Industrial Control Research Articles

Improving the Dynamics of an Electrical Drive Using a Modified Controller Structure Accompanied by Delayed Inputs

This paper presents the operation of a modified speed controller with a standard PI/PID structure that includes the preprocessing of the controller’s input signal, focusing on the past behavior of control errors. The modification involves adding a delay line, with the outputs of the individual line segments summed with a weighting method, as detailed in the paper. One of the significant advantages of this method is its use of a standard industrial controller structure, which makes it highly practical and easily implementable in existing systems. By relying on well-established control frameworks, this approach reduces the need for specialized hardware or complex modifications, allowing for smoother integration and lower implementation costs. The delay-based signal shaping shows excellent properties for the electric drive system powered by a hard-switching PWM converter. The set of weighted delays acts as a filter whose parameters are chosen using the quality function to test different configurations for optimal performance. When tested in a speed control system for a Permanent Magnet Synchronous Motor, the modifications improved the control quality index, indicating better performance and efficiency. Importantly, the system allows for reducing or eliminating the gain in the differentiating part of the controller, which decreases motor current chattering and noise. This paper includes an experimental verification of the proposed solution in a laboratory setting under semi-industrial conditions.

Exploring the effects of RNNs and deep learning frameworks on real‐time, lightweight, adaptive time series anomaly detection

SummaryReal‐time, lightweight, adaptive time series anomaly detection is increasingly critical in cybersecurity, industrial control, finance, healthcare, and many other domains due to its capability to promptly process time series and detect anomalies without requiring extensive computation resources. While numerous anomaly detection approaches have emerged recently, they generally employ a single type of recurrent neural network (RNN) and are implemented using a single type of deep learning framework. The impacts of using various RNN types across different deep learning frameworks on the performance of these approaches remain unclear due to a lack of comprehensive evaluations. In this article, we aim to investigate the impact of different RNN variants and deep learning frameworks on real‐time, lightweight, and adaptive time series anomaly detection. We reviewed several state‐of‐the‐art anomaly detection approaches and implemented a representative approach using several RNN variants supported by three popular deep learning frameworks. A thorough evaluation was conducted to analyze the detection accuracy, time efficiency, and resource consumption of each implementation using four real‐world, open‐source time series datasets. The results show that RNN variants and deep learning frameworks have a significant impact. Therefore, it is crucial to carefully select appropriate RNN variants and deep learning frameworks for the implementation.

Pengendalian Biaya Kedelai Home Industry Tahu-Tempe Mbak Sri

This study aims to analyze the material cost control in Mbak Sri's home industry of tofu and tempeh production. Material costs constitute a significant portion of the total production costs, and effective control measures are crucial for ensuring profitability. The research employs a qualitative approach, utilizing observation and in-depth interviews to gather data. The findings reveal that Mbak Sri implements several cost control strategies, including careful material selection, efficient utilization, and waste minimization. However, challenges arise due to fluctuating raw material prices and limited bargaining power. The study recommends implementing a comprehensive material management system, establishing strategic supplier relationships, and exploring alternative sourcing options to enhance cost control measures. By addressing these recommendations, Mbak Sri's home industry can improve its overall operational efficiency and profitability.

Potential of water-soluble chitosan Schiff bases extracted from Metapenaeus dobsoni shells as effective corrosion inhibitors

Corrosion causes significant economic losses and structural failures in industries, highlighting the need for eco-friendly inhibitors. Chitosan (CS), a biodegradable and non-toxic biopolymer, shows potential, though its limited water solubility restricts its applications. To overcome this challenge, this study presents the synthesis of two water-soluble chitosan Schiff bases (CSBs) derived from the shells of Metapenaeus dobsoni (M. dobsoni). The extracted CS exhibits a remarkable degree of deacetylation exceeding 95 %, which was subsequently modified through reactions with o-vanillin (2-hydroxy-3-methoxybenzaldehyde) (CSB I) and 2,3-dihydroxybenzaldehyde (CSB II). Structural characterization using spectroscopic techniques confirmed the successful formation of CSBs. Electrochemical measurements were employed to assess the corrosion resistance of mild steel in 0.5 M HCl with varying concentrations of CSB I and CSB II. The results revealed superior corrosion inhibition by CSB II (% IE = 94.48 %) compared to CSB I (% IE = 88.80 %). The methoxy group in CSB II contributed to its higher electron density and enhanced adsorption, leading to better surface coverage and corrosion resistance. Both inhibitors followed the Langmuir isotherm, suggesting a mix of physisorption and chemisorption. These CSBs are promising for corrosion control in industries like pipelines, storage tanks, construction materials, and acid pickling.

