Error System Research Articles

Measurement error in hydrostatic leveling system due to temperature effect and their reduction method.

Temperature is a crucial factor influencing the accuracy of the hydrostatic leveling system (HLS), necessitating a temperature compensation test for HLS. This paper investigates HLS temperature compensation through theoretical correction and experimental verification. The influence of temperature on the accuracy of hydrostatic level products is determined through temperature tests on 34 hydrostatic level products. The optimal temperature compensation formula is derived using the non-linear curve fitting method. The HLS is enhanced with a temperature compensation algorithm and temperature sensor, resulting in a new, high-precision, and high-stability hydrostatic leveling product with temperature compensation. Experimental findings reveal that the stability of the improved hydrostatic leveling product exceeds 30% compared to products on the market and surpasses 70% compared to the original products.

Event-Based Nonsingular Fixed-Time Tracking Control of an Uncertain Manipulator System Subject to Full-State Static Constraints.

This article centers around investigating the event-triggered nonsingular fixed-time tracking issue for an n -link rigid robot manipulator with full-state constraints, external disturbances, and model uncertainties. We propose the definition of the constrainedly practically fixed-time stability (CPFTS) and provide a sufficient condition for CPFTS. A novel auxiliary function is developed to address the singularity issue caused by repeated differentiation in achieving the fixed-time tracking control. The uncertain parameters are approximated using the radial basis function neural network (RBFNN). This study proposes the model-based and the neutral network-based tracking control approaches, designed using the scaling function technique and the barrier Lyapunov function, respectively, to ensure that the tracking error systems are CPFTS and the full-state constraints comply. Moreover, the communication transmission load is reduced using the relative threshold event-triggered control strategy. Simulation results demonstrate the effectiveness of the proposed tracking control algorithms.

Kontrol Motor Berbasis PID untuk Pompa Air di Miniatur Rumah Kaca

Greenhouses are crucial in agriculture for enhancing crop yields by providing controlled environmental conditions. Efficient water management is a key challenge in conventional greenhouse systems, often leading to water wastage due to unmeasured soil moisture levels. This research aimed to design and implement a PID control system for water pumps in a miniature greenhouse to optimize water usage and maintain stable soil moisture and temperature levels. The methodology involved integrating PID control with sensors to monitor and adjust the water pump operation based on real-time soil moisture and temperature data. The study found that the PID-controlled system reduced the time to reach steady-state conditions from 650 ms to 600 ms, with no overshoot compared to systems without control. Additionally, the error in the PID system ranged from 0.89% to 2.32%. These findings suggest that PID control significantly enhances the efficiency and effectiveness of water usage in greenhouses, contributing to better crop management and resource conservation. The implications of this research are substantial for agricultural practices, as it demonstrates a viable solution for improving irrigation precision and overall greenhouse management.

Public Sector Innovation Through Web Marriage Management Information System

Improving the quality of public services is carried out by the government, one of which is by creating public service innovations. SIMKAH (Nikah Management Information System) is present as a public service innovation related to marriage registration services at the website-based District KUA. The research method carried out at the Office of Religious Affairs (KUA) of Tanjung Morawa District, Deli Serdang Regency using a qualitative descriptive approach with 5 variables including Relative Advantage, Compatibility, Complexity, Triability and Observability. The results showed that the SIMKAH is an update of the marriage registration service which was originally manual to online so that the service delivery becomes effective and efficient. This can be seen from the five indicators in the theory of innovation attributes, only in the complexity indicator that there are still obstacles, namely technical problems such as system error network problems and lack of human resources who specifically manage this application, as well as the complexity that can be seen from the understanding and ability of the community to access online services that cannot be generalized. So that improvements are needed from the central system of the Ministry of Religion and socialization to the community regarding the use of SIMKAH innovations in marriage registration services. Data uniformity and integrated data backup in marriage registration services is very necessary to provide solutions to existing problems with the previous service system.

