Error System Research Articles

On the Positive Role of Noise and Error in Complex Systems

Noise and error are usually considered to be disturbances negatively affecting the behavior of a system. Nevertheless, from a systemic perspective, taking into account openness and incompleteness of complex systems, noise and error may assume a creative, constructive, and positive role in that they are a source of novelty that can trigger the reorganization of the system, the growth of complexity, and the emergence of new meaning. Examples of this phenomenon can be found in evolutionary phenomena driven by affordances, the formation of new attractors in dynamic systems responding to external perturbations, and improvisation in music. We argue that it is possible to identify general properties that enable the positive effect of noise and errors in complex systems, namely, multilevel organization, redundancy, incompleteness, and criticality. These properties play a major role in living systems and can guide the design of robust and adaptive artificial systems.

Analysis of the focusing error of the building solar optical fiber lighting system

Abstract At present, the main way to use solar energy still stays in photovoltaic power generation, which has the disadvantages of high cost and low efficiency. With the development and popularization of optical fiber technology, it is possible to use optical fiber to transmit sunlight. However, due to its light attenuation problem, it is only suitable for short distance transmission, such as solar optical fiber lighting systems in buildings. This paper designs and develops a solar optical fiber lighting system that is suitable for building daylighting and is relatively cheap. The system includes a concentrating system, a tracing system, a transmission system, and a remote control system. In this paper, the important problems of focusing errors in the system are analyzed, and five main types of focusing errors are discussed. Taking the diameter of the big end of the light cone as the synthetical systematic error of the focusing, two distribution modes of the systematic error are discussed/ Aiming at the weighted distribution of the system error, the tracking accuracy of the tracking system is discussed. The experiment proves that the required tracking accuracy is obtained by using proportional control and PID control respectively.

Cooperative Adaptive Command Filtered Backstepping Control for EVs to UPS-Microgrid via Virtual Synchronous Generator.

Multiple batteries in uninterruptible power supply (UPS)-microgrid systems based on multiagents composed of multiple electric vehicles (EVs) can encounter state of charge (SoC) consistency problems. To solve this differential expansion and controller saturation problem, an adaptive command filter sliding-mode control strategy based on virtual synchronous generators (VSGs) and considering the power allocation principle is proposed. First, based on directed graph theory, an SoC consistency algorithm and power allocation strategy for multiple EVs were proposed, forming a dc power system with a fixed communication topology. Second, the rotor motion equation of synchronous generator (SG) is introduced into the inverter control algorithm to form the mathematical model of VSG. Third, a low-pass filter (LFP) was introduced in the voltage control process to simulate the excitation attenuation characteristics of the SG. Based on the above, a backstepping control strategy, including a command filter and sliding mode controller is proposed, which improves the operating stability of the system based on the system errors of angle, frequency, and power output. Finally, the UPS-microgrid system based on multiagents is simulated to demonstrate the stability of the system and the effectiveness of the proposed control strategy.

Exponential Boundary Control for 2-D Spatial Distributed Parameter Systems Under Boundary Collocated and Planar Linear Measurements.

For a class of 2-D spatial distributed parameter systems (DPSs) with space-dependent diffusivity, this article aims to achieve exponential realization of their desired profiles. To reduce the number of required sensors and actuators, a planar output feedback boundary control strategy is proposed with combining two nonfull-domain measurement methods, boundary collocated measurement and planar linear measurement, in which only two boundaries of the considered 2-D spatial DPSs are controlled and a little output information is measured. Moreover, by employing the Poincaré-Wirtinger inequality and variable substitution dexterously, the final exponential convergence criteria of the error system can be obtained with method of "Diverse treatment for same term." Finally, we provide a general numerical example and an application example in 2-D heat conduction systems to illustrate the effectiveness and practicability of the proposed measurement and control schemes.

Finite-time trajectory tracking control for unmanned ground vehicle based on finite-time disturbance observer

This paper studies the trajectory tracking problems of unmanned ground vehicle with model uncertainties and external disturbances. We propose a finite-time control method, in which the uncertainty and disturbance compensating is considered. To match the real motion scenarios of vehicles, the Frenet-coordinate system is used and the error dynamics equation is constructed. A new type of coordinate transformation is adopted to simplify the calculation of finite-time stability. Further, we design a new finite-time disturbance observer to deal with the model uncertainties and external disturbances. Based on this observer, the finite-time trajectory tracking controller is constructed by using a new integral-type Lyapunov function. It is theoretically proved that all tracking errors in the closed-loop system are finite-time stable. To verify the feasibility, simulation on the unmanned ground vehicle has been conducted.

