Development Of Control System Research Articles

NON-ANALYTICAL DESIGN OF THE PI CONTROLLER OPTIMAL PARAMETERS FOR A CONTINUOUS LINE

The multiple motor drives are complex and coupled MIMO nonlinear systems. Due to the complexity of their mathematical models the development of effective control systems is quite complicated task. This paper presents the design of optimal control based on the quadratic optimality criterion for the central section of the continuous production line using soft computer methods when fuzzy model of the controlled system has been used. In this case, the proposed method demonstrates non-analytical design of the optimal parameters of PI controller with emphasis on minimal knowledge on the controlled system. The realized experimental measurements on a continuous line laboratory model confirmed the effectiveness and the good dynamic properties of the optimal continuous line controller and also its applicability to others MIMO nonlinear dynamic systems.

Development of Water Quality Control and Monitoring System for Hydroponic Plants with a Cloud-Based System

Development of Water Quality Control and Monitoring System for Hydroponic Plants with a Cloud-Based System

Energy management in hybrid complexes based on wind generation and hydrogen storage

This work is devoted to the analysis of scientific articles in the field of hybrid energy complexes based on hydrogen technologies and renewable energy sources. A special attention is given to wind generation. The review of the topics of scientific publications indexed in the Scopus database in the field of research is carried out, the most frequently encountered topics and the rarest ones are highlighted. Brief statistics about publications selected for detailed analysis are given. The most interesting direction for studying is development of control systems for hybrid energy complexes. Several traditional approaches, which are commonly used in research on this topic and methods are highlighted. The existing shortcomings and inaccuracies in the overviewed works are identified. Conclusions are drawn about the need to transform existing methods and specific proposals are made to improve management systems to increase the efficiency of decision-making and achieve greater economic benefits. Promising areas of research that also require special attention are formulated.

Development of Control System for a Prefabricated Board Transfer Palletizer Based on S7-1500 PLC

Development of Control System for a Prefabricated Board Transfer Palletizer Based on S7-1500 PLC

Pedestrian Abnormal Behavior Detection System Using Edge–Server Architecture for Large–Scale CCTVEnvironments

As the deployment of CCTV cameras for safety continues to increase, the monitoring workload has significantly exceeded the capacity of the current workforce. To overcome this problem, intelligent CCTV technologies and server-efficient deep learning analysis models are being developed. However, real-world applications exhibit performance degradation due to environmental changes and limited server processing capacity for multiple CCTVs. This study proposes a real-time pedestrian anomaly detection system with an edge–server structure that ensures efficiency and scalability. In the proposed system, the pedestrian abnormal behavior detection model analyzed by the edge uses a rule-based mechanism that can detect anomalies frequently, albeit less accurately, with high recall. The server uses a deep learning-based model with high precision because it analyzes only the sections detected by the edge. The proposed system was applied to an experimental environment using 20 video streams, 18 edge devices, and 3 servers equipped with 2 GPUs as a substitute for real CCTV. Pedestrian abnormal behavior was included in each video stream to conduct experiments in real-time processing and compare the abnormal behavior detection performance between the case with the edge and server alone and that with the edge and server in combination. Through these experiments, we verified that 20 video streams can be processed with 18 edges and 3 GPU servers, which confirms the scalability of the proposed system according to the number of events per hour and the event duration. We also demonstrate that the pedestrian anomaly detection model with the edge and server is more efficient and scalable than the models with these components alone. The linkage of the edge and server can reduce the false detection rate and provide a more accurate analysis. This research contributes to the development of control systems in urban safety and public security by proposing an efficient and scalable analysis system for large-scale CCTV environments.

Comparison and Evaluation of Learning Capabilities of Deep Learning Methods for Predicting Ship Motions

The development of intelligent ship control systems in real-world conditions relies heavily on the accurate identification and prediction of ship seakeeping and maneuvering trajectories. In this study, we comprehensively evaluate a selection of deep learning methods to assess their learning capabilities in terms of idealizing ship motion behavior in realistic operational environments. To recover real conditions, we utilize historical Automatic Identification System (AIS) data and a time domain 6 Degree of Freedom (6- DoF) grounding dynamics model to generate ship motion sequences for a Ro-Ro passenger ship operating in the Gulf of Finland. Via a rigorous evaluation process, we validate the performance of these methods using extensive data streams. The analysis includes the identification and estimation of uncertainties between two ports. The paper demonstrates the proficiency of the selected deep learning methods in capturing ship maneuvering features, their potential use in the design of ship control and intelligent decision support systems.

