Synchronous Control Research Articles

Design and synchronization control application of a new five-dimensional memristor CNN conservative hyperchaotic system

Abstract Incorporating memristors into a cellular neural network (CNN) and introducing chaotic characteristics can generate in highly complex and unpredictable dynamic behaviors. To advance this research area, this paper proposes a new five-dimensional memristor CNN conservative hyperchaotic system and systematically analyzes its dynamic properties. The analysis content includes equilibrium point analysis, Poincaré sections, Lyapunov exponent spectra, bifurcation diagrams, two-parameter Lyapunov exponent spectra, complexity assessment, homogeneous and heterogeneous extreme multistability, etc. In addition, the simulation circuit for the new system is designed and constructed. The digital circuit of the new system is implemented using a microcontroller (MCU). After running simulations, the experimental results from the analog circuit, digital circuit, and numerical simulation are consistent with each other, demonstrating the feasibility of the circuit implementation. Finally, two different synchronization control strategies are employed to achieve synchronization control within a finite time.

Impulsive tracking synchronization control of networked robotic manipulator systems in the task space

This study investigates the impulsive tracking synchronization control of networked robotic manipulator systems based on Lagrangian systems. In the task space, the end-effector positions of networked robotic manipulators can synchronize and track a desired trajectory by designing an impulsive controller. Based on impulsive control, some algebraic synchronization criteria are derived. As a direct application of the theoretical results, tracking synchronization of six double-link robotic manipulators in the task space is discussed in detail. Numerical simulations are given to demonstrate the effectiveness and feasibility of the proposed control strategy.

Adaptive Weighted Particle Swarm Optimization for Controlling Multiple Switched Reluctance Motors with Enhanced Deviatoric Coupling Control

Switched reluctance motors (SRMs) are widely used in industrial applications due to their advantages. Multi-motor synchronous control systems are crucial in modern industry, as their control strategies significantly impact synchronization performance. Traditional deviation coupling control structures face limitations during the startup phase, leading to excessive tracking errors and exacerbated by uneven load distribution, resulting in desynchronized motor acceleration and increased speed synchronization errors. This study proposes a modified deviation coupling control method based on an adaptive weighted particle swarm optimization (PSO) algorithm to enhance multi-motor synchronization performance. Traditional deviation coupling control applies equal reference torque inputs to each motor’s current loop, failing to address uneven load distribution and causing inconsistent accelerations. To resolve this, a gain equation based on speed deviation is introduced, incorporating self-tracking error and gain coefficients for dynamic synchronization error compensation. The gain coefficients are optimized using the adaptive weighted PSO algorithm to improve system adaptability. A simulation model of a synchronization control system for three SRMs was developed in the Matlab/Simulink R2023b environment. This model compares the synchronization performance of traditional deviation coupling, Fuzzy-PID improved structure, and adaptive PSO improved structure during motor startup, sudden speed increases, and load disturbances. The validated deviation coupling control structure achieved the initial set speed in approximately 0.236 s, demonstrating faster convergence and a 6.35% reduction in settling time. In both the motor startup and sudden speed increase phases, the two optimized methods outperformed the traditional structure in dynamic performance and synchronization accuracy, with the adaptive PSO structure improving synchronization accuracy by 54% and 37.17% over the Fuzzy-PID structure, respectively. Therefore, the PSO-optimized control system demonstrates faster convergence, improved stability, and enhanced synchronization performance.

Adaptive Neural Network Synchronous Tracking Control for Teleoperation Robots Under Event-Triggered Mechanism

Adaptive Neural Network Synchronous Tracking Control for Teleoperation Robots Under Event-Triggered Mechanism

Study of Resistive SFCL for Stability Increase of BTB-VSC-HVDC System Based on Virtual Synchronous Generator Control Strategy

Study of Resistive SFCL for Stability Increase of BTB-VSC-HVDC System Based on Virtual Synchronous Generator Control Strategy

Load forecasting based on multi-core learning Support Vector Machine (SVM)

Abstract The development of smart grids requires enhanced data integration, robust risk assessment, and dynamic response optimization. In this paper, a multi-core learning Support Vector Machine (SVM) model is presented to improve the accuracy and efficiency of load and photovoltaic output forecasting. The model leverages kernel function optimization and parallel computing frameworks to handle large-scale data efficiently. Additionally, a comprehensive risk assessment system is developed to quantify risks such as overvoltage, undervoltage, line overload, and load loss in distribution networks. An adaptive genetic algorithm-based risk control model is also proposed, optimized in two stages—day-ahead and intra-day—to achieve minimal comprehensive risk through real-time adjustments in distributed power output and electric vehicle charging strategies. Furthermore, an integrated virtual synchronous control online verification method for source-network-load-storage is introduced, enhancing system response speed and control accuracy. These innovations collectively provide a solid theoretical foundation and technical support for the efficient and safe operation of smart grids, addressing the increasing demands of modern energy systems.

