Synchronization Error Research Articles

The influence of journal's roundness on rotating accuracy of aerostatic spindle and precision improvement by time-controlled grinding

The roundness of the journal directly affects the radial synchronous error (RSE) of the aerostatic spindle. However, the state-of-the-art ultra-precision grinding machine can only achieve a roundness of 0.2–0.3 µm, which limits the improvement of the rotating accuracy. Hence, novel time-controlled grinding (TCG) is proposed to further converge the rotor's roundness. To provide the machining basis and prediction, CFD is used to quantitatively analyze the effect of roundness on the flow field and a rotating trajectory model is established. Based on the model's guidance, the rotor's average roundness converged to 0.08 µm by TCG, even better than manual lapping, and the RSE decreased from ∼30 nm to ∼11 nm synchronously, proving the model's and TCG's feasibility and potential.

Алгоритм совместной оценки и компенсации ошибок временной и частотной синхронизации сигналов с возможностью регулирования точности оценки

An analysis of the influence of synchronization errors on signal processing is presented, a review of existing approaches for estimating and compensating for time and frequency synchronization errors is given, a new approach is proposed that allows joint evaluation of time and frequency offsets, providing a more accurate estimate compared to existing analogues and, moreover, regulating accuracy of fractional error estimation of both time and frequency synchronization.

Алгоритм совместной оценки и компенсации ошибок временной и частотной синхронизации сигналов с возможностью регулирования точности оценки

Представлен анализ влияния ошибок синхронизации на обработку сигнала, дан обзор существующих подходов оценки и компенсации ошибок временной и частотной синхронизации, предложен новый подход, позволяющий совместно оценить смещения по времени и частоте, обеспечить более точную оценку по сравнению с существующими аналогами и, более того, регулировать точность оценки дробной ошибки и временной, и частотной синхронизации.

Fixed-time anti-disturbance constraint fuzzy synchronization control for PMSM based four-wheel-independent-drive EVs

A fixed-time anti-disturbance constraint fuzzy synchronization control method is proposed, for the sake of solving the problem of fast recovery of synchronization between in-wheel permanent magnet synchronous motors (PMSMs) in four-wheel-independent drive electric vehicles. The system model of in-wheel PMSM is presented in the beginning, and fuzzy logic systems are used for approximating the uncertainty parts of the PMSM model. Based on the PMSM model, a fixed-time disturbance-observer is designed to account for fast estimation of fluctuating load torque. Then, the neighborhood synchronization error is constructed based on a directed-graph, and a performance function is designed to guarantee the neighborhood synchronization error evolves strictly within the prescribed bound. Besides, the fixed-time bound of all signals is proved. Finally, the hardware-in-the-loop platform is built, and the electronic differential and the nonlinear vehicle model are employed to validate the advanced performance of the design control method.

Time Delay Characterization in Wireless Sensor Networks for Distributed Measurement Applications

This paper investigates the critical aspect of synchronization in wireless sensor networks (WSNs) across diverse industrial applications. The low-cost sensor network topologies are analyzed. The communication delay measurements and quantitative jitter analysis are performed under different conditions, and dependencies of the propagation time delay on the data bitrate and modulation type for different hardware implementations of the WSNs are presented. The time delay distribution influence on the time synchronization error propagation over WSN layers was assessed from the experimental probability density functions. The network synchronization based on the controlled propagation delay jitter approach has been proposed. This research contributes quantitative insights into the complexities of synchronization in WSNs, offering a foundation for optimizing network configurations and parameters to extend the operational life of low-power sensor nodes.

Neural network based adaptive robust synchronous control of double-lift overhead crane considering input saturation

This paper proposes a novel model-free controller, considering the double-lift overhead crane system cannot be accurately modeled and affected by external disturbances. Firstly, to improve the convergence speed of the system states, a time-varying sliding mode surface using a sigmoid-like function is proposed. Then, an adjustable weights radial basis function neural network (AWRBFNN) is introduced to estimate and compensate for the unknown dynamics of the system. This AWRBFNN introduces direct and effective model-free properties, but also brings approximation errors. To estimate the uncertain disturbances and the errors of the neural network, an adaptive uncertainty and disturbance estimator (AUDE) is designed. Furthermore, a fast hyperbolic power reaching law is developed to enhance the robustness while suppressing chattering. Considering that the driving capacity of the actual double-lift overhead crane system and the driving motor torque are limited, an innovative continuous auxiliary system is designed to mitigate the effects of the input saturation while reducing overshoot from synchronization errors. Finally, the stability of the control system is analyzed using the Lyapunov stability theory, and the effectiveness and robustness of the control scheme are verified by simulation.