Development of a humanoid robot control system based on AR-BCI and SLAM navigation

Development of a humanoid robot control system based on AR-BCI and SLAM navigation

AC PWM Control System in Induction Motor using Microcontroller

The demand for efficient and precise control of induction motors has led to the development of advanced control systems, among which AC PWM (Pulse Width Modulation) stands out as a prominent technique. This project focuses on the design and implementation of an AC PWM control system for an induction motor utilizing a microcontroller. The system employs a microcontroller to generate PWM signals, controlling the power electronics responsible for modulating the voltage supplied to the motor. The report details the architecture, components, and methodology employed in the project. A comprehensive discussion on the PWM control algorithm, motor drive circuitry, and sensor integration is presented. The microcontroller programming and experimental results showcase the effectiveness of the proposed system in achieving precise control over motor speed and efficiency. The challenges faced during the project are discussed, along with the corresponding solutions implemented. The report concludes with a summary of achievements and suggestions for future enhancements. Overall, this project contributes to the field of motor control systems, offering a reliable and efficient solution for induction motor control through AC PWM with microcontroller integration

THE IMPACT OF USING INTERNAL CONTROL UNDER THE ELECTRONIC OPERATING SYSTEM

Internal control tools constitute one of the modern frameworks approved by the Committee of Sponsoring Organizations (COSO). "And the one which takes into account setting the objectives first, then estimating the possibility of those objectives being exposed to threats, then internal control procedures are designed that reduce the risks, and at the same time provide reasonable guarantees for achieving the organization's objectives and introducing all modern developments into the system. The contemporary trend of internal control focuses on the integration between managing the risks to which the organization is exposed and the internal control frameworks (Enterprise Risk Management- Integrated Framework) and its abbreviation (ERM), which provides the organization Management in conditions of uncertainty related to risks and their probabilities to be able to create and preserve value. The continuous developments in internal control systems, on the one hand, and competition between economic units, on the other hand, have led to many challenges through the use of modern technology and the innovation of new methods of development, which required the development of all sub-systems of control, including internal control systems, and then review the internal control structure and the extent of its acceptance of this computer-based technology and the effectiveness of its devices in providing information that supports the system's ability to adopt it. Based on this, the research topic was divided into five sections. The first section dealt with the research methodology, the second section dealt with the theoretical framework of the internal control system, and the third section dealt with the problems and limits of using electronic computers. The fourth section was devoted to studying the impact of using computers. The electronic section focuses on the components of the internal control system, while the fifth section is dedicated to stating the most important conclusions and recommendations.

Agent-Based System for Continuous Control and its Application to Activated Sludge Process

This paper presents a concept of architecture and ontology layouts for development of multiagent model-based predictive control systems. The presented architecture provides guidelines to simplify development of agent-based systems and improve its maintainability. The proposed multiagent system (MAS) layout is split into multiple subsystems that include agents dedicated to performing assigned tasks. MAS implementation was prepared which can use provided algorithms, actuators and can react to changes in its environment to reach the best available control quality. An example of MAS based on the proposed architecture is shown in application of dissolved oxygen (DO) concentration control in a laboratory activated sludge setup with biological reactor. For that application, MAS incorporates agent-based controllers from Boundary-Based Predictive Controllers (BBPC) family. Presented experiments prove flexibility, resilience and online reconfiguration ability of the proposed multiagent system.

Parental warmth buffers the negative impact of weaker fronto-striatal connectivity on early adolescents' academic achievement.

In past decades, the positive role of self-control in students' academic success has attracted plenty of scholarly attention. However, fewer studies have examined the link between adolescents' neural development of the inhibitory control system and their academic achievement, especially using a longitudinal approach. Moreover, less is known about the role of parents in this link. Using large-scale longitudinal data from the Adolescent Brain Cognitive Development (ABCD) study (N = 9574; mean age = 9.94 years at baseline, SD = .63; 50% girls), the current study took an integrative biopsychosocial approach to explore the longitudinal link between early adolescents' fronto-striatal connectivity and their academic achievement, with attention to the moderating role of parental warmth. Results showed that weaker intrinsic connectivity between the frontoparietal network and the striatum was associated with early adolescents' worse academic achievement over 2 years during early adolescence. Notably, parental warmth moderated the association between fronto-striatal connectivity and academic achievement, such that weaker fronto-striatal connectivity was only predictive of worse academic achievement among early adolescents who experienced low levels of parental warmth. Taken together, the findings demonstrate weaker fronto-striatal connectivity as a risk factor for early adolescents' academic development and highlight parental warmth as a protective factor for academic development among those with weaker connectivity within the inhibitory control system.

