Model Of Control System Research Articles

Characteristic analysis method for integrated multi-parameter hydro-viscous speed control system

Hydro-viscous clutch has become an inevitable choice for special vehicle transmissions. As a nonlinear dynamic system with large lagging link, its timing performance is affected by input rotational speed, lubricating oil temperature and pressure and other factors. However, from the control perspective, the speed regulation law, formation mechanism control characteristics, global model of hydro-viscous speed control system (HSCS) are unclear. To solve these problems, this paper presents a comprehensive analysis of the Hydro-viscous Speed Control System (HSCS), focusing on its steady-state and dynamic speed control characteristics. A data-driven model is established to describe the relationship between input rotational speed, output speed, control oil pressure, and lubricating oil temperature. The findings provide a foundation for optimizing HSCS structure and parameters, enhancing the performance and reliability of such systems in special vehicle transmissions and establishing a temperature-speed control system for special vehicle transmissions.

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.

Development of mathematical modeling of a mobile robot's motion control system

This study focuses on developing a mathematical model for precise control and stabilization of unmanned aerial vehicles (UAVs) in various spatial conditions. Addressing the problem of achieving precise control and stability, the proposed solution designs a control system based on a linear-quadratic controller (LQR) and simulates it using a proportional-derivative (PD) controller implemented in Matlab/Simulink. The results demonstrate high precision and stability in controlling the UAV motion parameters – roll, pitch, yaw, and altitude. This important level of performance is achieved due to the adaptivity of the LQR-based control system, which optimizes control actions according to the unsteady dynamics of the UAV. The integration of the PD controller improves responsiveness and stability, providing precise motion control over a range of spatial states. These features effectively solve the problem by handling the complex dynamics of the UAV and providing precise control. The results are explained by the ability of the LQR to provide optimal control laws that minimize deviations using a quadratic cost function, while the PD controller quickly corrects errors and responds to disturbances. The benefits of this approach include a significant reduction in control errors by about 25–30 %, increased response speed to external disturbances, and reduced computational latency due to efficient processing compared to more resource-intensive methods such as model predictive control. The developed mathematical model can be applied in practice in conditions requiring robust real-time control and adaptation to dynamic changes in the environment. It is especially suitable for industries such as logistics, surveillance, and environmental monitoring, providing an effective and optimal solution for stabilizing and controlling the motion of UAVs in various spatial states. This approach improves the performance of UAVs and expands their capabilities in various operating conditions.

Swiftly Chasing Gravitational Waves across the Sky in Real Time

We introduce a new capability of the Neil Gehrels Swift Observatory, dubbed “continuous commanding,” that achieves 10 s latency response time on orbit to unscheduled target-of-opportunity requests received on the ground. We show that this will allow Swift to respond to premerger (early-warning) gravitational-wave (GW) detections, rapidly slewing the Burst Alert Telescope (BAT) across the sky to place the GW origin in the BAT field of view at or before merger time. This will dramatically increase the GW/gamma-ray burst (GRB) codetection rate and enable prompt arcminute localization of a neutron star merger. We simulate the full Swift response to a GW early-warning alert, including input sky maps produced at different early-warning times, a complete model of the Swift attitude control system, and a full accounting of the latency between the GW detectors and the spacecraft. 60 s of early warning can double the rate of a prompt GRB detection with arcminute localization, and 140 s guarantees observation anywhere on the unocculted sky, even with localization areas ≫1000 deg2. While 140 s is beyond current GW detector sensitivities, 30–70 s is achievable today. We show that the detection yield is now limited by the latency of LIGO/Virgo cyberinfrastructure and motivate a focus on its reduction. Continuous commanding has been integrated as a general capability of Swift, significantly increasing its versatility in response to the growing demands of time-domain astrophysics. We demonstrate this potential on an externally triggered fast radio burst (FRB), slewing 81° across the sky, and collecting X-ray and UV photons from the source position <150 s after the trigger was received from the Canadian Hydrogen Intensity Mapping Experiment, thereby setting the earliest and deepest such constraints on high-energy activity from nonrepeating FRBs. The Swift Team invites the community to consider and propose novel scientific applications of ultra-low-latency UV, X-ray, and gamma-ray observations.

