Development Of Control System Research Articles

Automatic control of UAVs: new adaptive rules and type-3 fuzzy stabilizer

AbstractUnmanned Aerial Vehicles (UAVs) have become important in an extensive range of fields such as surveillance, environmental monitoring, agriculture, infrastructure inspection, commercial applications, and many others. Ensuring stable flight and precise control of UAVs, especially in adverse weather conditions or turbulent environments, presents significant challenges. Developing control systems that can adapt to these environmental factors while ensuring safe and reliable operation is a main motivation. Considering the challenges, first, an adaptive model is identified using the input/output data sets. New adaptation laws are obtained for dynamic parameters. Then, a Type-3 (T3) Fuzzy Logic System (FLS) is used to compensate for the error of dynamic identification. T3-FLS is tuned by a sliding mode control (SMC) strategy. The robustness is analyzed considering the adaptation error using the SMC approach. The main idea is that the basic dynamics of UAVs are taken into account, and adaptation laws are designed to enhance the modeling accuracy. On the other hand, an optimized T3-FLS with SMC is introduced to eliminate the adaption errors and ensure robustness. Several simulations show that known parameters converge under uncertainty, and the stability is kept, well. Also, output signals follow the desired trajectories under dynamic perturbations, identification errors, and uncertainties.

Unique algorithm for the evaluation of embryo photon emission and viability

Living cells have spontaneous ultraweak photon emission derived from metabolic reactions associated with physiological conditions. The ORCA-Quest CMOS camera (Hamamatsu Photonics, Japan) is a highly sensitive and essential tool for photon detection; its use with a microscope incubator (Olympus) enables the detection of photons emitted by embryos with the exclusion of harmful visible light. With the application of the second law of thermodynamics, the low-entropy energy absorbed and used by embryos can be distinguished from the higher-entropy energy released and detectable in their environment. To evaluate higher-entropy energy data from embryos, we developed a unique algorithm for the calculation of the entropy-weighted spectral fractal dimension, which demonstrates the self-similar structure of the energy (photons) released by embryos. Analyses based on this structure enabled the distinction of living and degenerated mouse embryos, and of frozen and fresh embryos and the background. This novel detection of ultra-weak photon emission from mouse embryos can provide the basis for the development of a photon emission embryo control system. The ultraweak photon emission fingerprints of embryos may be used for the selection of viable specimens in an ideal dark environment.

Hybrid Model Based Autonomous System for Controlling Double Side Grinding Process

Manufacturing uncertainty is rising due to diverse customer demands and new technologies. These challenges are propelling us into the Industry 4.U (Industrial Revolution 4.0 Upgrade) and Industry 5.0 era, focusing on human-centric manufacturing and value creation, necessitating digital transformation. This transformation relies on the core technologies of the 4th Industrial Revolution (Data Analytics, Connectivity, Autonomy, and Collaboration) to perform value-creating processes autonomously and reliably. To adapt, Industry 5.0-oriented Smart Manufacturing architecture was designed. In this system, process planners collaborate with generative AI to generate optimal process plans based on new specifications, pursuing human-centered manufacturing. The manufacturing system is reconfigured with modular technology, and each process is autonomously controlled as a 4.U/5.0-oriented production system. This paper focuses on developing an autonomous process control system for Industry 4.U/5.0-oriented Smart Manufacturing. The study aims to stabilize the quality of the brake disc grinding process in an automotive parts company. The developed control system has been tested on a car’s brake disc manufacturing line, with experimental results showing the reliability of the DT and AI in controlling the grinding process.