Optimizing olefin purification: An artificial intelligence-based process-conscious PI controller tuning for double dividing wall column distillation

This study investigates the light olefin separation using dual dividing wall columns (DWCs) and compares various proportional-integral (PI) controller tuning methods. A novel process-conscious reinforcement learning (RL) approach, utilizing Deep Deterministic Policy Gradient (DDPG), is developed by incorporating domain-specific knowledge into the reward function to ensure compliance with critical process variables and product purity. The DDPG-based PI tuning is evaluated against traditional methods such as Aspen’s recommended initial tuning, Ziegler-Nichols (ZN), and Cohen-Coon (CC). The result shows that the DDPG method significantly enhances control stability and accuracy, reducing error by factors of 11.9, 2.3, and 1.6 compared to Aspen, ZN, and CC, respectively. Additionally, DDPG demonstrates superior energy efficiency, consuming 13% less energy than the next best method. It also reduces purification costs by up to 13.5% and CO2 emissions by 20% compared to other methods. This study highlights the potential of integrating advanced RL techniques into industrial process control, delivering substantial improvements in stability, energy efficiency, economic, and environmental performance.

Cybersecurity Fundamentals Are Not Just for Industrial Control Systems: Guidance and Direction Are Available

Cybersecurity Fundamentals Are Not Just for Industrial Control Systems: Guidance and Direction Are Available

The Application of PLC in Industry Fields

This article explores the widespread application and importance of the Programmable Logic Controller (PLC) in industry. PLC, as an industrial controller designed specifically for industrial environments, occupies an important position in the field of industrial automation control due to its advantages of being less susceptible to interference, high reliability, easy programming, fast installation, and small size. With the rapid development of PLC technology, great progress has been made in the development of technology, hardware, and software, resulting in increasingly powerful functionalities and significantly enhanced system development and compatibility. This paper mainly analyzes the design ideas and applications of PLC in life and industry in detail, aiming to research PLC and find more scenes in industry fields in which PLC can participate. PLC has a wide range of applications and significant importance in industry. The continuous progress and innovation of its technology will bring more opportunities and challenges to the field of industrial automation control. In the future, with the deepening development of advanced manufacturing models such as Industry 4.0 and intelligent manufacturing, PLC technology is expected to play a greater role in more fields and promote industrial automation to a higher level.

Design and Investigation of a High-Performance Quartz-Based SAW Temperature Sensor

In this work, a surface acoustic wave (SAW) temperature sensor based on a quartz substrate was designed and investigated. Employing the Coupling-of-Modes (COM) model, a detailed analysis was conducted on the effects of the number of interdigital transducers (IDTs), the number of reflectors, and their spacing on the performance of the SAW device. The impact of the transversal mode of quartz SAWs on the device was subsequently examined using the finite element method (FEM). The simulation results indicate that optimizing these structural parameters significantly enhances the sensor’s sensitivity and frequency stability. SAW devices with optimal structural parameters were fabricated, and their resonant frequencies were tested across a temperature range of 25–150 °C. Experimental results demonstrate that the SAW temperature sensor maintains high performance stability and data reliability throughout the entire temperature range, achieving a Bode-Q of 7700. Furthermore, the sensor exhibits excellent linearity and repeatability. An analysis of the sensor’s response under varying temperature conditions reveals a significant temperature dependency on its Temperature Coefficient of Frequency (TCF). This feature suggests that the sensor possesses potential advantages for applications in industrial process control and environmental monitoring.

