Error System Research Articles

Method for establishing the multipath adaptive mitigation model considering deformation displacement of monitoring stations

ABSTRACT Multipath effects are an unavoidable error in Global Navigation Satellite System (GNSS) deformation monitoring. Existing multipath models are predominantly established under stationary conditions and are not applicable to scenarios involving slope ruptures or rapid displacements. To address this issue, we propose the Multipath Adaptive Mitigation Model (MAMM) and validate its precision. The study demonstrates that traditional multipath models are ineffective in suppressing multipath errors under the specified conditions. The comparison between MAMM and the Multipath Hemispherical Model (MHM) indicates that, in the simulated displacement experiments, MHM’s effectiveness in mitigating multipath errors significantly decreases as the actual displacement distance increases, whereas MAMM consistently maintains an improvement rate in positioning precision of over 100%, markedly surpassing that of MHM. In real displacement scenarios, MAMM demonstrates similar advantages to those observed in the simulated experiments under instantaneous deformation conditions, while in slow displacement scenarios, MAMM offers a limited precision advantage over MHM.

Analysis of laser spot centroiding errors for laser acquisition system in gravitational wave detection missions

Analysis of laser spot centroiding errors for laser acquisition system in gravitational wave detection missions

A multivariable adaptive formation control for multiple UAVs with external uncertain disturbances

Purpose A multivariable model reference adaptive control method is proposed to solve a distributed leader–follower formation control problem of unmanned aerial vehicles (UAVs) with uncertain parameters and unknown external disturbances for both leader and followers. Design/methodology/approach A case of uncertain stochastic external disturbances for UAVs is considered, and based on the distributed communication network of UAVs, a state-feedback adaptive controller is proposed to maintain the formation of UAVs consistently. Then, the stability and asymptotic tracking performance of the UAV formation control system are analyzed by the Lyapunov function. Findings The simulation results demonstrate that this formation control scheme can effectively solve the stochastic external disturbance problem of UAVs and ensure the stability of their formation. Originality/value The proposed multivariable model reference adaptive control method reduces the error of formation control system and improves the stability and control performance of UAV compared with fixed control.

Synchronization of Neural Networks With Unbounded and Non‐Differentiable Delays via Decentralized Adaptive Control

ABSTRACTSynchronization of delayed neural networks has been investigated in recent years via decentralized adaptive control methods. However, the effectiveness of the reported results heavily depends on the assumptions that network delays are bounded or differentiable. For more general cases involving unbounded and non‐differentiable delays, it remains unclear whether the existing analytical methods and controller designs are still effective. To this end, in this article, a novel method is established to analyze the asymptotical convergence of the controlled error system with adaptive parameters by employing the differential inequality techniques for unbounded delay and Barbalat's lemma, which can effectively overcome the limitations of traditional methods in handling general delay scenarios. The theoretical results demonstrate that traditional decentralized adaptive controller for network synchronization remains effective even if the boundedness and differentiability of delay are removed. A numerical simulation further validates the effectiveness of the proposed theories.

Contribution of radio occultation data to atmospheric river landfall forecasts in the eastern North Pacific

AbstractThis study assesses the impact of Global Navigation Satellite System radio occultation (RO) data assimilation (DA) on low‐predictability atmospheric river (AR) landfall forecasts. The period of study is October–March 2020–2022, focusing on AR events over the eastern North Pacific. Eighteen AR landfall events with large forecast errors in the Global Forecast System of the National Centers for Environmental Prediction are selected. Two numerical experiments are conducted for each AR event. One assimilates RO data using a non‐local excess phase operator to improve its initial condition (labeled as EPH forecast), while the other does not (reference run; labeled as REF forecast). Relative to REF, the root‐mean‐squared errors of integrated water vapor transport (IVT), moisture, and wind are reduced by EPH in the main AR activity areas throughout the whole forecast period, except for zonal winds. EPH significantly reduces the underestimation of strong IVT occurrence rate by 2.6%–4.8% and the underestimation of IVT intensity by 15.7–35.0 kg·m−1·s−1, compared to REF. EPH also substantially improves the precipitable water distribution and AR‐related precipitation over the ocean. Despite these basin‐wide improvements, AR coverage, location, and precipitation over land are not significantly affected by RO DA. Specifically, the main contribution of RO DA to the root‐mean‐squared error reduction of AR forecast is the wind improvement prior to AR landfall and the mid‐level moisture improvement after AR landfall. In summary, this study highlights the potential positive contributions of RO DA to AR forecasts, and explores the mechanisms and spatial and temporal characteristics of the improvements.