Theoretical Analysis on Active Polarization Control of Fiber Laser Based on Root Mean Square Propagation Algorithm

High-power linearly polarized fiber lasers are widely used in coherent beam combination, nonlinear frequency conversion, and gravitational wave detection. With the increase in output power, it is challenging for fiber lasers to maintain a high polarization extinction ratio (PER). Combined with intelligent techniques, active polarization control is a prospective method to obtain the laser output with high PER and high stability. We demonstrate a comprehensive model of an active polarization control system. The root mean square propagation (RMS-Prop) algorithm is used to control the non-polarization-maintaining (non-PM) fiber laser to generate linearly polarized laser. The parameters of the RMS-Prop algorithm are theoretically analyzed, including cost function, perturbation amplitude, and global learning rate. The simulation results show that PER is the optimal cost function. When the perturbation amplitude is 0.06 and the global learning rate is 0.6, the system can achieve the optimal control speed and accuracy. By comparison with the stochastic parallel gradient descent (SPGD) algorithm, the RMS-Prop algorithm has an advantage in obtaining higher PER.

Analytical model of an active noise control system for performance analysis of adaptive algorithms in HVAC system

In real-world acoustic scenarios related to the problem with the low-frequency noise such as HVAC systems, the signal is a stochastic, non-stationary and time-varying process where the performance of the active noise control technique mainly depends on the characteristics of the adaptive filtering techniques. In this paper, a method for analytical modeling of active noise control of signals in MATLAB/Simulink is presented, in which the acquisition of audio signals is provided by using a National Instruments control and acquisition module. For the realization of the active noise control, an adaptive filtering system model for signal analysis has been developed, using three adaptive algorithms (LMS,RLS and NLMS). The analysis of the characteristics of each of the three investigated adaptive algorithms is performed according to four parameters: Mean Square Error (MSE), convergence speed, stability and robustness. The conclusions are presented through a comparative analysis of the results obtained from the implemented methodology. The individual characteristics and parameters that are observed for correct adaptation and optimal results of each of the adaptive filters are explained in detail.

A Target Tracking Method Based on the Self-organizing Fuzzy Extended Kalman Filter for UAV

T his paper proposed a novel approach based on integrated tracking and positioning for Unmanned Aerial Vehicle (UAV) systems. In the proposed scheme, a self-organizing fuzzy Extended Kalman Filter (EKF) system was studied to track the flight trajectory of an UAV. With this goal, the control model of the UAV system with the disturbances was established based on the dynamic equation firstly. Secondly, the self-organizing Fuzzy EKF system was designed to identify and estimate the errors of the UAV position adaptively. And then, the trajectory tracking control was determined for the UAV according to the obtained errors. Finally, the proposed strategy was applied to a UAV to demonstrate the effectiveness.

Research on Adaptive Control Systems for Building Shading Based on Decision Tree and Random Forest Classification Algorithms

Dynamic shading systems for buildings, due to their variable mechanisms and high adaptability, have the potential to reduce energy consumption in buildings significantly. However, the performance of dynamic shading is largely influenced by the control system employed. This study aims to explore an adaptive control method for building shading based on decision tree and random forest classification algorithms (machine learning), with the objective of minimizing energy demands related to lighting and cooling as much as possible. The adaptive control system model may independently modify the location of building shading in response to input environmental conditions, thereby achieving energy savings for both lighting and cooling. According to the findings, the automated control system model may reduce building energy consumption by 38% overall, which is close to the ideal level of energy conservation.

System approach in urban transportation case: Huánuco region-Peru

The growing increase in vehicles produces serious congestion problems in urban centers; This situation raises questions about the control methods of the variables of interest in this problem. The case study presented is an approach to the topic and is based on a hypothetical scenario that is modeled and simulated with a systemic approach, considering the individual movement of the vehicles. A methodology is presented for the statistical analysis of the simulation results. The article shows the main aspects in the modeling of a basic traffic control system to simulate it and observe its behavior in the rhythm of arrivals, travel times and speeds, as well as monitoring traffic, in this sense the most important aspects will be illustrated. Relevant aspects that must be addressed in the configuration of a simulation experiment, such as determining the appropriate simulation time, the number of repetitions, the warm-up period, the evaluation of indicators to submit them to the normality test, and the application of techniques of variance reduction, experiencing congestion and evaluating measures to control excessive speed. The operation of traffic lights at intersections is also modeled and evaluated.

