Application Of Control System Research Articles

Design and Application of Intelligent Control System for Molten Iron Transportation Based on 5G Technology

Molten transport is an important link in the iron and steel enterprise production, involves many complex factors, artificial management is difficult. Therefore, puts forward a kind of molten iron transport wisdom control system based on 5G technology, which mainly contains the intelligent identification tracking system, equipment status collection information acquisition system, locomotive vehicle terminal system, etc. Combined with the analysis of the actual application situation, the system could integrate all the processes and elements of molten iron production and transportation, realize the integration of operation and management, and also promote the improvement of the turnover efficiency of molten iron tank, reduce the demand for personnel, and reduce the labor cost.

Remote operation of the DIII-D National Fusion Facility

Full remote scientific operation of the DIII-D National Fusion Facility is now possible through significant advances in the computer science hardware and software infrastructure made over the last decade. Capabilities around information visualization, data movement, and communication have all been enhanced. The level of capability deployed to remotely operate DIII-D required an infrastructure advancement over what had previously been achieved in the fusion community. The large quantity of real-time data that is automatically displayed on DIII-D’s control room screens can now be visualized by remote participants via web-based applications. New audio/video solutions using the VoIP and instant messaging application Discord have been implemented to mimic the dynamic and ad-hoc scientific conversations that are critical in successfully operating an experimental campaign. Discord’s ability for a user to rapidly move between audio channels, text with images, and share screens is a significant enhancement over traditional videoconferencing tools. In addition, multiple combinations of broadcast audio are made available via a web-based application to allow remote participants to simultaneously listen to general announcements/sounds while conducting their own specific conversations. Secure methodologies have been put into place to allow remote control of hardware including DIII-D’s plasma control system application. Secure methods also included the ability of the on-site team to closely coordinate their work with remote team members which has been enhanced through extensions to the wireless network and the use of tablet computers for audio/video/screen sharing. However, no amount of software can fully replace the need for ‘hands on hardware.’ This infrastructure was severely stress tested during the COVID-19 pandemic where occupancy of the DIII-D control room was restricted. Operational efficiency during the pandemic, measured in discharges per hour, remained high (3.8 ± 0.8) compared to values obtained pre-pandemic (3.7 ± 0.8).

Multi-Output Physically Analyzed Neural Network for the Prediction of Tire–Road Interaction Forces

<div>This article introduces an innovative method for predicting tire–road interaction forces by exclusively utilizing longitudinal and lateral acceleration measurements. Given that sensors directly measuring these forces are either expensive or challenging to implement in a vehicle, this approach fills a crucial gap by leveraging readily available sensor data. Through the application of a multi-output neural network architecture, the study focuses on simultaneously predicting the longitudinal, lateral, and vertical interaction forces exerted by the rear wheels, specifically those involved in traction. Experimental validation demonstrates the efficacy of the methodology in accurately forecasting tire–road interaction forces. Additionally, a thorough analysis of the input–output relationships elucidates the intricate dynamics characterizing tire–road interactions. This research underscores the potential of neural network models to enhance predictive capabilities in vehicle dynamics, offering insights that are valuable for various applications in automotive engineering and control systems.</div>

Pengaruh Penerapan Sistem Pengendalian Intern, Motivasi Kerja, dan Aksesibilitas Pelaporan Terhadap Kualitas Laporan Keuangan pada Dinas Pekerjaan Umum Kota Padang

The effect of the application of the internal control system, work motivation and reporting accessibility on the quality of the financial statements of the Padang City Public Works Department. This approach uses a quantitative approach, using primary data through a questionnaire. The respondents in this study were the Head of the Finance Division, the Secretary of Finance, Verification (internal Control) and the Staff of the Financial Management Section. The variables used in this study are the Internal Control System, Work Motivation, and Reporting Accessibility as independent variables, and the Quality of Financial Statements as the dependent variable. The method used in analyzing the data is using multiple regression analysis statistics. The results show that the implementation of the internal control system, work motivation and reporting accessibility on the quality of financial reports has no significant effect on the quality of the financial statements of the Padang City Public Works Department.

Forward solution of shield tunneling pose based on the Newton–Raphson method

To achieve precise control of the shield tunneling pose, obtaining a real-time shield pose state during the excavation process is necessary to achieve closed-loop feedback control of the shield pose. This study proposes a forward solution method to address the high cost and low efficiency of direct measurement methods, such as using lasers and gyroscopes, for measuring shield pose. This method relies on the real-time measurement of the strokes of hydraulic thrust cylinders during shield tunneling. Based on the inverse kinematics of the shield machine, the Newton–Raphson method was used to calculate the shield tunneling pose. Accordingly, a forward solution algorithm for shield pose was developed using MATLAB, and the effectiveness of the proposed method was verified through an example analysis. The results demonstrated the high computational efficiency of the method, proving its potential application in real-time control systems for shield tunneling poses.

Intersection of domains: Designing a remote-controlled dish antenna

The advent of technology has instigated rapid advances in the field of communications, a prime example being the remote-controlled dish antenna, which embodies a parabolic design intended to intercept and concentrate electromagnetic waves onto a common focal point. The modification towards remote control mandates a core understanding of diverse technological realms as it involves an amalgamation of com-ponents and knowledge. This discourse probes into the nuanced design and implementation of a remote-controlled dish antenna, conceived around the core touchstones of a microcontroller and an array of complementary components. The research work signifies a compounding of several technological do-mains, including microcontroller programming, power electronics, wireless communication, and motor control, necessitating extensive proficiency in these areas and consequently, offering an invaluable foundation to the broader control system applications. The overarching aim of this research is to enable users to effectively control a dish antenna's positioning remotely, leveraging a Bluetooth HC-05 module for optimal convenience and accessibility. This revolutionary project relies on a PIC18F2550 microcon-troller, a high-performance RISC CPU with integrated EEPROM and SRAM, which allows for flexible data storage and retrieval. Considering its compatibility with a variety of peripherals, the PIC18F2550 emerges as a logical choice, significantly enhancing the system's performance efficacy.