Analisis Sistem Akuntansi Persediaan Barang Dagang Pada UMKM MiMa Frozen Food

In its application, the inventory accounting system often faces problems with inaccurate recording data, such as differences between the recorded amount of inventory and the number of physical goods, which can cause financial and operational losses. The purpose of this research is to determine the recording of merchandise inventory at UMKM MiMa Frozen Food as well as inventory problems regarding the factors that cause differences between the recorded inventory amount and the number of physical goods. This research uses a qualitative descriptive method with observation, interviews and documentation. The results of this research show that inventory recording uses a perpetual recording system using the "Qasir" application system. The inventory valuation method applied is appropriate, namely by choosing the FIFO method where goods that come in first will be released first so as to avoid expiration problems. Meanwhile, several causes of inventory discrepancies are human error (data input and calculations), system errors, different goods from distributors, and damaged or expired goods that are not recorded.

Finite‐Time Functional Interval Observer Design for Nonlinear Discrete‐Time Switched Descriptor Systems

ABSTRACTIn this article, the finite‐time functional interval estimation method for nonlinear discrete‐time switched descriptor systems with perturbations is investigated. To deal with the nonlinear terms in the switched system, the nonlinear error system is converted into a linear parameter varying form using the reformulated Lipschitz property. Firstly, a functional observer with finite‐time gain is constructed for the nonlinear switched descriptor system. Then, based on the proposed finite‐time functional observer, the reachability analysis is applied to design a finite‐time functional interval observer. Finally, the superiority and effectiveness of the interval estimation method presented in this paper are verified through a simulation of the circuit system.

Construction error control method of large-span spatial structures based on digital twin

In the process of construction of large-span spatial structures, there are many control elements, which need to carry on the dual control of internal force and shape. The traditional control method cannot measure the construction deviation completely and accurately, and there is a lag in information feedback. In this paper, spatio-temporal information is integrated into digital twin and a construction error control method is proposed for large-span spatial structures. By analyzing the fusion mechanism of spatio-temporal information and digital twin, a closed-loop control system for structural errors before, during and after construction is formed. Driven by the control architecture, the method of structure simulation analysis and real-time monitoring during construction is proposed, and the functional relationship between cable force and cable length is established. Based on point cloud data, construction error optimization detection method is proposed for structure formation state. The nonlinear relation between elevation change rate and cable force deviation is formed and the control measures of formation state error are obtained. The experimental model of cable truss structure is taken as an example to apply and verify the theoretical method. The construction error control twin platform is formed in the experiment. Driven by the platform, the twinning analysis and real-time monitoring of the construction process are carried out to control the error of the structure formation. The results show that this method integrates spatio-temporal information into digital twin to realize data integration processing. The optimized error detection method can save 30% time cost and effectively control the construction error within 3%. The integration of spatio-temporal information and digital twin provides theoretical and algorithm support for the intelligent management and control of large-span spatial structure construction.