Assessing Driver’s Trust, Compliance, and Reliance in an Automated Flood Warning System: Effects of Error Types and System Reliability

Assessing Driver’s Trust, Compliance, and Reliance in an Automated Flood Warning System: Effects of Error Types and System Reliability

Putting error bars on density functional theory

Predicting the error in density functional theory (DFT) calculations due to the choice of exchange–correlation (XC) functional is crucial to the success of DFT, but currently, there are limited options to estimate this a priori. This is particularly important for high-throughput screening of new materials. In this work, the structure and elastic properties of binary and ternary oxides are computed using four XC functionals: LDA, PBE-GGA, PBEsol, and vdW-DF with C09 exchange. To analyze the systemic errors inherent to each XC functional, we employed materials informatics methods to predict the expected errors. The predicted errors were also used to better the DFT-predicted lattice parameters. Our results emphasize the link between the computed errors and the electron density and hybridization errors of a functional. In essence, these results provide “error bars” for choosing a functional for the creation of high-accuracy, high-throughput datasets as well as avenues for the development of XC functionals with enhanced performance, thereby enabling the accelerated discovery and design of new materials.

Finite‐time H∞ filtering for Markov jump systems with partially unknown transition rates

SummaryThe thesis studies a stochastic finite‐time bounded (SFTB) filter design method for time‐varying delay Markov jump systems (MJSs) with partially unknown transition rates. First, a Lyapunov–Krasovskii functional with triple integral is built, the free weight matrix is added for lowering the conservatism. And the condition of SFTB of the error system is analyzed. Then, according to linear matrix inequalities (LMIs), a new filter design method is presented. Moreover, the dimension of the filter obtained is free, and the filter ensures that the error system is SFTB. Finally, the validity and effectiveness of the conclusion of this paper are demonstrated by simulation examples.

Fuzzy disturbance observer-based fuzzy swing suppression control of a ‘mooring-heavy lift crane-cargo’ coupled system

In this paper, an adaptive fuzzy disturbance observer (FDO)-based control is proposed for the swing suppression of the mooring-heavy lift crane (HLC)-cargo system in the presence of a wave disturbance. First, the dynamic model of the HLC system is determined by employing the Lagrangian method, and the wave force is modeled as an exosystem with unknown terms based on the Jonswap spectrum. Then, based on the HLC model and the wave force exosystem, an FDO is established to determine the wave disturbances, and a fuzzy approximator is developed to estimate the unknown terms. A novel disturbance estimation error observer is first developed to facilitate the parameter adaptive updating law. Subsequently, by augmenting the HLC system and disturbance estimation error system, an FDO-based fuzzy antiswing control method is proposed in terms of the linear matrix inequality technique to suppress the swing. The closed-loop system stability is examined by using the Lyapunov method. Finally, the effectiveness of the proposed method is validated by numerical simulation.

Ultraviolet positioning via TDOA: error analysis and system prototype

Ultraviolet positioning via TDOA: error analysis and system prototype

The IoT Integrated Electric Vehicle Fire Detection System: Case Study of IMEI TEAM UMSIDA

Abstract—In the modern era, electric vehicle technology is rapidly developing and emerging as a more environmentally friendly option than fossil fuel vehicles. Despite its many advantages, electric vehicles face safety concerns, especially fire hazards caused by problems with the battery, electrical system, or human error. Fires in electric vehicles can occur due to problems with the lithium-ion battery, accidents, or improper maintenance. Although rare, this risk needs to be considered by following maintenance and safety guidelines. Fire detection systems in electric vehicles are designed to detect and respond to potential fires, provide early warning, and take preventive or emergency action. The system uses NodeMCU esp8266 as the microcontroller, which is connected with a smartphone via WiFi. Data from the MQ2 sensor and the fire sensor are sent and recorded on the IoT platform on the smartphone. This tool is able to detect fires in real-time, but unstable internet or WiFi quality can affect data transmission. Thus, this system is expected to increase safety and reduce the risk of fire in electric vehicles, so that people feel safer and more confident in using this environmentally friendly technology. Keywords— Electric Vehicles; Fie Detector; Monitoring

Event‐triggered tracking control for networked control systems under diversified attacks

AbstractThis paper mainly concerns on the tracking control problem for event‐triggered networked control systems (NCSs) with diversified cyber‐attacks. An event‐triggered mechanism (ETM) is exploited to decrease the amount of redundant transmissions, thus saving communication resources. In addition, a novel tracking error model for NCSs is built with consideration of ETM and diversified cyber‐attacks, which involves deception attacks and Denial‐of‐Service (DoS) attacks. On the basis of Lyapunov stability theory and linear matrix inequalities (LMIs), the stability criterion of the tracking error system is constructed, and the controller is designed to ensure the tracking performance of the system. In the end, simulation examples are provided to testify the validity of the proposed method.