An Improved Nonlinear Active Disturbance Rejection Controller via Sine Function and Whale Optimization Algorithm for Permanent Magnet Synchronous Motors Speed Control

Permanent magnet synchronous motors (PMSMs) speed control has gained wide application in various fields. Specifically, there is a disadvantage that nonlinear functions in the conventional active disturbance rejection controller (ADRC) is non‐differentiable at the piecewise points. Thus, an improved nonlinear active disturbance rejection controller (NLADRC) for permanent magnet synchronous motor speed control via sine function and whale optimization algorithm (WOA), abbreviated as NLADRC‐sin‐IWOA, is proposed to overcome this drawback. Considering the unsatisfactory control effect caused by the poor active disturbance resisting ability of the traditional PMSM controllers, this paper proposes an improved NLADRC for PMSM, that reconstructs a novel differentiable and smooth nonlinear function, the novel nonlinear function grounded on primitive function by the function of inverse hyperbolic, sine, square functions, and with difference fitting approach; and designs an improved whale optimization algorithm via convergence factor nonlinear decreasing, Gaussian variation and adaptive cross strategies. The experimental results findings show that the improved NLADRC‐sin‐IWOA has the advantages of response fast, small steady‐state error and tiny overshoot. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Rapid QC-MS: Interactive Dashboard for Synchronous Mass Spectrometry Data Acquisition Quality Control.

Consistently collecting high-quality liquid chromatography-coupled tandem mass spectrometry (LC-MS/MS) data is a time-consuming hurdle for untargeted workflows. Analytical controls such as internal and biological standards are commonly included in high-throughput workflows, helping researchers recognize low-integrity specimens regardless of their biological source. However, evaluating these standards as data are collected has remained a considerable bottleneck─in both person-hours and accuracy. Here we present Rapid QC-MS, an automated, interactive dashboard for assessing LC-MS/MS data quality. Minutes after a new data file is written, a browser-viewable dashboard is updated with quality control results spanning multiple performance dimensions such as instrument sensitivity, in-run retention time shifts, and mass accuracy drift. Rapid QC-MS provides interactive visualizations that help users recognize acute deviations in these performance metrics, as well as gradual drifts over periods of hours, days, months, or years. Rapid QC-MS is open-source, simple to install, and highly configurable. By integrating open-source Python libraries and widely used MS analysis software, it can adapt to any LC-MS/MS workflow. Rapid QC-MS runs locally and offers optional remote quality control by syncing with Google Drive. Furthermore, Rapid QC-MS can operate in a semiautonomous fashion, alerting users to specimens with potentially poor analytical integrity via frequently used messaging applications. Initially developed for integration with Thermo Orbitrap workflows, Rapid QC-MS offers a fast, straightforward approach to help users collect high-quality untargeted LC-MS/MS data by eliminating many of the most time-consuming steps in manual data curation. Download for free: https://github.com/czbiohub-sf/Rapid-QC-MS.

Modeling of Liquefied Natural Gas Cold Power Generation for Access to the Distribution Grid

Cold energy generation is an important part of liquefied natural gas (LNG) cold energy cascade utilization, and existing studies lack a specific descriptive model for LNG cold energy transmission to the AC subgrid. Therefore, this paper proposes a descriptive model for the grid-connected process of cold energy generation at LNG stations. First, the expansion kinetic energy transfer of the intermediate work mass is derived and analyzed in the LNG unipolar Rankine cycle structure, the mathematical relationship between the turbine output mechanical power and the variation in the work mass flow rate and pressure is established, and the variations in the LNG heat exchanger temperature difference, seawater flow rate, and the turbine temperature difference in the cycle system are investigated. Secondly, based on the fifth-order equation of state of the synchronous generator, the expressions of its electromagnetic power, output AC frequency, and voltage were analyzed. Finally, the average equivalent models of the machine-side and grid-side converters are established using a direct-fed grid-connected structure, thus forming a descriptive model of the overall drive process. The ORC model is built in Aspen HYSIS to obtain the time series expression of the torque output of the turbine; based on the ORC output torque, the permanent magnet synchronous generator (PMGSG) as well as the direct-fed grid-connected structure are built in MATLAB/Simulink, and the active power and current outputs of the grid-following-type voltage vector control method and the grid-forming-type power-angle synchronous control method are also verified.