Anomaly-based error and intrusion detection in tabular data: No DNN outperforms tree-based classifiers

Recent years have seen a growing involvement of researchers and practitioners in crafting Deep Neural Networks (DNNs) that seem to outperform existing machine learning approaches for solving classification problems as anomaly-based error and intrusion detection. Undoubtedly, classifiers may be very diverse among themselves, and choosing one or another is typically due to the specific task and target system. Designing and training the optimal tabular data classifier requires extensive experimentation, sensitivity analyses, big datasets, and domain-specific knowledge that may not be available at will or considered a non-strategical asset by many companies and stakeholders. This paper compares, using a total of 23 public datasets: i) traditional (tree-based, statistical) supervised classifiers, ii) DNNs that are specifically designed for classifying tabular data, iii) DNNs for image classification that are applied to tabular data after converting data points into images, alone and as ensembles. Experimental results and related discussions show clear advantages in adopting tree-based classifiers for anomaly-based error and intrusion detection in tabular data as they outperform their competitors, including DNNs. Then, individual classifiers are compared against ensembles using different combinations of the classifiers considered in this study as base-learners, providing a unified final response through many meta-learning strategies. Results show that there is no benefit in building ensembles instead of using a tree-based classifier as Random Forests, eXtreme Gradient Boosting or Extra Trees. The paper concludes that anomaly-based error and intrusion detectors for critical systems should use the old (but gold) tree-based classifiers, which are also easier to fine-tune, and understand; plus, they require less time and resources to learn their model.

Quadrotor height control system using LQR and recurrent artificial neural networks

The quadorotor is a type of unmanned flying vehicle known as Unmanned Aerial Vehicle (UAV). In recent years, quadrotors have attracted much attention from researchers around the world due to their excellent maneuverability. A good control system in this quadrotor system is needed for ease of use of this quadrotor. One control system that is often used is the Linear Quadratic Regulator (LQR) control system. This control system has challenges for dynamic system disturbances in quadrotor control. Researchers proposed a recurrent artificial neural network (RNN) system to address these challenges.RRN is used to change the value of the feedback component in the LQR control system. The nature of the feedback component in LQR, which is static, is changed based on the system error value based on changes in the error value entered into the RNN. The result of this RNN is a change in the value of the LQR feedback component based on the input of the system. The results of this research show that LQR control with RNN produces a faster system response of 0.075 seconds and a faster settling time of 0.221 seconds. Compensation for the system response speed produces a higher overshot value.

Local exponential synchronization rate of commutative Kuramoto oscillators on spheres

For proportional Kuramoto oscillators on spheres, the local exponential synchronization rate for complete phase synchronization was obtained in existing literature. But for the general nonidentical Kuramoto oscillators on spheres, the problem of local exponential synchronization rate has not been solved. In this paper, for commutative Kuramoto oscillators on spheres, the matrix differential equations involving the synchronization errors are constructed, and the local exponential synchronization rate is obtained by calculating the spectrum of the linearized error system limited to an invariant subspace.

Perlindungan Hukum Bagi Para Pihak Terhadap Terjadinya Sistem Error Pada Penyelenggaraan M-Banking

A rapidly developing economy cannot be separated from the support of capital provided through banking credit. However, system errors in the operation of M-banking can cause losses for both customers and banks. This study aims to analyze the legal protection for parties against system errors in M-banking operations and the responsibilities that must be met. The research method used is normative legal research with a statutory approach and conceptual approach. The legal sources used include primary and secondary legal sources, collected through literature study and analyzed using descriptive analytical methods. The results of the study indicate that although there are regulations governing the responsibilities of M-banking service providers, legal protection for customers needs to be strengthened, particularly in terms of dispute resolution mechanisms and compensation. This research recommends enhancing system security and consumer education to minimize risks and the establishment of more specific regulations regarding the responsibilities of M-banking providers in dealing with system errors.