Artificial Neural Network-Based Data-Driven Parameter Estimation Approach: Applications in PMDC Motors

The optimal performance of direct current (DC) motors is intrinsically linked to their mathematical models’ precision and their controllers’ effectiveness. However, the limited availability of motor characteristic information poses significant challenges to achieving accurate modeling and robust control. This study introduces an approach employing artificial neural networks (ANNs) to estimate critical DC motor parameters by defining practical constraints that simplify the estimation process. A mathematical model was introduced for optimal parameter estimation, and two advanced learning algorithms were proposed to efficiently train the ANN. The performance of the algorithms was thoroughly analyzed using metrics such as the mean squared error, epoch count, and execution time to ensure the reliability of dynamic priority arbitration and data integrity. Dynamic priority arbitration involves automatically assigning tasks in real-time depending on their relevance for smooth operations, whereas data integrity ensures that information remains accurate, consistent, and reliable throughout the entire process. The ANN-based estimator successfully predicts electromechanical and electrical characteristics, such as back-EMF, moment of inertia, viscous friction coefficient, armature inductance, and armature resistance. Compared to conventional methods, which are often resource-intensive and time-consuming, the proposed solution offers superior accuracy, significantly reduced estimation time, and lower computational costs. The simulation results validated the effectiveness of the proposed ANN under diverse real-world operating conditions, making it a powerful tool for enhancing DC motor performance with practical applications in industrial automation and control systems.

Automatic control strategy of electronic and electrical engineering based on FPGA

Field Programmable Gate Array (FPGA) have demonstrated significant potential in the field of automation control due to their flexibility, programmability, and high performance. This study designs an FPGA-based automation control strategy for motor control systems, encompassing four core modules: signal acquisition, signal processing, control algorithm, and output drive. The signal acquisition module employs the AD9280 high-speed, high-precision ADC chip to achieve rapid and accurate signal acquisition. The signal processing module utilizes digital filtering and FFT analysis within the FPGA to extract key information such as motor speed. The control algorithm module adopts an adaptive fuzzy control strategy that fine-tunes control rules in real-time to ensure precise control. The output drive module generates PWM signals via the FPGA to drive the motor, guaranteeing efficient and stable operation. Experimental results indicate that the FPGA-based control strategy significantly outperforms traditional methods in terms of response speed and stability, capable of meeting the high-performance requirements of modern industrial motor control systems. This research provides new technical insights and practical references for the field of automation control in electrical and electronic engineering.

PID-based control strategies for enhancing stability and precision in electro-hydraulic actuation systems

Electro-hydraulic actuation systems are essential in industrial applications but often face challenges related to nonlinearity, leading to instability and reduced precision. This paper explores the application of Proportional-Integral-Derivative (PID) control strategies to enhance the stability and precision of these systems. A detailed system model is developed, and the PID controller is designed and tuned using optimization techniques. Simulation results demonstrate improvements in performance metrics, such as reduced overshoot, settling time, and steady-state error. The proposed control strategy is validated experimentally on a physical setup, confirming its effectiveness in real-world applications. The findings show that PID-based control significantly enhances both stability and precision in electro-hydraulic actuation systems, offering a practical solution for industrial control challenges.

Research of Optical Fiber Sensing Technology in Bridge Detection

Fiber optic sensing technology is a new type of detection technology that realizes information transmission through total reflection of light and photoelectric conversion. It has been used in the fields of medicine, military, communication, transportation, industrial control, scientific research, etc., especially in the field of bridges for decades.This paper summarizes the research status of optical fiber sensing technology and optical fiber in bridge detection, and reviews the application of optical fiber in bridge detection. First, this paper introduce the optical fiber technology, its working principle and application advantages. Secondly, the feasibility of optical fiber sensing technology in bridge detection is demonstrated through concrete application cases. It includes the detection of bridge structure and the detection of bridge material structure. Through the optical fiber sensing technology, this paper can detect the hidden dangers of the bridge in a timely and non-destructive manner Finally, this paper prospect the future development of optical fiber technology in the field of bridge detection.

Predicting Hass Avocado Maturity with NIR Spectroscopy for Non-invasive Dry Matter Estimation in Hass Avocados

Aims: To develop a rapid, non-invasive method for predicting Hass avocado maturity using near- infrared diffuse reflectance spectroscopy (NIR-DRS) combined with machine learning algorithms, and to identify the optimal NIR wavelength range for accurate dry matter content prediction. Study Design: An experimental design involving spectral data collection from Hass avocados and the development of machine learning models for dry matter prediction. Methodology: Spectral data from 200 Hass avocados were collected using near-infrared diffuse reflectance spectroscopy (900-2500 nm). To improve the quality of the spectral data and reduce noise, standard normal variate was used to correct for scattering and remove unwanted variability in the spectral data. PCA was then performed to reduce the dimension of the spectral data while retaining the most significant variance. Following preprocessing, machine learning models, including Convolutional Neural Networks (CNN), were trained to predict dry matter content, and the optimal wavelength range was determined for accurate prediction. Results: The CNN model demonstrated superior performance for dry matter prediction with R² of 0.91 in the testing set. The wavelength range of 1000-1500 nm was identified as optimal, offering accurate predictions while reducing computational complexity. This range shows potential for developing cost-effective NIR devices for real-time maturity assessment. Conclusion: NIR spectroscopy combined with machine learning offers a non-invasive, accurate method for predicting avocado dry matter, with potential applications for quality control in the avocado industry. The findings demonstrate that focusing on specific wavelength ranges can lead to more affordable and efficient NIR solutions.

