaly detection accuracy compared to traditional unsupervised learning methods in which UDA is not used. Furthermore, this study investigates the impact of incorporating a percentage of target data labels during training (semi-supervised domain adaptation), along with various sensor layouts and different tuning parameters, on the accuracy of the proposed approach. The results show that the proposed framework can significantly facilitate the monitoring of railway track conditions using the data collected by in-service trains which could be great interest of railway owners.

Wheel wear of mountain metro vehicles with complex line conditions

Purpose Mountain metro vehicles have unique wheel wear characteristics due to the complex flat and longitudinal lines. With a combination of flat and longitudinal curved tracks, the traction and braking conditions are more frequent in mountain metro vehicles. This paper aims to analyze the wheel wear characteristics of mountain metro vehicles in complex flat and longitudinal lines. Design/methodology/approach A dynamic model of the mountain metro vehicle and a wear model are established to analyze the dynamic and wheel wear characteristics of mountain metro vehicles. The wheel wear law of mountain metro vehicles under complex track conditions is analyzed, and the suppression measure based on variable stiffness rotary arm nodes of mountain metro vehicles is proposed. Findings The results showed that the maximum wheel wear depth without considering the ramp track and considering the ramp track are 3.283 mm and 3.717 mm, respectively; the maximum wheel wear depth increases by 13.2%. Wheel wear can be effectively suppressed by the variable stiffness rotary arm model, and the maximum wear depth of the wheel profile is 3.316 mm, which is reduced by 10.79% compared with the constant stiffness model. Originality/value A dynamic model of a mountain metro vehicle is established, and the metro vehicle wheel wear under the large ramps under the traction and braking conditions is analyzed, and the metro vehicle wheel wear suppression measure based on variable stiffness rotary arm nodes is proposed. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-07-2024-0247

Electroencephalographic and Cardiovascular Assessments of Isoflurane-Anesthetized Dogs.

This study investigated the use of frontal electroencephalography (EEG) to monitor varying levels of isoflurane anesthesia in dogs. The patient state index (PSI), burst suppression ratio (SR), and waveforms, were continuously recorded while mean arterial blood pressure (MBP), heart rate, responses to electric stimuli, and subjective anesthetic "depth" were assessed every 3 min. At deep anesthesia (2.5× MAC - 3.2%), the PSI (6.5 ± 10.8) and MBP (45.6 ± 16.4 mmHg) were the lowest, and SR was the highest (78.3 ± 24.0%). At 1× MAC (1.3%), the PSI and MBP increased significantly to 47.8 ± 12.6 and 99.8 ± 13.2, respectively, and SR decreased to 0.5 ± 2.5%. The EEG was predominantly isoelectric at 2×-2.5× MAC, indicating unconsciousness and unresponsiveness. As anesthesia lightened, waveforms transitioned to flatter and faster activity patterns with a response to noxious stimuli, suggesting regained consciousness. The PSI and MBP exhibited a stronger correlation (ρ = 0.8098, p = 0.001) than the relationship of PSI with heart rate (ρ = -0.2089, p = 0.249). Five of the six dogs experienced rough recovery, possibly due to high SR and low MBP. These findings suggest that EEG monitoring in dogs can be a valuable tool for the real-time tracking of brain states and can be used to guide the management of isoflurane anesthesia.

ECDG-DST: A dialogue state tracking model based on efficient context and domain guidance for smart dialogue systems

Dialogue state tracking (DST) is an important component of smart dialogue systems, with the goal of predicting the current dialogue state at conversation turn. However, most of the previous works had problems with storing a large amount of data and storing a large amount of noisy information when the conversation takes many turns. In addition, they also overlooked the effect of the domain in the task of dialogue state tracking. In this paper, we propose ECDG-DST 1 (A dialogue state tracking model based on efficient context and domain guidance) for smart dialogue systems, which preserves key information but retains less dialogue history, and masks the domain effectively in dialogue state tracking. Our model utilizes the efficient conversation context, the previous conversation state and the relationship between domains and slots to narrow the range of slots to be updated, and also limit the directions of values to reduce the generation of irrelevant words. The ECDG-DST model consists of four main components, including an encoder, a domain guide, an operation predictor, and a value generator. We conducted experiments on three popular task-oriented dialogue datasets, Wizard-of-Oz2.0, MultiWOZ2.0, and MultiWOZ2.1, and the empirical results demonstrate that ECDG-DST respectively improved joint goal accuracy by 0.45 % on Wizard-of-Oz2.0, 2.44 % on MultiWOZ2.0 and 2.05 % on MultiWOZ2.1 compared to the baselines. In addition, we analyzed the scope of the efficient context through experiments and validate the effectiveness of our proposed domain guide mechanism through ablation study.