Development of a combined harvester navigation control system based on visual simultaneous localization and mapping-inertial guidance fusion

Recently, the existing unmanned systems of combine harvesters mostly adopts satellite navigation scheme, lacking real-time observation of harvesting adjustment. To improve the operational efficiency of combine harvester assisted navigation operation, this paper designs a combine harvester navigation control system based on vision simultaneous localization and mapping (SLAM)-inertial guidance fusion. The system acquires field image information and extracts the crop boundary line as the navigation datum by binocular camera. First, the system acquires field image information through binocular camera and extracts the crop boundary line as the navigation datum. Second, fusing camera and inertial guidance information to obtain the real-time relative position of a combine harvester. Third, constrained optimization of image and inertial guidance information is achieved through a sliding window optimization method based on tightly coupled nonlinear optimization. Finally, obtain the position of the combine harvester relative to the navigation datum line, and output a signal to the steering mechanism to realize the combine harvester in the field intelligent positioning navigation control. The system consists of binocular camera, inertial measurement unit, motorized steering wheel, monitor display, angle sensor and microcontroller. During field testing, the system underwent repetitive harvesting trials over a distance of 25 m.. The testing machine performs field operations at a speed of 0.9-1.5 m/s, with an average lateral deviation range of 2.21-8.62 cm, a standard deviation range of 0.13-4.21 cm and an average cutting rate range of 92.2%-96.0%, achieving the expected harvesting effect.

Development of a hand prosthesis control system with tactile feedback

Development of a hand prosthesis control system with tactile feedback

Development and design approach of an sEMG-based Eye movement control system for paralyzed individuals

Development and design approach of an sEMG-based Eye movement control system for paralyzed individuals

Human-inspired similarity control system: Enhancing line-following robot perception

Human-Inspired Control (HIC) holds promise for endowing machines with human-like cognition, decision-making, and adaptability. In this study, we employ a fusion of cognitive modeling, machine learning, and control theory as the foundational architecture and empirically validate its suitability for robot control within the realm of HIC. Fuzzy logic stands out as a viable approach for HIC control, wherein control rules can be devised drawing from human intuitive inspiration. Specifically, this study explores similarity inference control systems in robotics, with the objective of enhancing line-following control as an alternative to fuzzy systems. The experimental optimization results provide insights into the advantages and limitations of the similarity inference control system. Despite achieving performance comparable to that of traditional two-stage fuzzy control systems, careful consideration of noise sensitivity is paramount. While the similarity inference approach streamlines implementation and obviates the necessity for expert-designed fuzzy rules, its susceptibility to noise can compromise performance, particularly in noisy environments. These considerations are pivotal for the development of control systems aimed at mitigating noise sensitivity, enhancing task-specific performance, and ensuring the adaptability and robustness of line-following robots. To tackle this challenge, we both discuss and experimentally evaluate potential solutions and their applicability in this paper.

Development of undulator motion control system with redundant protections

The undulator is a key luminous component of the Shanghai high repetition rate x-ray free electron laser and extreme light facility, and the motion control system is an important part of the undulator. The main task of the motion control system is to complete the high precision adjustment of the gap and taper between the upper magnetic pole girder and lower magnetic pole girder, so as to adjust the wavelength of the radiation light. According to the development requirements of the motion control system for the U26 undulator, which is a conventional permanent magnet, this paper proposes a new motion control system with double position feedback closed-loop control, including programmable logic controller (PLC) closed-loop control based on position feedback by a linear absolute encoder (LAE) and driver closed-loop control based on position feedback by a rotary absolute encoder (RAE) of a servo motor. Multiple motion tests of the U26 undulator have shown that the repeatability accuracy of the gap is ±50 nm and the gap drive can reach 0.1 µm step size, the error of which is about ±20 nm according to the feedback of the LAE. The conclusion can be drawn that the motion step size and repeatability accuracy of the motion control system for the U26 undulator are improved by one order of magnitude on the basis of meeting all technical requirements. At the same time, motor torque protection, motor temperature protection, and redundant position protection with RAE can further improve the reliability and safety of the motion control system for the U26 undulator.