Assessing Dietary Supplements, Nutritional Deficiencies, and the Potential of Personalized Nutrition

The COVID-19 pandemic has heightened public awareness of health, leading to a surge in the consumption of dietary supplements, particularly vitamins and minerals. These micronutrients are vital for numerous physiological processes and overall health maintenance. However, the rapid expansion of the health product market has also resulted in the proliferation of substandard items and the risk of misuse or overconsumption. This study analyzes the ingredients in various health products and assesses the nutritional deficiencies prevalent in local populations. It emphasizes the importance of balanced nutrition while cautioning against the excessive intake of certain supplements. The research also explores the potential of personalized nutrition, driven by advances in genomics and biotechnology, to provide tailored supplement recommendations. Additionally, the study highlights the need for stricter regulations and improved quality control in the health product industry to ensure consumer safety and confidence. By offering insights into the nutritional needs of individuals, this study aims to guide informed and responsible supplement use.

Fabrication of a molecularly imprinted poly(methyl methacrylate) decorated graphite electrode for detection of aloe emodin in Aloe vera

In this work, the authors represent a comprehensive approach for the development and validation of a novel graphite-supported molecularly imprinted poly(methyl methacrylate) electrode for the selective and sensitive detection of aloe emodin (AE) in aloe-based cosmetic products. The electrocatalytic activity of the electrode was evaluated using cyclic voltammetry and differential pulse voltammetry techniques in phosphate buffer saline. Surface morphology, structural characteristics, and imprinting endorsement were investigated using field emission scanning electron microscopy, Fourier-transform infrared spectroscopy, and UV-Vis spectroscopy. Under the optimal conditions, the electrode showed an oxidative differential pulse voltammetry peak at -0.2931±0.011 V (vs. Ag/AgCl, saturated KCl). The current was increased linearly with increasing concentrations of AE from 0.0005 to 350 µM, with a low detection limit of 0.0003 µM. The voltammetric analysis has been validated by reverse-phase high-performance liquid chromatography (RP-HPLC) methods. A partial least square regression model was developed to correlate the results obtained from the proposed system with the reference values obtained by RP-HPLC. The model showed a high prediction accuracy of 95.95 % for the detection of AE in cosmetic samples. The developed electrode provides a low-cost, rapid, easy, and convenient method for the detection of AE in cosmetic products, offering potential benefits for quality control and safety assessment in the cosmetic industry.

ASIP Performance Enhancement by Hazard Control through Scoreboard

The application-specific instruction set processor (ASIP) has been gradually accepted in AI, communication, media, game and industry control. The digital signal processor (DSP) is a typical ASIP, whose benefits include high performance in specific domains, low power consumption, high flexibility and low silicon consumption. One of the challenges for DSP design is to handle problems induced by datapath acceleration. The datapath acceleration (instruction fusion, black box instructions) induces control complexities. To most efficiently utilize hardware, control challenges can be summarized as RAW (Read-After-Write) handling, hardware hazard handling, and WAW (Write-After-Write) handling. Both an advanced compiler and hardware hazard handler can be used as solutions. In this paper, we introduced both solutions and exposed the benefits from the hardware solution. The benefits include utilizing low silicon to achieve higher performance and program memory reduction on chip. In summary, our solution only uses 0.91% extra silicon area yet achieves 32.75% performance improvement. So, the overall performance-to-cost ratio could be evaluated as 32%.

Genomic analysis of Salmonella Heidelberg isolated from the Brazilian poultry farms.