Sliding mode control for finite-time projective synchronisation in distinct Caputo fractional-order delayed neural networks with inconsistent orders

This manuscript delves into the realm of projective synchronisation in delayed neural networks governed by Caputo fractional-order dynamics, addressing the intricate challenges of attaining finite-time synchronisation in networks with distinct structures, functions, and orders. To start, the integration of a compensation controller within the control architecture is examined, aiming to formulate the projection error dynamics. By leveraging projection error system analysis alongside output observation and sliding mode control, a meticulously crafted derivative-based sliding mode surface along with a sliding mode controller are devised, ensuring both convergence and seamless sliding motion. Afterwards, the accessibility of the surface is assessed through relevant lemmas, Caputo derivative characteristics and the Lyapunov direct approach. The subsequent stability of sliding path is corroborated, and indispensable prerequisites for achieving finite-time projective synchronisation are outlined. Furthermore, the establishment of an upper bound for synchronisation time of distinct Caputo fractional-order delayed neural networks (CFDNNs) with inconsistent orders is carried out. Ultimately, simulations on distinct CFDNNs with consistent and inconsistent orders are conducted to exemplify our approach, employing the continuous frequency distribution model.

Analysis of Optimization Algorithms Used in Permanent Magnet Synchronous Motor Control According to Different Performance Indices

Permanent magnet synchronous motors (PMSM) can be produced at lower costs with new developments in magnet technology and are widely used in many industrial areas due to their low energy consumption. The widespread use of PMSM brings with it the requirement for high accuracy control performance. In order to achieve high performance accuracy, vector control technique is generally preferred. However, the parameter values of the controllers used in this technique are very important for motor performance. Tuning these parameters by optimization techniques instead of classical methods has become a popular topic. Nowadays, modern control methods show more effective control behavior than classical control methods, which has made the use of modern control methods widespread and studies on modern control methods have intensified. In this study, the analysis of different optimization algorithms used in the control of an PMSM according to their performance indices is investigated in Matlab-Simulink environment. As a result of simulation with different optimization algorithms under the same conditions, the analysis of optimization algorithms using different performance indices such as integral of the absolute value of the error (IAE), integral of the square of the error (ISE), Integral of the Square of the Error Multiplied by Time (ITSE), and Integral of the Absolute Error Multiplied by Time (ITAE) is carried out.

Synchronous flying-around control for uncontrolled tumbling spacecraft

The problem of synchronous flying-around control for uncontrolled tumbling target spacecraft is investigated in the paper. Firstly, the relative motion equations of the chaser and the target is established based on the line of sight (LOS) coordinate system, and the scenario of the chaser flying-around uncontrolled tumbling target is analyzed. Secondly, the attitude motion model of the uncontrolled tumbling target is established, and the attitude of the target is described by the Modified Rodriguez Parameters (MRPs). The synchronous flying-around problem is transformed into a two-dimensional control problem in the instantaneous rotation plane of LOS (IRPL). The sliding mode controller is designed to control the LOS direction and vertical LOS direction of the chaser in the IRPL, and the stability of the controller is analyzed. In order to verify the effectiveness of the synchronous flying-around control method, three simulation scenarios are designed. The simulation results verify the effectiveness of the proposed method.

Pareto-optimal synchronization control of nonlinear multi-agent systems via integral reinforcement learning

Pareto-optimal synchronization control of nonlinear multi-agent systems via integral reinforcement learning

Three-stage resilience enhancement via optimal dispatch and reconfiguration for a microgrid

Extreme weather causes an increase in power system failure. Previous studies on system resilience have often overlooked the user-side of a microgrid. This study proposes composite resilience indices (RI) based on the power supply of a large-scale manufacturing campus microgrid to quantify its ability to withstand extreme events. The proposed RI consider load priority, minimum supply load, total energy supplied, and the performance recovery-to-degradation slope ratio in an islanding microgrid. Accordingly, this study presents a three-stage resilience optimal dispatch and reconfiguration strategy, including energy-level scheduling, grid-level reconfiguration, and dynamic-level verification. A multi-objective optimization approach is used for energy scheduling, followed by system reconfiguration via DIgSILENT system modeling to meet the grid code and maximize load supply. Value at Risk methods are used to verify microgrid stability and load shedding requirements, supported by the virtual synchronous generator control of the energy storage system. The test results from a practical large-scale manufacturing campus microgrid validate the proposed approaches for enhancing system resilience considering load values.

Dynamic overmodulation strategy based on torque and flux optimal tracking for DTC‐SVM of surface‐mounted PMSM drives

AbstractDirect torque control with space vector modulation has been increasingly attracting emphasis for permanent‐magnet synchronous machine control, benefiting from a high dynamic response and low torque ripple. However, the rapid tracking performance of torque and stator flux linkage is gradually deteriorating as the machine enters the overmodulation region because of the output‐voltage limitation of the inverter. Therefore, to enhance the system tracking performance in the overmodulation region, an innovative overmodulation method based on torque and flux optimal tracking is proposed. In contrast to the conventional overmodulation method where only one of the torque and stator flux linkage tracking is considered, the proposed strategy first constructs the weightless cost function to achieve a trade‐off control between the torque and stator flux linkage. Then, by using an equivalent geometric path to intuitively express the weightless cost function, the minimum cost function can be easily solved and the optimal output voltage vector can be determined correspondingly, to achieve the fast‐tracking of both the torque and stator flux linkage. Additionally, the voltage utilization of different overmodulation schemes is also analysed. Finally, the experimental results demonstrate the efficiency and practicality of the proposed strategy.