[formula omitted] filtering for uncertain discrete-time singular switched positive systems with time delay and output quantization

This paper focuses on the design problem of an l1 filter for singular switched positive systems with time delay and uncertainty. In order to enhance resource efficiency and information transmission security, the quantization mechanism is introduced when designing the filter. Furthermore, sufficient conditions for causality, regularity, positivity, and exponential stability of the filtering error system are provided under the constraint of mode-dependent minimum dwell time (MDMDT). These conditions are obtained by selecting an appropriate co-positive Lyapunov function and are presented in the form of linear programming (LP). Subsequently, the disturbance attenuation performance is further analyzed, and a design methodology for the corresponding filter is outlined. Finally, a numerical example is presented to verify the validity of the theoretical results.

Integral reinforcement learning-based event-triggered optimal tracking control for modular robot manipulators via non-zero-sum game

Abstract Under an event-triggered mechanism, a non-zero-sum (NZS) game optimal tracking control method for modular robot manipulator (MRM) systems with input constraints is proposed using the adaptive dynamic programming (ADP) method based on integral reinforcement learning (IRL). First, a dynamic model of the MRM system is developed based on joint torque feedback technology, consisting of an n-joint subsystem related to interconnected dynamic coupling (IDC). Second, we design a robust compensation controller to handle the known model term and an optimal compensation controller to deal with the uncertainty term caused by the IDC and friction, respectively. In addition, a nonlinear disturbance observer is established to dispose of the negative effects caused by the uncertain sensor output disturbance. Third, based on differential game theory, we transform the optimal tracking control problem of the MRM system into an n-player NZS game problem. Then, the IRL-based ADP method is adopted, which relaxes the need for system partial unknown dynamic information, and only a critic neural network is used to solve the coupled Hamilton–Jacobi equation, so as to obtain the optimal control policy. Then, using Lyapunov theory, the tracking error of the MRM system is demonstrated to be uniformly ultimately bounded. Finally, the effectiveness and superiority of the proposed algorithm are verified through experiments.

Global-in-time error estimates of non-relativistic limits for Euler–Maxwell system near non-constant equilibrium

It was proved that Euler–Maxwell systems converge globally-in-time to Euler–Poisson systems near non-constant equilibrium states when the speed of light c→∞. In this paper, we establish the global-in-time error estimates between smooth solutions of Euler–Maxwell systems and those of Euler–Poisson systems near non-constant equilibrium states. The main difficulty lies in the singularity of the error variable for the electric field E, so that more careful estimates for the time derivatives of error variables should be established. The proof takes good advantage of the anti-symmetric structure of the error system and an induction argument on the order of the derivatives.

Adaptive Sliding Mode Fixed-/Preassigned-Time Synchronization of Stochastic Memristive Neural Networks with Mixed-Delays

The paper addresses the fixed-/preassigned-time synchronization of stochastic memristive neural networks (MNNs) with uncertain parameters and mixed delays. Adaptive sliding mode control (ASMC) technology is mainly utilized. First, a proper sliding surface is constructed and the adaptive laws are given. Also, the synchronization control scheme is designed, which can ensure error system to realize fixed-time stability. Second, preassigned-time sliding mode control scheme is mainly provided to realize fast synchronization of MNNs. The presented theoretical methods can guarantee the error system convergence and stability for reaching and sliding mode within preassigned-time. And the synchronization criteria and explicit expression of settling time (ST) are acquired, where ST is not related with initial values and controller parameters but can be predefined perferentially. Finally, the calculation example is offered to interpret the practicability and availability of the innovations in this paper.

A Novel Simulation Method for 3D Digital-Image Correlation: Combining Virtual Stereo Vision and Image Super-Resolution Reconstruction.