Synchronization of angular velocities of chaotic leader-follower satellites using a novel integral terminal sliding mode controller

Synchronization of satellite systems offers numerous advantages, including cost-effectiveness, flexibility, and extended area coverage. However, the presence of chaotic behavior in such systems poses substantial challenges. This paper investigates chaos in satellite systems and proposes a novel approach - the Novel Integral Terminal Sliding Mode (NITSM) controller - specifically designed for synchronizing the angular velocities of chaotic Leader-Follower satellite systems. The NITSM controller leverages limited-time features to eliminate chattering and exhibits remarkable robustness against external disturbances such as cosmic rays and solar storms. MATLAB simulations are conducted to validate the effectiveness of the proposed method, comparing the synchronization error of the NITSM controller with a common approach. The results demonstrate the superior performance of the Novel Integral Terminal Sliding Mode controller, showcasing reduced response time, robust behavior, and a substantial six-fold reduction in angular velocity synchronization error.

Estimation of wind load on supertall buildings using partial output measurements

Supertall buildings possess distinctive characteristics such as high flexibility, low damping, and susceptibility to wind. The impact of the wind load and wind-induced responses plays a critical role in ensuring the safety and functionality of these structures. Consequently, investigating the wind load and wind-induced responses of supertall buildings has immense practical significance and engineering value to guarantee the secure operation of such buildings under strong wind conditions. However, the direct measurement of wind loads and wind-induced responses across all building floors presents substantial challenges, including considerable engineering workload and implementation difficulties. This study proposes a novel approach based on Kalman filtering to estimate wind loads and wind-induced responses in supertall buildings. This approach involves fusing partial acceleration and displacement measurements to estimate wind load and wind-induced response accurately. Additionally, optimized sensor placement was considered based on the iterative process of the effective independence method. Wind-induced responses were obtained through numerical testing and subsequently utilized to estimate the wind loads on a supertall building. Various factors, such as measurement noise, modeling errors of structural dynamic properties, sensor placement, and response synchronization, were thoroughly discussed to assess their impact on the overall analysis.

Synchronization effects in multiplex networks of chaotic maps with memristive interlayer coupling

Models of two-layer multiplex networks of one-dimensional maps with memristive interlayer coupling are studied. In the absence of coupling, the layers exhibit different types of chimera states. Two types of maps are considered as the elements of networks: modified Ricker maps and cubic maps. The effects of complete and partial synchronization of the spatiotemporal dynamics of layers are observed in the case of a homogeneous and inhomogeneous network, respectively. The dependence of the synchronization threshold for the coupling parameter on the initial states of the memristors has been established. This dependence is partially preserved in the case of non-ideal memristors. Diagrams of the synchronization error are constructed on the plane of the initial state variable of memristors and the interlayer coupling coefficient. The effect of noise in memristive coupling elements on synchronization is considered.

Coding–decoding-based synchronization of Markov jump neural networks with PDT switched topologies

This paper investigates the observer-based synchronization control issue for a class of discrete-time switched neural networks under the constraint that information exchange between the observer and controller is subject to coding–decoding communication protocol. The variation of network parameters is governed by a Markov chain while a set of pre-known persistent dwell-time switching sequence is introduced to describe the variation of communication topologies among neurons. To ensure the efficient and secure of information transmission, data received from the observer is encoded into a particular set of codewords before sending to the communication channel. Then, these codewords are transformed into corresponding decoded form via the designed decoder after transmission. The main attention is focused on devising a feasible coding–decoding procedure such that the coding–decoding-based controller can be established efficiently. By constructing suitable Lyapunov function that can reveal the variation characteristic of the network parameters and topologies properly, sufficient conditions which ensure the exponential mean-square boundedness of the closed-loop synchronization error systems are presented. Finally, the validity of the proposed control scheme is illustrated by a numerical example.