Enhanced Accuracy in State-of-Charge Estimation for Lithium-Ion Batteries in Electric Vehicles Using Augmented Adaptive Extended Kalman Filter

Accurate assessment of state of charge (SoC) is crucial for the safety and efficiency of lithium-ion batteries used in electric vehicles (EVs). For precise SoC estimation in EV BMSs, an Augmented Adaptive Extended Kalman Filter (AAEKF) has been proposed in this research. To improve the precision of SoC estimation, AAEKF approach combines Kalman filtering methods with adaptive robust control concepts. The effectiveness of the AAEKF algorithm is shown by simulation results and confirmed by comparison with data from LiFePO4 battery research. The proposed AAEKF SoC estimation matches measurement results with errors of less than 3%. Adaptive Extended Kalman Filter (AEKF) is quite sensitive to the accuracy of the battery model, as errors in this region can lead to large estimate errors. Faster convergence to actual SoC, improved real-time responsiveness, accuracy, and resilience to disturbances and parameter fluctuations are all results of AAEKF's efficient handling of nonlinearities in battery systems, adaptation to system changes, errors (Root Mean Square Error (RMSE) and Mean Absolute Error (MAE), and overall performance. The maximum estimate error stays under 5% at various external temperatures.

Design of an ultrasonic flowmeter using a cow horn-shaped structure and secretary bird optimization algorithm-back propagation neural network algorithm

A novel dual-channel ultrasonic flowmeter based on the time-difference method is proposed, aiming at solving the measurement error due to the installation angle of the transducers and improving the measurement accuracy. The angle error is eliminated by optimizing the ultrasonic propagation path so that it is parallel to the fluid flow direction. The pipeline design is optimized to reduce the pressure loss to ensure high-precision measurements at different flow rates. In addition, in order to solve the measurement accuracy problem caused by the transducer position, the measurement results of the two channels are fused by Secretary Bird Optimization Algorithm-Back Propagation Neural network, which reduces the error of the measurements and improves the overall accuracy of the measurements. The results of system error analysis and uncertainty evaluation show that the calibrated flowmeter has a maximum relative error of 0.6% and a maximum repeatability of 0.7%, which proves its reliability and effectiveness in fluid measurement.

Formation tracking control for networked heterogeneous MSV systems with actuator faults: A distributed optimal event-triggered fixed-time sliding mode fault-tolerant control approach

This paper studies the formation tracking problem in networked heterogeneous marine surface vessel systems (NHMSVs) subject to model perturbations, marine environmental disturbances, and actuator faults. A distributed optimal event-triggered fixed-time sliding mode fault-tolerant control (EFSMFC) method is proposed, which can achieve formation tracking within a fixed time that is only related to the control parameters. At the same time, a distance-based Floyd distributed optimization method is used to reduce the high communication cost caused by the redundancy of the network communication topology. In addition, an event-triggered strategy is integrated into the design of the controller to reduce the update frequency of control signals and reduce waste of communication resources. Finally, using Lyapunov stability theory, the global fixed-time convergence of system errors is proved. The simulation results demonstrate the feasibility of the proposed method, exhibiting superior robustness and speed compared to existing control methods.

Consensus of second-order multi-agent systems based on PIDD-like control protocol with time delay

This paper concentrates on investigating the consensus for general second-order linear multiagent systems subjected to time-varying delay. Initially, a novel PIDD-like consensus control protocol with time-varying delay is designed, which includes information on position and its integration, as well as velocity and its differentiation. Subsequently, based upon model transformation, the consensus problem is transformed into the asymptotic stability of the error system. Through constructing of a novel augmented vector Lyapunov-Krasovskii functional, including delay-product terms and multiple integral terms, and then by adopting auxiliary-function-based integral inequalities and the delay-dependent-matrix-based reciprocally convex inequality to address the derivative of the constructed functional, the less conservative consensus conditions for multi-agent systems are obtained. Additionally, numerical simulations are given to demonstrate that the presented consensus conditions not only ensure the consensus of position and velocity states but also exhibit better dynamic performance.

Virtual airways heatmaps to optimize point of entry location in lung biopsy planning systems.