FPGA-based control system for real-time driving of UHD Micro-LED display with color calibration

Micro-LED technology offers numerous advantages, including high brightness, low power consumption, and superior color performance. However, driving micro LED displays requires complex control algorithms and high-speed data processing. To address these challenges, this paper presents the development of a real-time FPGA-based control system for a 68-inch 4K/60Hz micro-LED display. The objective of this project was to create a high-performance control system capable of driving the micro-LED display with precise color accuracy, supporting 10-bit color depth for enhanced color rendition. One crucial aspect of the system is color calibration, which ensures accurate color reproduction across the display, maintaining consistent and vibrant colors. By incorporating advanced color calibration techniques, the system achieves excellent color consistency and fidelity, providing a visually stunning viewing experience. Moreover, the system incorporates hot plug functionality, allowing for seamless reconnection of the display after Ethernet disconnection. This feature ensures uninterrupted operation and enhances user experience. In FPGA design, the proposed system demonstrates the feasibility and effectiveness of real-time control in driving micro-LED displays, offering improved color performance and reliable operation in various applications.

Innovative AI-Enhanced Ice Detection System Using Graphene-Based Sensors for Enhanced Aviation Safety and Efficiency.

Ice formation on aircraft surfaces poses significant safety risks, and current detection systems often struggle to provide accurate, real-time predictions. This paper presents the development and comprehensive evaluation of a smart ice control system using a suite of machine learning models. The system utilizes various sensors to detect temperature anomalies and signal potential ice formation. We trained and tested supervised learning models (Logistic Regression, Support Vector Machine, and Random Forest), unsupervised learning models (K-Means Clustering), and neural networks (Multilayer Perceptron) to predict and identify ice formation patterns. The experimental results demonstrate that our smart system, driven by machine learning, accurately predicts ice formation in real time, optimizes deicing processes, and enhances safety while reducing power consumption. This solution holds the potential for improving ice detection accuracy in aviation and other critical industries requiring robust predictive maintenance.

Time/frequency domain analysis of detonation wave propagation mechanism in a linear rotating detonation combustor

Controlling the rotating detonation combustor (RDC) is crucial for enhancing propulsion system safety, efficiency, and overall performance. Swift and precise identification of the operational mode of RDC is of great significance for refining the propulsion dynamics of detonation-based technologies. In this study, two-dimensional numerical simulations are performed to investigate time-domain characteristics and frequency-domain characteristics of various operational modes of the RDC, hydrogen–air mixtures are used as fuel, and fast Fourier transformation (FFT) is applied to the analysis of frequency-domain. The alteration of the RDC operational mode is achieved through adjustments to the chemical equivalent ratio (ER) of the fuel, spanning from 0.52 to 3.50. Our findings reveal four distinct operational modes within this equivalent ratio range: ignition failure (ER ≤ 0.52 and ER ≥ 3.50), collision failure (ER = 0.85,1.60 and 1.65), single wave (0.53 ≤ ER ≤ 0.80 and 1.70 ≤ ER ≤ 3.40) and multiple wave mode (0.90 ≤ ER ≤ 1.50). For the ignition failure mode, the detonation waves in RDC decouple from the flame and fail after ignition. In the collision failure mode, the inability of detonation waves to propagate sustainably within the RDC is evident. Frequency-domain analysis underscores the significance of a characteristic low-frequency (0.3 kHz) in distinguishing this mode from others. For single wave mode, it represents the steady and continuous propagation of a detonation wave in the RDC. Frequency-domain analysis reveals characteristic frequencies approximately equal to integer multiples of the propagation frequency (fD), where fD ∼ 10fD serves as the primary characteristic frequencies. The multiple wave mode can be categorized into two subtypes: the forward and reverse single detonation waves appear alternately or the forward and reverse single detonation waves finally co-direction double detonation waves. In the former subtype, characteristic frequencies are integer multiples of the fD, but the amplitude distribution is different with single wave mode. In contrast, primary characteristic frequencies of the latter subtype are fD ∼ 4fD, accompanied by two adjacent characteristic frequencies near each integer multiples of fD. These findings promise advancements in the controllability of RDC and lay the foundations for the development of RDC control systems.