Development of beamline control system for Hefei Advanced Light Facility

The design and the control scheme of beamline control system for Hefei Advanced Light Facility (HALF;Hefei, People's Republic of China) which is under construction is described in this paper.It mainly includes, equipment protection safety, personal protection safety,fast signal interlock and status monitoring and fault diagnosis system. In this control system ,the human presence perception function has been added to the personal protection safety system. Multi-parameter status monitoring of key equipment on beamline station can automatically alarm equipment failures and send relevant personnel timely notifications. At the same time, it can also provide early warning on the health status of key equipment on the beamline. The system not only improves the safety and reliability of beamline control, but also increases the degree of automation and intelligence, reduces the workload of operation and maintenance personnel, and improves operation and maintenance efficiency.

Development of the interferometer control system on HFRC

The CO2 laser interferometer is an important diagnostic system in the HUST Field Reversed Configuration (HFRC) and can provide routine electron density measurement for plasma experiments. A commercial laser (λ = 10.6 μm) with a maximum output power of 20 watts is used as the laser source for the interferometer, which can pose a threat to the safety of experimenters and experimenters' experimental environment. Moreover, most of the components of the system are sensitive to the temperature and humidity of the environment. For the aim of safe and stable operation, it is necessary to develop a CO2 interferometer control system (CICS). A module was innovatively designed with a servo to regulate power according to commands and protect timely when a fault takes place. Additionally, a special device using a stepper motor can automatically regulate and release the laser according to the different stages of the experiment. The hardware design of the CO2 control system incorporates the ESP32 microcontroller, servo and stepper motor, which can automatically monitor, regulate and protect in case a fault takes place. The software implementation of the interaction is based on the OneNET platform and is first used on the CO2 interferometer, linking in the Internet based on Internet of Things (IoT) technologies to each sensor and device in the control system, which can achieve visualization monitoring and remote control. With the operation of CICS, the CO2 interferometer can run stably and be timely protected.

Modeling and adaptive neuro-fuzzy inference system control of quarter electric vehicle

Electric vehicles (EVs) have gained importance in recent years, prompting the development of several control systems to improve their efficiency and performance. In this work, a quarter electric vehicle (QEV) was controlled using a conventional proportional integral derivative (PID) and fuzzy controller to examine and compare with the response of the adaptive neuro-fuzzy inference system (ANFIS) controller. The response of the ANFIS controller was evaluated using MATLAB/Simulink according to different parameters and compared with those of other controllers. In addition, the simulation was based on different driving conditions such as the acceleration and deceleration modes and the type of road: wet and dry. The simulations were carried out on a longitudinal electric vehicle model based on a brushless DC motor, including the Pacejka tire model. The results showed that the ANFIS controller outperformed the PID and fuzzy logic controllers, providing superior dynamic responsiveness and stability when the ANFIS controller smoothly followed the input speed and the longitudinal slip value reached 3%.

MODELS OF THE SYSTEM OF COLLECTIVE SELF-ORGANISATION OF UNMANNED AERIAL VEHICLES USING ARTIFICIAL INTELLIGENCE

The article examines the current state and trends in the development of unmanned aerial vehicles (hereinafter - UAVs), methods and means of their control, self-organisation, flight routing, as well as the involvement of artificial intelligence technologies in the UAV teams system. The main trends in the use of UAVs in the defence sector are analysed. The key problems in this area are highlighted, as well as the shortcomings of existing methods and systems. The article analyses scientific works of domestic and foreign scholars, studies problematic issues and suggests ways of their solution. The introduction of artificial intelligence into the UAV control system has significant potential and makes the development of these technologies urgent. The development of UAV collective control systems, including those using artificial intelligence, allows for the effective use of technology in various fields, ensuring increased coordination, functionality and overall efficiency. At the same time, despite active research in this area, a number of problems related to the development of methods and algorithms for group work still remain unresolved. The issue of integrating information technologies created on their basis and the specifics of their implementation in collective intelligence systems in order to increase the efficiency of solving complex formalised tasks is not sufficiently studied. A method of self-organisation of the UAV team with decentralised control is proposed, in which a number of functions (route planning, distribution of roles, determination of optimal actions, obtaining and processing information) assigned to the onboard control system of the robotic air complex can be performed by each element of the UAV team system due to their self-organisation. The practicality of this method lies in the fact that the UAV's artificial intelligence will constantly self-learn and improve, and if necessary, can be reprogrammed to meet the required conditions of the task. As a consequence, the results and time required to complete missions will improve significantly, while the number of control operators will decrease. It solves a number of problems and shortcomings related to the organisation of the management system, route planning, distribution of roles, speed and completeness of receiving, processing and transmitting information, which in turn improves the security and performance of the system.