Railways Health Monitoring Employing KSK Approach: A Novel AIIoT based Decision-Making Approach for Railways

Central to this AIIoT approach is the implementation of machine learning algorithms that can process vast amounts of data generated from the sensors. These algorithms can identify patterns and anomalies that might indicate wear and tear or incipient failures in critical systems. For example, vibration sensors on trains can detect irregularities in wheel dynamics, while track-side monitoring systems can check for track integrity. By integrating these insights into a centralized health monitoring platform, railway operators are not only able to understand the current health status of their assets but also make informed decisions about maintenance schedules and resource allocation. Moreover, the innovative use of edge computing in this AIIoT framework allows for localized data processing, reducing latency and enabling immediate responses to critical situations. This is crucial in a railway environment where timely interventions can prevent accidents and improve service reliability. Additionally, the combination of AIIoT with cloud computing creates opportunities for advanced data analytics and machine learning models that can continuously improve their accuracy over time as more data becomes available. In essence, this novel AIIoT approach not only enhances operational efficiency but also aligns with broader initiatives aimed at making rail transport more sustainable by reducing unnecessary maintenance trips and optimizing resource utilization. The system informs the decision based on Track condition, speed of train, train condition to the authority.

Experimental analysis of wheel and bogie frame transfer functions for on-board measurements of rail roughness

Acceleration measurements on in-service rail vehicles give many valuable insights into track condition. These measurements are favourable in terms of measurement frequency, no need for additional track possession for measurements, operator safety, etc. The collected acceleration data however needs to be further processed to extract more detailed information on rail surface defects. To derive rail roughness from bogie frame acceleration, a complex signal processing approach is required, as well as determining the dynamic parameters of the track, vehicle, and operational conditions. In this paper, a method to evaluate the influence of resilient wheel and bogie frame on the acceleration data received from bogie frame-mounted accelerometers is described. The method is based on a standstill measurement of frequency response functions on wheel and bogie frame positions using an impact hammer and accelerometers. Following the preliminary results of bogie acceleration measurements, in-situ wheel receptance measurements were carried out on the same tramway vehicle in a standstill position, to determine the transfer function between the wheel-rail contact point and the position of the accelerometer mounted on the wheel frame. After the experimental study, a discussion of their potential application in the method for deriving rail roughness from vibration data is presented.

Sliding mode-based direct power control of unified power quality conditioner

The Unified Power Quality Conditioner (UPQC) is a promising solution for mitigating multiple Power Quality(PQ) issues in distribution systems, including harmonics, poor power factor, voltage sag/swell and voltage imbalance. The conventional Sliding Mode Controller (SMC) in UPQCs suffers from wide switching frequency variations, chattering problems, and inherent active and reactive power coupling. This study proposes a nonlinear control method, Sliding Mode-based Direct Power Control (SMC-DPC), for the simultaneous regulation of the shunt and series compensators in a UPQC. By optimizing voltage vector selection based on real-time power errors, the proposed method effectively mitigates chattering, and reduces switching frequency variations, and ensures precise tracking of instantaneous active and reactive powers even in the presence of coupling effects. The proposed approach simplifies the system design and improves steady state and dynamic power tracking. The simulation results in MATLAB Simulink® on a 20 kVA system demonstrate that the proposed method achieves a source current THD of 1.52%, compared to 2.31% for conventional SMC and 5.11% for linear controller. Furthermore, the method is robust to grid parameter variations and demonstrates satisfactory performance in both strong and weak grids. In weak grids, the proposed method reduces the line losses by 13.71% and 14.54% compared to SMC and linear controller respectively. The reported results comply with international standards such as IEEE-519 and IEC 61000-3-12, confirming effectiveness of SMC-DPC for enhancing PQ in distribution system.

Application of vibration signals in railway track diagnostics using a mobile railway platform

The article presents a comprehensive method for using vibration signals to diagnose railway tracks. The primary objective is to gather detailed information on track conditions through a passive experiment. This involves using mobile diagnostic tools and techniques to assess railway infrastructure. The article elaborates on the range of diagnostic activities conducted in accordance with detailed railway regulations and highlights the benefits and capabilities of mobile diagnostics in railway transport. The research includes mobile field measurements across the general railway manager’s network, employing vibration signals to detect and evaluate track conditions. The methodology section provides a thorough description of the mobile measurement rail platform, detailing the equipment used, the routes taken for measurements, and the processes of data acquisition and processing. The data obtained from these measurements is crucial for understanding the actual technical condition of the railway tracks. The method of obtaining and processing data is explained in relation to the real technical condition of the railway track. This involves using transducers with specific parameters and parametrically defined signal recording, along with dedicated analysis techniques in post-processing. Vibration signals serve as the primary carrier of information in this diagnostic method. The article details the step-by-step procedures for collecting and analyzing these signals to provide accurate assessments of track conditions. Based on the results from the mobile measurement rail platform, the article characterizes various areas of diagnostics where vibration signals are particularly effective for technical evaluation. These areas include identifying track defects, monitoring track surface and railway crossing and assessing the overall structural health of the railway infrastructure. The use of vibration signals offers a non-invasive and efficient means of track diagnostics, providing real-time data for maintenance and repair decisions. In conclusion, the article underscores the significance of mobile diagnostics in enhancing the safety and reliability of railway transport. By leveraging vibration signals and advanced data processing techniques, this method provides a framework for continuous monitoring and assessment of railway track conditions, ultimately contributing to improved maintenance strategies and operational efficiency.