3D digital-image correlation (3D-DIC) is a non-contact optical technique for full-field shape, displacement, and deformation measurement. Given the high experimental hardware costs associated with 3D-DIC, the development of high-fidelity 3D-DIC simulations holds significant value. However, existing research on 3D-DIC simulation was mainly carried out through the generation of random speckle images. This study innovatively proposes a complete 3D-DIC simulation method involving optical simulation and mechanical simulation and integrating 3D-DIC, virtual stereo vision, and image super-resolution reconstruction technology. Virtual stereo vision can reduce hardware costs and eliminate camera-synchronization errors. Image super-resolution reconstruction can compensate for the decrease in precision caused by image-resolution loss. An array of software tools such as ANSYS SPEOS 2024R1, ZEMAX 2024R1, MECHANICAL 2024R1, and MULTIDIC v1.1.0 are used to implement this simulation. Measurement systems based on stereo vision and virtual stereo vision were built and tested for use in 3D-DIC. The results of the simulation experiment show that when the synchronization error of the basic stereo-vision system (BSS) is within 10-3 time steps, the reconstruction error is within 0.005 mm and the accuracy of the virtual stereo-vision system is between the BSS's synchronization error of 10-7 and 10-6 time steps. In addition, after image super-resolution reconstruction technology is applied, the reconstruction error will be reduced to within 0.002 mm. The simulation method proposed in this study can provide a novel research path for existing researchers in the field while also offering the opportunity for researchers without access to costly hardware to participate in related research.

Environmental information-assisted intelligent fusion localization for vehicles in urban area

In this paper, we propose a localization methodology aimed at improving accuracy through two primary aspects: environment information assisting fusion algorithm and the elaborate localization errors prediction. First, the environment-assisted particle filter (EA-PF) is proposed by enhancing the particle filter algorithm with perceived environmental feature information, which is reflected by the rasterized map constructed based on LiDAR sensors. Then, an elaborate localization error prediction model is designed to accurately forecast Inertial Navigation System (INS) errors. The model comprises the noise characteristic feature extraction and encoding network and multi-feature error prediction network. The localization errors in the EA-PF output can be further compensated. The proposed localization methodology can be divided into two operational modes based on the availability of Global Navigation Satellite System (GNSS) signal: GNSS-available and GNSS-unavailable modes. In the GNSS-available mode, the EA-PF algorithm yields precise localization results, while in the GNSS-unavailable modes, the elaborate localization error prediction model can effectively predict INS errors and provide the compensation for the EA-PF. To validate the effectiveness of the proposed methodology, relevant test experiments were performed using the KITTI dataset. The experimental validation results demonstrate the feasibility and effectiveness of the proposed methodology. The experimental results demonstrate that the EA-PF outperforms traditional PF methods by improving the root mean square error (RMS.) by 32.70 % on average during 90 s GNSS outages. Moreover, the proposed localization methodology achieves 2.30 m RMS error on average during 90 s GNSS outage.

Equation-free data-driven synchronization of rod-type plasma torch systems with a Koopman approach

This study explores equation-free data-driven synchronization of a rod-type plasma torch by employing the Koopman approach. In the data-driven methodology, we systematically extract information from driving-driven systems at fixed time intervals within a specific range. Utilizing the data, the Koopman operator is applied to attain observables for both driving and driven systems. Parameter estimation is employed to construct a flexible model capable of capturing subtle changes. The error system is derived from these models, and parameter optimization is performed to achieve synchronization. Leveraging Julia packages, system synchronization is achieved by dynamically controlling parameters, providing insights without relying on explicit equations.

Research on an Ice Tolerance Control Method for Large Aircraft Based on Adaptive Dynamic Inversion

Considering the effect of icing on aircraft control performance, this paper proposes an adaptive dynamic inverse ice tolerance control method based on piecewise constant. A control allocation algorithm is introduced to compensate for the change of control surface performance caused by icing. This method can achieve satisfactory disturbance estimation accuracy under a given sampling time, and thus ensure a closed-loop system error within an acceptable range. The proposed design method is applied to the design of a flight control law for a transport aircraft, aiming to solve the problem of ice-tolerant flight control, reduce the influence of icing conditions on controllability and safe flight of the transport aircraft, and thus improve the flight quality of the transport aircraft. The simulation results are verified under the influence of both standby ice type and failure ice type, and the interference effect on aircraft aerodynamic parameters is further added. The simulation results show that adaptive dynamic inverse control based on piecewise constant can overcome the influence caused by icing and aerodynamic parameter interference, achieve accurate tracking of command, and provide excellent fault tolerance and robustness, which ensures that the transport aircraft can achieve the desired control performance and safe flight capability.