Preface

On behalf of the organizing committee, we are very pleased to introduce the proceedings of 2024 The 17th International Conference on Computer and Electrical Engineering (ICCEE 2024), which was held in Shenzhen, China (virtual) on June 28-30, 2024. Because more than two-thirds of the authors chose to attend the conference online, we had to switch to totally virtual conference to ensure the effectiveness of the conference. It is with great pleasure and anticipation that we convene once again to explore the latest advancements, challenges, and opportunities in the dynamic fields of Computer and Electrical Engineering. In today’s interconnected world, computer and electrical engineering play critical roles in shaping the technological landscape. ICCEE 2024 serves as a platform for fostering interdisciplinary collaboration, knowledge sharing, and networking among researchers, practitioners, and industry leaders. ICCEE 2024 lasted 3 days. There are online test sessions on ZOOM on the first day, and then followed with a wonderful array of 2 keynote speeches and 2 invited speeches, along with 4 technical sessions during June 29-30. The authors presentations cover topics on Power System Control Model and Simulation Analysis, Electrical Fault Detection and Maintenance Strategy, Digital Communication and Information Systems, Intelligent Electronic Systems and Status Monitoring, with 15 minutes for each presentation, including 2 minutes for the Q&amp;A. Additionally, there are around 100 delegates attending online conference. Delegates had a wide range of sessions to choose from and had enough time in deciding which sessions to attend. List of Committee and Statement of Peer Review are available in this Pdf.

Active Control for Construction Stability of Suspension Arch Rib Segments Based on ARID System

In response to the issues of vibrations affecting the construction safety, speed, and accuracy of suspended structures due to dynamic disturbances during lifting, environmental disturbances, and improper operator actions, this paper investigates the application of the Active Rotary Inertia Driver (ARID) system for double-rope suspended structures, namely analyzes, and applies control to the vibration response of double-rope suspended structures under external excitations. Taking the double-rope suspended structure as an example, a dynamic analytical model of the double-rope suspended structure is established based on D’Alembert’s principle. Through shake table tests, the dynamic response characteristics and vibration control effects of the double-rope suspended structure under various external excitations are studied, and the influence of the twisting radius of the suspended structure on the control effect is analyzed. The experimental results show that the ARID control system achieves significant control effects on the swing and torsional vibrations of the suspended structure, which validates the accuracy of the theoretical model for the ARID control system and demonstrates its feasibility for practical applications.

Modeling of a six-degree-of-freedom magnetic levitation platform actuated by noncontact Lorentz forces

In this study, the modeling of a six-degree-of-freedom (DOF) magnetic levitation platform actuated by noncontact Lorentz forces was analyzed. First, an analytic model of the actuator forces was studied, and the magnetic flux density in the working gap of the actuators was established using the Image Method, Ampere molecular hypothesis, and Biot–Savart law. The dynamics model of the floater actuated by the actuators was then built using the Newton–Euler method. Moreover, the double-pendulum chaotic nonlinear system of the hoisted floater was simplified as a single-DOF system, in which the interaction effects between two DOFs were considered. Furthermore, modeling of the platform control system was performed, which included an acceleration measuring unit with six uniaxial accelerometers, a position measuring unit with three two-dimensional PSDs, and a control unit that decouples the six-DOF control. Finally, an experimental setup was built to perform ground tests on the platform. The results verified the platform modeling; translational and rotational positioning accuracies were approximately 5 μm and 40 μrad at the six DOFs, respectively, and the vibrational suppression efficiencies were greater than 90% for low frequency disturbances. Moreover, Kalman estimators and disturbance observers were introduced into the controller to estimate the movement of the floater and observe the direct disturbance. Simulation testing demonstrated a significant improvement in the vibration-suppression performance of the platform.