A matrix-separation-based integral inequality for aperiodic sampled-data synchronization of delayed neural networks considering communication delay

This paper achieves the synchronization of delayed neural networks (DNNs) considering aperiodic sampled-data control and communication delay. First of all, based on the master-slave DNNs with aperiodic sampling synchronization controller, a synchronization error system is constructed. Then, an augmented functional containing both the error state and its derivative is constructed. Compared with the existing researches, the augmented functional introduces more cross information of error states to the criterion. Next, an integral inequality based on the separation of internal integral variable and matrix is developed. Compared to the inequalities that treat the internal variable and the matrix as unified ones, the developed inequality provides a tighter estimate of the derivative of the augmented functional. On this basis, a criterion with less conservative is developed for the aperiodic sampled-data synchronization of DNNs considering communication delay. Finally, to indicate the superiority of the developed method on improving the acceptable sampling upper bound of synchronization, three numerical examples are provided.

Preview Control for Cyber–Physical Systems under Periodic Denial-of-Service Attacks

In this paper, the preview control problem for cyber–physical systems (CPSs) under denial-of-service (DOS) attacks is studied. First, we employ an attack-tolerant strategy to design an augmented error system (AES) for scenarios where both state and reference signal channels are subject to periodic attacks. We then discuss the stochastic stability conditions for the AES and derive the corresponding controller. Subsequently, the preview controller for the original system is developed. Finally, the effectiveness of the obtained results is demonstrated through a numerical simulation using an unmanned ground vehicle (UGV) model, indicating the practical applicability of the proposed control strategy.

Neural learning control for sampled‐data nonlinear systems based on Euler approximation and first‐order filter

AbstractThe primary focus of this research paper is to explore the realm of dynamic learning in sampled‐data strict‐feedback nonlinear systems (SFNSs) by leveraging the capabilities of radial basis function (RBF) neural networks (NNs) under the framework of adaptive control. First, the exact discrete‐time model of the continuous‐time system is expressed as an Euler strict‐feedback model with a sampling approximation error. We provide the consistency condition that establishes the relationship between the exact model and the Euler model with meticulous detail. Meanwhile, a novel lemma is derived to show the stability condition of a digital first‐order filter. To address the non‐causality issues of SFNSs with sampling approximation error and the input data dimension explosion of NNs, the auxiliary digital first‐order filter and backstepping technology are combined to propose an adaptive neural dynamic surface control (ANDSC) scheme. Such a scheme avoids the ‐step time delays associated with the existing NN updating laws derived by the common ‐step predictor technology. A rigorous recursion method is employed to provide a comprehensive verification of the stability, guaranteeing its overall performance and dependability. Following that, the NN weight error systems are systematically decomposed into a sequence of linear time‐varying subsystems, allowing for a more detailed analysis and understanding. In order to ensure the recurrent nature of the input variables, a recursive design is employed, thereby satisfying the partial persistent excitation condition specifically designed for the RBF NNs. Meanwhile, it can verify that the NN estimated weights converge to their ideal values. Compared with the common ‐step predictor technology, there is no need to redesign the learning rules due to the designed NN weight updating laws without time delays. Subsequently, after capturing and storing the convergence weights, a novel neural learning dynamic surface control (NLDSC) scheme is specifically formulated by leveraging the acquired knowledge. The introduced methodology reduces computational complexity and facilitates practical implementation. Finally, empirical evidence obtained from simulation experiments validates the efficacy and viability of the proposed methodology.

Secure pinning synchronization on aperiodic intermittent event-triggered control in discrete-time complex networks against multi-pattern link attacks

This paper investigates the problem of secure synchronization in aperiodic intermittent event-triggered pinning control for discrete-time complex networks (DCNs) against multi-pattern link attacks. Firstly, in order to reduce communication burden and control cost, a novel aperiodic intermittent event-triggered control (AIEC) with discontinuous characteristics is designed based on periodic sampling, where triggering instants are determined by the intermittent control (IC). Secondly, multiple pattern attack are modeled, as they can interrupt different edges and change the network topology. In addition, multi-pattern attacks for each link are independent and their impact on the coupling topology is analyzed. Thirdly, under destroyed network topology, this paper designs aperiodic intermittent event-triggered pinning control (AIEPC) by combining pinning control (PC) with the AIEC. Meanwhile, based on PC, the isolated node and pinned nodes form a like-directed spanning tree with an asymmetrical coupling matrix, where rooted at the isolated node and only directed connections between the isolated node and pinned nodes. Fourthly, using the segmentation analysis method and the inequality iteration technique, a sufficient condition for exponential synchronization of error system is obtained by considering the instants neighboring different intermittent control and attack intervals. Finally, a simulation example on Chua’s circuit network is provided to verify the validity of the theoretical results achieved in this paper.