Research on constant tension yarn transfer control technology based on AGA-PID and FOC algorithm

To solve the problems of poor real-time tension tracking, large overshoot, long adjustment time, and large tension fluctuation error that exist in the current control system for yarn feeding in the circular weft knitting machine, this paper presents an optimized and improved adaptive genetic algorithm (AGA) for adjusting the PID (Proportional Integral Derivative) parameters of the tension control part of the yarn-conveying system (AGA-PID). The algorithm is combined with the field-oriented control technology of the permanent magnet synchronous motor to design a closed-loop control strategy for the yarn-conveying system of a circular weft knitting machine. The MATLAB-Simulink simulation model was used to analyze the external tension loop-based AGA-PID controller and permanent magnet synchronous motor dual closed-loop control strategy of the yarn transport control system. The experimental verification was conducted by constructing an experimental platform that simulates the real process of yarn transportation. The outcomes of system simulations and experiments have consistently demonstrated that the AGA-PID control algorithm outperforms the GA-PID and PID control algorithms in terms of overshooting, response speed, stability, and anti-jamming ability. The yarn transport control system, which is designed to operate based on an AGA-PID control algorithm, is capable of reducing the fluctuations in tension that occur during the transportation of yarn and exhibits enhanced control accuracy, effectively stabilizing yarn tension control during the conveying process. The application of this algorithm can enhance the overall elasticity uniformity and surface flatness of knitted fabrics, thereby facilitating the realization of adjustable and controllable local elasticity in fabrics.

Optimization of Synchronous Control Parameters Based on Improved Sinusoidal Gray Wolf Algorithm

High precision control is often accompanied by many control parameters, which are interrelated and difficult to adjust directly. It is difficult to convert the system control effect directly into mathematical expression, so it is difficult to optimize it by intelligent algorithm. To solve this problem, we propose an improved sinusoidal gray wolf optimization algorithm (ISGWO). In this algorithm, a particle crossing processing mechanism based on the symmetry idea is introduced to maximize the retention of the position information of the optimal individual and improve the search accuracy of the algorithm. In addition, a differential cross-perturbation strategy is adopted to help the algorithm jump out of the local optimal solution in time, which enhances the development capability of ISGWO. Meanwhile, the position update formula with improved sinusoidal can better balance the development and exploration of ISGWO. The ISGWO algorithm is compared with three improved Gray Wolf algorithms on the CEC2017 test set as well as the synchronization controller. The experimental results show that the ISGWO algorithm has better selectivity, speed and robustness.

Observer-based impulsive control for finite-time synchronization of delayed neural networks on time scales

Observer-based impulsive control for finite-time synchronization of delayed neural networks on time scales

Two-cylinder Synchronous Electro-hydraulic Servo System and its Control Technology Development

Background: Electro-hydraulic servo control system synchronous control in industrial application is very extensive because the application of electro-hydraulic servo control system is mostly in a poor working environment, there are a variety of complex external factors. Objective: Improve the comprehensive performance indicators such as accuracy, speed, and stability of synchronous control of hydraulic cylinders Methods: In the process of synchronous movement, the typical hardware structure and control methods and algorithm strategies of the double hydraulic cylinder are analyzed in detail, and the basic principle of common hydraulic double cylinder synchronous control is summarized. Results: Through the comprehensive analysis of papers and patent documents related to the synchronous control of hydraulic twin cylinders, these papers and patented technologies study the control algorithm, and apply the new technology to the synchronous control of electro-hydraulic servo hydraulic cylinders to improve its comprehensive performance indicators such as work accuracy, speed and stability. Conclusion: Referring to the development trajectory of control algorithms in this regard, combined with new control theory and new technology, many achievements can be applied and researched in hydraulic cylinder synchronous control, and better develop the synchronous control system when double hydraulic cylinders and more hydraulic cylinders work together. By collating and analyzing the relevant research results, the two-cylinder synchronous control technology is comprehensively evaluated and prospected, which provides a valuable reference for the research and application in this field.

Predictive Synchronous Rectification Control Scheme for Resonant DC–DC Converters for Battery Charging and Telecom Application

Predictive Synchronous Rectification Control Scheme for Resonant DC–DC Converters for Battery Charging and Telecom Application