Aeolian Environment Regionalization in Xinjiang and Suggestions for Sand Prevention in Typical Areas

The Xinjiang region is prone to frequent and complex wind and sand disasters, which present a significant challenge to the sustainable development of local areas. This research uses multi-source data to analyze the spatial distribution of the aeolian environment in Xinjiang, establishes a four-level zoning scheme, and proposes recommendations for ecological management and engineering and control. Results indicate that (1) Xinjiang’s aeolian environment and its types exhibit spatial heterogeneity. The aeolian environment types display a high concentration in the eastern region and a low concentration in the western region. Furthermore, the aeolian environment types are concentrated in the basin region. Moreover, the aeolian environment types exhibit a meridional distribution pattern. (2) A four-level zoning system for aeolian environments in Xinjiang was developed, comprising two first-level zones, seven s-level subzones, 22 third-level wind zones, and 31 fourth-level subdivisions. (3) A structural model for a highway sand control system is proposed for aeolian environment types of subdivisions, including fixing-based, combined blocking and fixing, wind-blocking and sand-transferring, and combined blocking and fixing–transferring. The aeolian environment regionalization program proposed in this study can be a scientific reference for relevant departments in formulating and implementing sand prevention and control planning.

Comparison between Genetic Algorithms of Proportional–Integral–Derivative and Linear Quadratic Regulator Controllers, and Fuzzy Logic Controllers for Cruise Control System

One of the most significant and widely used features currently in autonomous vehicles is the cruise control system that not only deals with constant vehicle velocities but also aims to optimize the safety and comfortability of drivers and passengers. The accuracy and precision of system responses are responsible for cruise control system efficiency via control techniques and algorithms. This study presents the dynamic cruise control system model, then investigates a genetic algorithm of the proportional–integral–derivative (PID) controller with the linear quadratic regulator (LQR) based on four fitness functions, the mean squared error (MSE), the integral squared error (ISE), the integral time squared error (ITSE) and the integral time absolute error (ITAE). Then, the response of the two controllers, PID and LQR, with the genetic algorithm was compared to the response performance of the fuzzy and fuzzy integral (Fuzzy-I) controllers. The MATLAB 2024a program simulation was employed to represent the system time response of each proposed controller. The output simulation of these controllers shows that the type of system stability response was related to the type of controller implemented. The results show that the Fuzzy-I controller outperforms the other proposed controllers according to the least Jmin function, which represents the minimum summation of the overshoot, settling time, and steady-state error of the cruise control system. This study demonstrates the effectiveness of driving accuracy, safety, and comfortability during acceleration and deceleration due to the smoothness and stability of the Fuzzy-I controller with a settling time of 5.232 s and when converging the steady-state error to zero.

Characterization of Position Synchronous Control System for Double Hydraulic Cylinder

Background: There are some problems in the synchronous control system of electrohydraulic servo double hydraulic cylinders, such as uncertainty of system parameters, time variation, difference between two subsystems, and unbalanced load disturbance during operation. The above problems cause the output responses of the two cylinders to be inconsistent, resulting in the situation that the output flows of the two cylinders are not synchronized. Objective: Further improve the accuracy of synchronous control while reducing the cost of using synchronous control system. Methods: The AMEsim simulation model of electro-hydraulic servo dual-cylinder synchronous control system is established, and based on this model, the dynamic characteristics, coupling characteristics, and synchronization characteristics of the double-cylinder system are analyzed theoretically and simulated. Results: Analyze and verify the influence of various factors on the synchronization of double hydraulic cylinders, and summarize the influence law of the change of different factors on the output and synchronization accuracy of the double hydraulic cylinder system. Conclusion: This study analyzes the influence of various factors on the synchronization of double cylinders by adjusting the parameters, and summarizes the influence law of the change of different factors on the output and synchronization accuracy of the double cylinder system.