We present a virtual model to optimize point of entry (POE) in lung biopsy planning systems. Our model allows to compute the quality of a biopsy sample taken from potential POE, taking into account the margin of error that arises from discrepancies between the orientation in the planning simulation and the actual orientation during the operation. Additionally, the study examines the impact of the characteristics of the lesion. The quality of the biopsy is given by a heatmap projected onto the skeleton of a patient-specific model of airways. The skeleton provides a 3D representation of airways structure, while the heatmap intensity represents the potential amount of tissue that it could be extracted from each POE. This amount of tissue is determined by the intersection of the lesion with a cone that represents the uncertainty area in the introduction of biopsy instruments. The cone, lesion, and skeleton are modelled as graphical objects that define a 3D scene of the intervention. We have simulated different settings of the intervention scene from a single anatomy extracted from a CT scan and two lesions with regular and irregular shapes. The different scenarios are simulated by systematic rotation of each lesion placed at different distances from airways. Analysis of the heatmaps for the different settings shows a strong impact of lesion orientation for irregular shape and the distance for both shapes. The proposed heatmaps help to visually assess the optimal POE and identify whether multiple optimal POEs exist in different zones of the bronchi. They also allow us to model the maximum allowable error in navigation systems and study which variables have the greatest influence on the success of the operation. Additionally, they help determine at what point this influence could potentially jeopardize the operation.

Finite-time group consensus for nonlinear heterogeneous fractional-order multi-agent systems with communication disturbances

This paper studies the finite-time group consensus problem of nonlinear heterogeneous fractional-order multi-agent system (FOMAS) with communication disturbances. Without the inter-group balance condition, a class of distributed control protocols is proposed by combining the information of neighbouring agents and their corresponding group consensus goals. By using the Kronecker product and the LMI technique, a linear FOMAS composed of the group consensus error and communication disturbance variables is obtained to describe the original system. Then, by constructing a suitable fractional-order Lyapunov function, sufficient conditions are presented to analyse the stability of the consensus error system in finite time. Moreover, it provides the upper bound of the convergence time for the group consensus. Finally, simulation examples are presented to demonstrate the effectiveness of the proposed scheme and theoretical results.

Real-time Error Compensation Transfer Learning with Echo State Networks for Enhanced Wind Power Prediction

Accurate wind power forecasting is essential for efficient energy management and grid stability, enabling energy providers to balance supply and demand, optimize renewable energy integration, reduce operational costs, and enhance power grid reliability. The Echo State Network (ESN) is widely used for modeling nonlinear dynamic systems due to its simple and rapid training process. However, ESNs can be prone to system errors, leading to inaccurate models when handling high-order nonlinear complexities. To overcome this, we developed the Error Compensation Transfer Learning Echo State Network (ETL-ESN), which combines a computing layer based on ESN and a compensation layer using transfer learning. Our model identifies error auto-correlation as a key factor that increases variance in ESN predictions, and addresses this with an error compensation layer to reduce system errors. We further leverage transfer learning to prevent overfitting within the error domain. Extensive experiments using real-world wind power data demonstrate that the ETL-ESN model reduces training time from 65 s to 2 s compared to LSTM, while lowering MAE by up to 95%. The ETL-ESN achieves a 95% to 98% improvement in prediction accuracy across different turbines compared to traditional models. The code and datasets used in this study are available at GitHub repository https://github.com/zhuyingqin/Error-Transfer-ESN for further research and replication.

Accurately differentiating inflamed and healthy tissue samples by removing errors and noises in depth-resolved polarization parameter images

The fiber-optics-based Mueller matrix polarization sensitive optical coherence tomography (PSOCT) can measure the complete depth-resolved Mueller matrices of biological samples and is a choice for practical clinical applications. To quantitatively interpret the measurement results, the system errors and the detection noise in a fiber-optics-based PSOCT are analyzed, simulated and eliminated. The depth-resolved polarization parameter images of the skin of inflamed and healthy mice are presented to demonstrate that the system has the capability of accurately differentiating between normal and diseased tissues. In addition, the values of the polarization parameters after removing the noise and their changes with depth may be helpful quantitative diagnosis of the diseases and the evaluation of the treatment.