Multi-criteria analysis of the continuous operation of a membrane photobioreactor to treat sewage: Modeling and sensitivity analysis

Microalgae-based wastewater remediation is aligned with circular economy principals but successful large-scale facilities are scarce due to the limited knowledge of the interactions between all relevant variables and the scarce development of control and automation systems. Machine learning (ML) methods have the potential predict performance in microalgae photobioreactors, with the aim to optimize it. Some attempts have been performed under controlled lab conditions which prevents the direct application of their results to industrial scale. This study aims to develop ML models for the prediction of nutrient removal, biomass production and photosynthetic efficiency using a database obtained from 1.5-y operation of a pilot-scale membrane photobioreactor (MPBR) that treated sewage. A total of 14 inputs and 6 outputs were selected. Random forests (RF), boosted trees (BT), multilayer perceptron (MLP), support vector machine methods (SVM) were tested. The lowest prediction errors were obtained with the MLP model, allowing the developed tool to be used as an alternative to mechanistic models. Large ranges of data were used, considering the variability of factors in the diurnal cycle whose influence on the variability of the data is usually neglected. Using global sensitivity analysis (GSA), input-output relationships were verified, reflecting relevant number of variables showing significant values of Shapley Indices. Additionaly, partial dependence plots showed both linear and nonlinear depending on the selected inputs and outputs. Finally, using ML models, multi-criteria optimization of operating parameters was performed for two variants: a) optimization of operating parameters (HRT, SRT, and air flowrate (Fair)); and b) optimization of operating parameters and influent nutrient loads (HRT, SRT, Fair, nitrogen and phosphorus loading rates).

Controlled Fluid Flow Without Controlling Pump Through Arduino

Various Industries like Chemical, Energy, Nuclear, Process, Manufacturing, Healthcare, and Agriculture require the flow rate to be controlled. It may be constant, steady state, or between the specific upper and lower range. The article explained the development of a cost-effective fluid flow control system using an Arduino microcontroller. The direct use of the pump is not good because there are a lot of challenges (Corrosion, Power fluctuations, Pressure leak) with the pump and with time as it gets old, the chances of getting good, desired flow from it are not possible. We achieved the controlled flow rate (volume rate and mass rate) in a very precise and cost-effective manner. Because the flow corresponding to the height is steadier and more constant than getting steady flow from the pump. Hence, our solution is that we don’t get direct flow from the pump but from Tank B. Tank B must be very carefully designed (especially its height and cross section) to get the desired value of the flow rate from it, as per Bernoulli’s Theorem the flow velocity has a direct relation with the height. We just used the pump to refill tank B up to the desired level. In this case, the flow rate only varies when there is a larger change in the head of the tank from which it is coming (It is tank B in our case). The pump automatically becomes ON when the flow rate drops below 2 and fills tank B again to the height which corresponds to the flow rate 3 and when the flow rate becomes 3 the pump becomes OFF automatically. The process is repeated until the desired process is completed. After Completion the valve from Tank B can be closed. The Hardware integrations, Software Programming Logic, and Physical experimental setup are discussed.

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

Design of Automotive Collision Avoidance Lower Position Controller Based on GA Optimization

Abstract To improve the control stability of the lower controller in the vehicle collision avoidance system, and solve the problem of traditional fuzzy PID not providing good dynamic performance under large parameter changes, a lower controller for vehicle collision avoidance based on genetic algorithm optimized fuzzy proportional-integral-derivative (PID) control was studied. The vehicle collision avoidance system collects real-time motion information of the preceding vehicle through millimeter-wave radar and calculates the safety distance. The safety distance model and upper controller of the system calculate the required acceleration of the vehicle. Afterwards, the expected acceleration of the vehicle is controlled by the lower controller of fuzzy PID. At the same time, the improved GA is used to adjust the PID parameters, solving the problem of poor robustness of the lower controller. A simulation platform was built to simulate the longitudinal vehicle collision avoidance control system jointly. The simulation results show that the lower controller designed based on this control algorithm has better stability and meets the collision avoidance requirements, providing an important basis for the development of vehicle control systems.

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.