Empirical Verification of the Control System Model of Profit Rate

Empirical Verification of the Control System Model of Profit Rate

Mechanistic indoor air quality model and data-driven calibration for intelligent HVAC AI control system through real-time data in underground buildings

Abstract Modeling the dynamics of indoor air quality (IAQ) in subway environments is challenging due to the complex interplay of variables like subway schedules, ventilation, and passenger numbers. This study developed a high-precision mechanistic model for IAQ management and intelligent HVAC control in underground buildings, focusing on Y-station. Global Sensitivity Analysis (GSA) highlighted the significance of the train piston factor during operational hours and the increased sensitivity of penetration and deposition factors when trains are not operational. The model, calibrated in real-time using a Genetic Algorithm (GA), exhibited remarkable accuracy in simulating PM2.5 levels, affirming its effectiveness in forecasting future air quality. The model adeptly captures the complexities of air quality dynamics, providing a comprehensive understanding of temporal IAQ variations. The result demonstrates the model’s efficacy as a tool offering a foundation for strategies to forecast IAQ and control the HVAC system in underground buildings.

Design and modeling of the case of unmanned aerial vehicle, in the technical facility control system

This article presents the design and modeling of a case with a combat kit for an army unmanned aerial vehicle with an autonomous launch system. The scientific problem solved by the authors in this article is the design and modeling of a new medium-class combat vehicle, according to the current concept of modern design and produc-tion «Industry 4.0», which implies the creation of new dual-use equipment and technologies. The purpose of scien-tific research is the design and modeling of a case with a combat kit for an army unmanned aerial vehicle. The following methods of scientific research were used in the scientific article: theoretical and empirical. The authors associate the prospects of their research with the design and modeling of an innovative control system for a two-case army unmanned aerial vehicle with an autonomous launch system, which has mounts for installation on the chassis of a combat vehicle.

SYNTHESIS OF NARMA-L2 NEURAL CONTROLLER FOR TRACTION INDUCTION ELECTRIC DRIVE SYSTEM

In today's world, where technology is developing rapidly, electric drive systems play a key role in a huge range of applications: from industrial production lines to vehicles and household appliances. These systems require high efficiency, accuracy and reliability. It is here that there is a need to improve and optimize the processes of controlling electric drives.In recent decades, neural network controllers, or neuroregulators, have won their place in the world of automatic control. Their unique ability to model complex nonlinear dependencies makes them an ideal tool for applications in electric drive systems. Neuro-regulators can adapt to changing operating conditions, learn from data and optimize the control process depending on specific requirements and conditions.This work is devoted to the possibility of using a neuroregulator in the traction asynchronous electric drive system. In the Matlab/Simulink environment, a simulation model of the AD914-U traction asynchronous electric motor vector control system described in the d,q rotating coordinate system was developed. The synthesis of the NARMA-L2 neuroregulator was performed, which combines the principles of the autoregressive model and the moving average model to provide prediction and control of complex processes. The main idea of this controller is to build a nonlinear transformation of input data that can predict the future states of the system. To demonstrate the capabilities of neuroregulators in traction electric motor control systems, comparative modeling of the NARMA-L2 neuroregulator and the classical proportional-integral regulator was conducted.The results of simulation modeling show that the system with a neuro-regulator shows the best indicators of regulation of the given parameters in transient processes and is a promising tool in the development of high-performance and energy-efficient traction electric drives.

System response curve based first‐order optimization algorithms for cyber‐physical‐social intelligence

AbstractThe continuous enhancement of optimization algorithms and their parameters has spurred the expansion of AI into novel application domains such as image recognition and smart home technology. This paper employs the system response curve (SRC) to the adaptive learning rate optimizer, addressing challenges associated with the establishment of the optimizer control model and parameter adjustments affecting the dynamic performance of the system. These insights offer theoretical support for the optimizer's application in deep learning models. To begin, the adaptive learning rate optimizer is a time‐varying system. Based on the intrinsic relationship between the network optimization and the control system, the time domain expression and approximate transfer function of the adaptive learning rate optimizer are derived, and the system dynamic model is established. Furthermore, based on the system control model of the optimizer, it is proposed to explain the performance impacts of different optimizers and their hyperparameters on the deep learning model through the SRC. Finally, experiments are performed on the MNIST, CIFAR‐10, UTKinect‐Action3D, and Florence3D‐Action datasets to validate the control theory of explaining optimizers through system response curves. The experimental results show that the recognition performance of the Adaptive Moment Estimate (Adam) is better than that of the Adaptive Gradient (AdaGrad) and Root Mean Square Propagation (RMSprop). Additionally, the learning rate affects the model training speed, and the practical application aligns with the theoretical analysis.