Stochastic analysis of drift error of gyroscope in the single-axis attitude determination

Understanding and mitigating the various stochastic errors in gyroscope measurements is crucial for accurate attitude determination. White noise and bias instability can introduce drift errors, reducing the system’s accuracy. Efficient sensor selection and algorithm design necessitate a thorough understanding of these influences and how they propagate throughout the system. This paper addresses the propagation of significant stochastic errors in the attitude determination system, including white noise (angle random walk), bias instability, rate random walk, and correlated noise. The paper discusses the modeling strategy and statistical characteristics of these error contributors. It then evaluates the variance propagation of these errors within the output of a single-axis attitude determination system. The presented analysis calculates and compares the impact of each source of error on the system’s precision over time. Experiments with the MPU6050 and L3G4200D sensors are conducted to validate the analytical results.

Geometric tracking control of a multi‐rotor UAV for partially known trajectories

AbstractThis article presents a trajectory‐tracking controller for multi‐rotor unmanned aerial vehicles (UAVs) in scenarios where only the desired position and heading are known without the higher‐order derivatives. The proposed solution modifies the state‐of‐the‐art geometric controller, effectively addressing challenges related to the non‐existence of the desired attitude and ensuring positive total thrust input for all time. We tackle the additional challenge of the non‐availability of the higher derivatives of the trajectory by introducing novel nonlinear filter structures. We formalize theoretically the effect of these filter structures on the system error dynamics. Subsequently, through a rigorous theoretical analysis, we demonstrate that the proposed controller leads to uniformly ultimately bounded system error dynamics. To demonstrate the controller's effectiveness and potential to enhance multi‐rotor performance and reliability in practical applications, we present a simulation study considering two examples with time‐varying trajectories.

Research on Calculation Method of On-Orbit Instrumental Line Shape Function for the Greenhouse Gases Monitoring Instrument on the GaoFen-5B Satellite

The Greenhouse Gases Monitoring Instrument is based on the spectroscopic principle of spatial heterodyne spectroscopy technology and has the characteristics of no moving parts, a hyperspectral resolution, and a large luminous flux. The instrumental line shape function is one of the most important parameters characterizing the features of the instrument, and it plays a vital role in the system error analysis of the instrument’s measurements. To accurately obtain the instrumental line shape function of a spatial heterodyne spectrometer during the on-orbit period and improve the accuracy of gas concentration retrieval, this study develops a method to model and characterize the characteristics of the instrumental line shape function, including modulation loss and phase error. This study employs the solar calibration spectrum in the 1.568–1.583 μm bands to conduct iterative calculations of the instrumental line shape function error model. After the instrumental line function is updated, the average relative deviation is reduced from 1.83% to 0.756% between the theoretical and measured solar spectra. Additionally, the average relative deviation is reduced from 7.049% to 2.106% between the GMI nadir and theoretical nadir spectra. The findings demonstrate that updating the instrumental line shape function mitigates the impact of variations in the spectrometer’s instrumental line shape due to alterations in the orbital environment. This study offers a dependable reference for both the enhancement and oversight of a spectrometer’s instrumental line shape function, along with an investigation of shifts in instrument parameters.

Energy-to-peak filtering for uncertain discrete-time singular bilinear systems with multipath data packet dropouts

In this paper, the energy-to-peak filtering problem for the uncertain discrete-time singular bilinear systems is studied. This paper considers the uncertainties that consist of real systems and the possible data packet dropouts when performance output signals and measurement output signals are transmitted over digital channels. In the course of the study, the phenomenon is elucidated utilising the Bernoulli random binomial distribution. The goal is to design the desired filter, so that the filtering error system is admissible, and meets the specified energy-to-peak performance index. The design conditions of energy-to-peak filters for uncertain discrete-time singular bilinear systems are given by introducing the Lyapunov function and using linear matrix inequalities (LMIs). The impact of uncertainty on singular bilinear systems is addressed using a two-step approach. At last, a numerical illustration is provided to demonstrate the validity of the design.