On-machine inspection and compensation for thin-walled parts with sculptured surface considering cutting vibration and probe posture

Abstract On-machine inspection has a significant impact on improving high-precision and efficient machining of sculptured surfaces. Due to the lack of machining information and the inability to adapt the parameters to the dynamic cutting conditions, theoretical modeling of profile inspection usually leads to insufficient adaptation, which causes inaccuracy problems. To address the above issues, a novel coupled model for profile inspection is proposed by combining the theoretical model and the data-driven model. The key process is to first realize local feature extraction based on the acquired vibration signals. The hybrid sampling model, which fuses geometric feature terms and vibration feature terms, is modeled by the lever principle. Then, the weight of each feature term is adaptively assigned by a multi-objective multi-verse optimizer. Finally, an inspection error compensation model based on the attention mechanism considering different probe postures is proposed to reduce the impact of pre-travel and radius errors on inspection accuracy. The anisotropy of the probe system error and its influence mechanism on the inspection accuracy are analyzed quantitatively and qualitatively. Compared with the previous models, the proposed hybrid profile inspection model can significantly improve the accuracy and efficiency of on-machine sampling. The proposed compensation model is able to correct the inspection errors with better accuracy. Simulations and experiments demonstrate the feasibility and validity of the proposed methods. The proposed model and corresponding new findings contribute to high-precision and efficient on-machine inspection, and help to understand the coupling mechanism of inspection errors.

Potential for Social Conflicts Due to the Globalization of Digital Money

With the development of technology, digital money is spreading as a means of payment. The method used is qualitative descriptive. The results of the study show that The growth of social media and the adoption of cryptocurrencies marks a digital transformation in the payment system. However, the shift from cash to digital transactions has also had an impact. On the positive side, transactions are more practical and the risk of theft is reduced. However, there is a risk of system errors and potential social instability in the event of major disruptions in the digital system. The Great Reset agenda supports the use of digital money to improve economic efficiency and inclusivity, but it also poses challenges related to national sovereignty and security. Digital education, proper regulation, and international cooperation are key to effectively managing this complex transition.

Implementasi Sistem Pengelolaan Aset Desa (SIPADES) di Kecamatan Bengkalis

This study aims to evaluate the implementation of the Village Asset Management System (SIPADES) in Bengkalis Regency. The focus of the research was to assess the effectiveness of this system in helping village officials manage assets. The research was conducted in six villages using a descriptive qualitative method. Data collection was conducted through reduction, presentation and conclusion drawing. The results showed that villages in Bengkalis Regency had implemented SIPADES well. Village operators, although most of them were only high school graduates, were able to master SIPADES thanks to intensive training. However, the main obstacles are network limitations that cause system errors and downtime, disrupt operational efficiency and stability of village asset management, and make it difficult to track data in real-time. Overcoming these problems requires improved technology infrastructure and better support so that SIPADES can run smoothly and effectively in all villages.

An obstacle avoidance safety detection algorithm for power lines combining binocular vision technology and improved object detection

In this paper, a framework of obstacle avoidance algorithm applied to power line damage safety distance detection is constructed, and its overall architecture and key processes are described in detail. The system design covers three core modules: visual data acquisition and preliminary processing, accurate target recognition and distance measurement, and system error analysis and correction. In the visual data processing chain, we deeply analyze every step from image acquisition to preprocessing to feature extraction, aiming to enhance the adaptability of applications to complex scenes. The target recognition and distance estimation part integrates advanced technology of deep learning to improve the reliability of recognition accuracy and distance estimation. In addition, many common error sources, such as system bias, parallax discontinuity, fluctuation of illumination conditions, etc., are discussed in depth, and corresponding correction strategies are proposed to ensure the accuracy and stability of the system, which provides powerful technical support for achieving efficient and accurate safety monitoring. Specifically, by carefully adjusting the learning rate, convolution kernel size, batch size, pooling layer type, and number of hidden layer nodes, we succeeded in improving the overall accuracy from the initial average of 92.4–95%, and the error rate decreased accordingly.