The rapid expansion of broiler chicken production in Brazil has presented significant sanitation challenges within the poultry industry. Among these challenges, Salmonella enterica subsp. enterica serotype Heidelberg stands as a contributor to global salmonellosis outbreaks. This study analyzed 13 draft genomes of Salmonella Heidelberg isolated from the pre-slaughter broiler chickens farms in Brazil. By conducting in silico analysis of these genomes, the study investigated genome similarity based on single nucleotide polymorphisms (SNPs) and identified genes encoding resistance to antimicrobials, sanitizers, and virulence factors. Furthermore, mobile genetic elements (MGE) were identified to assess their potential role in propagating genes through horizontal gene transfer. A risk classification was also applied based on the resistomes. The genomes revealed a high prevalence of genes conferring resistance to aminoglycosides, fosfomycin, sulfonamides, tetracycline, and genes linked to quaternary ammonium resistance. The study also uncovered six Salmonella pathogenicity islands (SPI) and over 100 genes encoding virulence factors. The association of MGE with antibiotic-resistant genes sul2 and blaCMY-2 raised concerns about the potential transfer to other bacteria, posing a substantial risk for spreading resistance mechanisms according to established risk protocols. Additionally, SNP analysis indicated close phylogenetic relationships among some isolates, suggesting a common origin. This study enhances our understanding of Salmonella Heidelberg strains by identifying key risk factors for transmission and revealing the association between resistance genes and MGEs. This insight provides a foundation for developing and implementing effective control, monitoring, and treatment strategies in the poultry industry.

Digital Twin-assisted anomaly detection for industrial scenarios

Industry 5.0 is the current industrial paradigm that inherits the technological diversity of its predecessor, Industry 4.0, but includes three priority goals: (i) resilience, (ii) sustainability and (iii) human-centeredness. Through these three goals, Industry 5.0 pursues a more far-reaching digital transformation in industrial ecosystems with high protection guarantees. However, the deployment of innovative information technologies for this new digital transformation also requires considering their implicit vulnerabilities and threats in order to avoid any negative impacts on the three Industry 5.0 goals, and to prioritize cybersecurity aspects so as to ensure acceptable protection levels. This paper, therefore, proposes a detection framework composed of a Digital Twin (DT) and machine learning algorithms for online protection, supporting the resilience that Industry 5.0 seeks. To validate the approach, this work includes several practical studies on a real industrial control testbed to demonstrate the feasibility and accuracy of the framework, taking into account a set of malicious perturbations in several critical sections of the system. The results highlight the effectiveness of the DT in complementing the anomaly detection processes, especially for advanced and stealthy threats.

Comprehensive review of recent research trends in dielectric elastomer transducers: applications, materials, and fabrication

Abstract While rigid transducers have led to innovations in industrial automation and control systems,
their rigidity limits them to the controlled environment of the factory. Recently, soft transducers
have become an attractive area of research because their compliant nature presents potential to
be applied to soft-bodied robots operating in uncontrolled environments. Dielectric elastomer
transducers (DETs) demonstrate large electrically-driven deformations, high energy density, fast
response, long lifespans, and self-healing capabilities which makes them a favorable replacement
for rigid transducers. This review first introduces the working principles of DETs, modeling work,
and different transducer designs. Then the applications of DETs to robotics, generators, and
electronics are reviewed. Lastly, the challenges currently faced by DET technology are discussed
and potential approaches are explored.

Conductive metal-organic framework for ppb-concentration and highly selective SAW hydrogen sulfide sensing at room temperature

Hydrogen sulfide (H2S) sensing is utilized for monitoring the concentration in environment protection and industrial process control, of which high selectivity and ppb detection limit are required. Here, a ppb-concentration and highly selective surface acoustic wave (SAW) H2S sensing has been developed with the Cu3(HHTP)2 MOFs conductive metal-organic frameworks (MOFs), which have been synthesized by connecting Cu2+ with the organic ligands via hydrothermal process. Typically, as-prepared Cu3(HHTP)2 MOFs take on shapes including nanoparticles and nanorods. Beneficially, the SAW H2S sensing presents excellent selectivity to H2S and enables to detection of H2S as low as 6 ppb at room temperature (∼ 26 °C). Remarkably, the SAW sensor prototypes exhibit high sensitivity (0.02 mV/ppb) to 50–2000 ppb H2S, fast response (T90: 281 s) and 46 day-long stability at room temperature. Theoretically, such excellent H2S SAW sensing performance might be attributed to the interaction between Cu ions and H2S molecules and the strong acoustoelectric effect of SAW. Practically, our ppb-concentration and highly selective H2S SAW sensing have the potential in the real-time detection of H2S.