Finite-Time Stability Analysis of a Discrete-Time Generalized Reaction–Diffusion System

This paper delves into a comprehensive analysis of a generalized impulsive discrete reaction–diffusion system under periodic boundary conditions. It investigates the behavior of reactant concentrations through a model governed by partial differential equations (PDEs) incorporating both diffusion mechanisms and nonlinear interactions. By employing finite difference methods for discretization, this study retains the core dynamics of the continuous model, extending into a discrete framework with impulse moments and time delays. This approach facilitates the exploration of finite-time stability (FTS) and dynamic convergence of the error system, offering robust insights into the conditions necessary for achieving equilibrium states. Numerical simulations are presented, focusing on the Lengyel–Epstein (LE) and Degn–Harrison (DH) models, which, respectively, represent the chlorite–iodide–malonic acid (CIMA) reaction and bacterial respiration in Klebsiella. Stability analysis is conducted using Matlab’s LMI toolbox, confirming FTS at equilibrium under specific conditions. The simulations showcase the capacity of the discrete model to emulate continuous dynamics, providing a validated computational approach to studying reaction-diffusion systems in chemical and biological contexts. This research underscores the utility of impulsive discrete reaction-diffusion models for capturing complex diffusion–reaction interactions and advancing applications in reaction kinetics and biological systems.

A Single-Tube, Single-Enzyme Clustered Regularly Interspaced Short Palindromic Repeats System (UNISON) with Internal Controls for Accurate Nucleic Acid Detection.

Clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated (Cas) proteins have been widely applied in molecular diagnostics. Unlike the Ct value quantification method of PCR, the CRISPR system mainly relies on the rise of the rate of the fluorescence signal to indicate the concentration of the target nucleic acid, which is susceptible to system errors caused by various factors, such as reaction conditions and instrument performance. Therefore, establishing internal controls is essential to improve the accuracy, reliability, and commercial feasibility of the CRISPR system. However, the nonspecific trans-cleavage activity of Cas proteins presents a challenge in establishing internal controls. In this study, we developed unified nucleic acid detection with a single-tube, one-enzyme system (UNISON) for accurate nucleic acid detection with internal controls. By extending the crRNA and modifying it with different fluorophores and quenchers, we achieved that the specific target can only specifically cleave the corresponding folded crRNA and generate a corresponding fluorescence signal. With this design, we established an internal control, achieving accurate and reliable detection of clinical samples of the hepatitis B virus. Integrating internal controls into the CRISPR/Cas system demonstrates significant potential in medical diagnostics and virus monitoring.

Data-Driven Tracking Control of a Cushion Robot with Safe Autonomous Motion Considering Human-Machine Interaction Environment

Abstract This study proposes a data-driven safety controller with velocity constraints for a cushion robot. We constructed a mathematical description of the human-machine interaction environment by decomposing the generalized input force and coefficient matrix in the dynamic model. We established a new equivalent data model, which considered various human-machine interaction environments using a pseudo-Jacobian matrix. A stochastic configuration network (SCN) estimation method for variations in the human-machine interaction environment was proposed, with hidden layer nodes added at random. We designed a safe autonomous navigation path and proposed a data-driven control method that limited the actual velocity while stabilizing the tracking error system. In addition, the desired motion velocity of the robot was designed. This approach has the advantage of ensuring safety at a specified velocity. We also demonstrated and validated the effectiveness of the proposed data-driven algorithm using simulation-based comparative analysis and an experimental study.

Research on nonlinear second-order multi-agent systems containment control method with inherent time-delay based on compensator

In this study, a nonlinear second-order multi-agent systems containment control method based on compensator is discussed. In order to deal with nonlinear systems with inherent time delay, a new compensator-based control algorithm is proposed. The fully distributed compensator is designed to effectively converge the states of all followers to the convex hull formed by the states of leaders. By introducing a compensator, the errors in the system can be corrected and the influence of time delay on the system can be compensated to ensure that the system can achieve the desired performance and stability. Then, the original system is transformed into an error system by using matrix theory, and the stability of the error system is analyzed by using Lyapunov method, and the sufficient conditions for the solvability of the control problem are obtained. Finally, the effectiveness of the control algorithm is verified by matlab simulation.