Suppression of effects of the synchronization error with adaptive down-conversion for underwater acoustic communication

In underwater acoustic communication, inaccurate synchronization can cause an incorrect estimation of the uniform Doppler shift and degrade the demodulation performance. In this paper, it is mathematically described how the inaccurate synchronization affects the demodulation procedure. Moreover, it is also shown theoretically that adaptive down-conversion, which has been proposed to suppress the effects of the nonuniform Doppler shift, reduces the degradation of the demodulation performance caused by the inaccurate synchronization. Furthermore, the improvement in the demodulation performance by the adaptive down-conversion is evaluated using experimental data. The demodulation results of the addition of intended synchronization errors clearly show that the adaptive down-conversion reduced the effects of the synchronization errors and improved the demodulation performance in the case of inaccurate synchronization.

Submission to Special Issue to Explainable Representation Learning-Based Intelligent Inspection and Maintenance of Complex Systems: Synchronization of Inertial Neural Networks With Unbounded Delays via Sampled-Data Control.

This article addresses the synchronization issue for inertial neural networks (INNs) with heterogeneous time-varying delays and unbounded distributed delays, in which the state quantization is considered. First, by fully considering the delay and sampling time point information, a modified looped-functional is proposed for the synchronization error system. Compared with the existing Lyapunov-Krasovskii functional (LKF), the proposed functional contains the sawtooth structure term V8(t) and the time-varying terms ex(t-βħ (t)) and ey(t-βħ (t)) . Then, the obtained constraints may be further relaxed. Based on the functional and integral inequality, less conservative synchronization criteria are derived as the basis of controller design. In addition, the required quantized sampled-data controller is proposed by solving a set of linear matrix inequalities. Finally, two numerical examples are given to show the effectiveness and superiority of the proposed scheme in this article.

Quantification and mitigation of the effect of resynchronization errors in ultrasound sensor network for passive imaging in elastic plates.

The problem of signal desynchronization in passive imaging based on noise correlation for defect detection in elastic plates is investigated. Although a post-processing resynchronization process relying on the symmetry of noise correlation functions can be applied prior to the imaging algorithm, perfect synchronization might not be achieved experimentally. Effect of such residual synchronization errors on the defect detection performance is quantified as a function of their probability density function. A mathematical regularization process is then proposed to reduce the standard deviation of the resynchronization errors by a factor of N-1/N (N is the number of sensors), which results in a significant improvement in the detection performance. Finally, these theoretical results are validated through a simple flexural-wave propagation simulation.

Nonlinear model predictive control—Cross-coupling control with deep neural network feedforward for multi-hydraulic system synchronization control

This paper studies a multi-hydraulic system (MHS) synchronization control algorithm. Firstly, a general nonlinear asymmetric MHS state space entirety model is established and subsequently the model form is simplified by nonlinear feedback linearization. Secondly, an entirety model-type solution is proposed, integrating a nonlinear model predictive control (NMPC) algorithm with a cross-coupling control (CCC) algorithm. Furthermore, a novel disturbance compensator based on the system's inverse model is introduced to effectively handle disturbances, encompassing unmodeled errors and noise. The proposed innovative controller, known as nonlinear model predictive control—cross-coupling control with deep neural network feedforward (NMPC-CCC-DNNF), is designed to minimize synchronization errors and counteract the impact of disturbances. The stability of the control system is rigorously demonstrated. Finally, simulation results underscore the efficacy of the NMPC-CCC-DNNF controller, showcasing a remarkable 60.8% reduction in synchronization root mean square error (RMSE) compared to other controllers, reaching up to 91.1% in various simulations. These results affirm the superior control performance achieved by the NMPC-CCC-DNNF controller.

Controller Design Based on a Synchronous Error Compensator for Wheeled Cross-coupled Systems

Controller Design Based on a Synchronous Error Compensator for Wheeled Cross-coupled Systems

Synchronization in a higher-order neuronal network with blinking interactions

The synchronization of higher-order networks presents a fascinating area of exploration within nonlinear dynamics and complex networks. Simultaneously, growing research interest focuses on uncovering synchronization dynamics in time-varying networks with time-dependent coupling structures, reflecting their prevalence in real-world systems like neuronal networks. Motivated by this, the present study delves into the synchronization phenomenon within a higher-order network incorporating a blinking coupling scheme. Blinking coupling is an on–off switching coupling that has been demonstrated to enhance synchronization effectively. Its efficacy stems from ensuring synchronization, as the master stability function (MSF) follows a linear pattern. In this study, our objective is to investigate such a time-varying coupling scheme in a higher-order network configuration. We investigate the influence of coupling parameters and blinking frequency on synchronization behavior. Notably, our findings demonstrate that as the blinking frequency increases, the network exhibits a gradual convergence toward the behavior of the average network. Furthermore, leveraging the analytical framework of MSF and the average synchronization error, we provide analytical and numerical evidence confirming that the MSF pattern within the average network transforms into a linear function. The synchronous and asynchronous regions also exhibit a clear separation demarcated by a linear curve across the coupling parameter space. Moreover, our results suggest that incorporating higher-order interactions fosters enhanced synchrony by effectively scaling the synchronization patterns to lower coupling parameter values.

Master stability functions of networks of Izhikevich neurons.

Synchronization has attracted interest in many areas where the systems under study can be described by complex networks. Among such areas is neuroscience, where it is hypothesized that synchronization plays a role in many functions and dysfunctions of the brain. We study the linear stability of synchronized states in networks of Izhikevich neurons using master stability functions (MSFs), and to accomplish that, we exploit the formalism of saltation matrices. Such a tool allows us to calculate the Lyapunov exponents of the MSF properly since the Izhikevich model displays a discontinuity within its spikes. We consider both electrical and chemical couplings as well as global and cluster synchronized states. The MSF calculations are compared with a measure of the synchronization error for simulated networks. We give special attention to the case of electric and chemical coupling, where a riddled basin of attraction makes the synchronized solution more sensitive to perturbations.

Impact Analysis of Time Synchronization Error in Airborne Target Tracking Using a Heterogeneous Sensor Network

This paper investigates the influence of time synchronization on sensor fusion and target tracking. As a benchmark, we design a target tracking system based on track-to-track fusion architecture. Heterogeneous sensors detect targets and transmit measurements through a communication network, while local tracking and track fusion are performed in the fusion center to integrate measurements from these sensors into a fused track. The time synchronization error is mathematically modeled, and local time is biased from the reference clock during the holdover phase. The influence of the time synchronization error on target tracking system components such as local association, filtering, and track fusion is discussed. The results demonstrate that an increase in the time synchronization error leads to deteriorating association and filtering performance. In addition, the results of the simulation study validate the impact of the time synchronization error on the sensor network.

Impact Analysis and Compensation Methods of Frequency Synchronization Errors in Distributed Geosynchronous Synthetic Aperture Radar

Frequency synchronization error, as one of the inevitable technical challenges in distributed synthetic aperture radar (SAR), has different impacts on different SAR systems. Multi-monostatic SAR is a typical distributed configuration where frequency synchronization errors are tiny in distributed airborne and low earth orbit (LEO) SAR systems. However, due to the long time delay and long synthetic aperture time, the imaging performance of a multi-monostatic geosynchronous (GEO) SAR system is affected by frequency oscillator errors. In this paper, to investigate the frequency synchronization problem in this configuration, we firstly model the echo signals with the frequency synchronization errors, which can be divided into fixed frequency errors and random phase noise. Secondly, we talk about the impacts of the two kinds of errors on imaging performance. To solve the problem, we thirdly propose an autofocus back-projection (ABP) algorithm, which adopts the coordinate descent method and iteratively adjusts the phase error estimation until the image reaches its maximum sharpness. Based on the characteristics of the frequency synchronization errors, we further propose the Node ABP (NABP) algorithm, which greatly reduces the amount of storage and computation compared to the ABP algorithm. Finally, simulations are carried out to validate the effectiveness of the ABP and NABP algorithms.

Fast Finite‐Time Dynamic Surface Synchronization Control for Telerobotics System with Prescribed Performance

AbstractThis study focuses on the fast finite‐time synchronization control problem for nonlinear telerobotics system with asymmetric time‐vary delays and uncertainties. A finite‐time prescribed performance function is incorporated into the backstepping control framework such that the synchronization error will remain within a predefined funnel boundary. To increase the rate of convergence, the selected error transformation function is a arctangent function. The utilization of dynamic surface control method aims to decrease computational complexity by mitigating the need for repetitive differentiation of virtual signals in the conventional backstepping algorithm. In the meantime, the non‐power approximate signals of fuzzy neural network algorithms are utilized to replace the system uncertainties, effectively addressing the passivity issue associated with time‐delayed channels. Both theoretical analysis and experimental results are present to verify that the synchronization error can converge to a small neighborhood around zero in the fast finite time and the closed‐loop system remains stable.