Photoresponsive Janus microfibers array film with tunable electrical anisotropy

A new kind of photoresponsive film is produced by integrating photoconductive material into the type-II anisotropic conductive film. This film consists of orderly arranged Janus microfibers, with each Janus microfiber is comprised of a conductive region and an insulating region. Copper phthalocyanine, as the photoconductive material, is introduced into the conductive regions of these Janus microfibers, and thus the electrical conduction of the conductive regions can be enhanced by applying appropriate external light irradiation, while the insulating regions are basically unaffected. Based on this property, the electrical conduction along with the length direction of the Janus microfibers in the film is adjustable by light, the perpendicular direction along the film surface remains constantly insulating, and thus the electrical anisotropy of the resulting Janus microfibers array film can be tuned under light irradiation. The products have potential applications in flexible electronic devices and control systems.

Development of real-time brain-computer interface control system for robot

Electroencephalogram (EEG)-based brain-computer interfaces (BCI) have been considered a prevailing non-invasive method for collecting human biomedical signals by attaching electrodes to the scalp. However, it is difficult to detect and use these signals to control an online BCI robot in a real environment owing to environmental noise. In this study, a novel state recognition model is proposed to determine and improve EEG signal states. First, a Long Short-Term Memory Convolutional Neural Network (LSTM-CNN) was designed to extract EEG features along the time sequence. During this process, errors caused by the randomness of the mind or external environmental factors may be generated. Thus, an actor-critic based decision-making model was proposed to correct these errors. The model consists of two networks that can be used to predict the final signal state based on both the current signal state probability and past signal state probabilities. Subsequently, a hybrid BCI real-time control system application is proposed to control a BCI robot. The Unicorn Hybrid Black EEG device was used to acquire brain signals. A data transmission system was constructed using OpenViBE to transfer data. An EEG classification system was built to classify the BCI commands. In this experiment, EEG data from five subjects were collected to train and test the performance and reliability of the proposed control system. The system records the time spent by the robot and the moving distance. Experimental results were provided to demonstrate the feasibility of the real-time control system. Compared to similar BCI studies, the proposed hybrid BCI real-time control system can accurately classify seven BCI commands in a more reliable and precise manner. Overall, the offline testing accuracy was 87.20%. When we apply the proposed system to control a BCI robot in a real environment, the average online control accuracy is 93.12%, and the mean information transmission rate is 67.07 bits/min, which is better than those of some state-of-the-art control systems. This shows that the proposed hybrid BCI real-time control system demonstrated higher reliability, which can be used in practical BCI control applications.

Advancing real-time drilling trajectory prediction with an efficient nonlinear DDE model and online parameter estimation

This study introduces a streamlined approach to predict planar drilling trajectory by employing an efficient nonlinear Delay Differential Equation (DDE) model with high computational speed and predictive accuracy. The efficient model is validated and compared with other models by utilizing not just data from existing literature but also two sets of experimental data with two distinct rigs. Following the validation, the model is integrated with an online estimation technique—calibrating one parameter in real-time using historical data—empowering it to forecast drill bit paths with remarkable fidelity. The validation of the model and its estimation strategy underscores their potential for application in real-time drilling control systems, setting a new benchmark for future advancements in the field.

Applicability evaluation of a temperature humidity index-controlled ventilation system in livestock using a building energy simulation model

In general, for livestock building, a simple system for controlling the HVAC system that is based only on the indoor air temperature is applied. This study evaluated the applicability of temperature humidity index (THI)-based ventilation systems installed in livestock buildings using a building energy simulation model. Based on the monitoring data of an actual pig house, the integrated predictive performance of the indoor temperature and humidity of the building energy simulation model was calibrated. Additionally, the indoor environment of the existing temperature-based control and THI-controlled ventilation systems was compared through simulations. The average THI and the section exceeding the THI threshold were both lower compared to the existing system. This was because the part load ratio of the fans of the THI-controlled ventilation system was higher than that of the existing system, indicating better ventilation. These results suggest that THI-based ventilation systems can overcome the limitations of existing temperature-based systems and effectively improve the thermal stress management of livestock buildings. This study portrays that the indoor environment can be improved by simply changing control variables, while presenting the field applicability and research value of THI-based control systems in the management of livestock environments.

Application of Bayesian Networks and Reinforcement Learning in Intelligent Control Systems in Uncertain Environments

<p>Reinforcement learning is a machine learning paradigm that focuses on how an agent can perform actions in an environment to achieve a certain goal. The agent learns through interaction with the environment, observing the state and making decisions to maximize its reward. Reinforcement learning has wide applications in intelligent control systems. However, one limitation of reinforcement learning is the uncertainty in handling the environment model. Usually, reinforcement learning is performed without a clear model, which requires estimating environmental uncertainty and state transitions. Bayesian Networks are effective in modeling uncertainty, which can aid in establishing a probabilistic model of environmental dynamics. This allows for the integration of uncertainty information into the environmental model, leading to a more accurate understanding of the dynamic characteristics of the environment. In this study, we propose a reinforcement learning algorithm based on Bayesian Networks. We utilize optimal generalized residual differentiation, parallel integration causal directional reasoning, and other modeling techniques to address reinforcement learning tasks. The main idea is to utilize the prior distribution to estimate the uncertainty of unknown parameters. Then, the obtained observation information is used to calculate the posterior distribution in order to acquire knowledge. Experiments demonstrate that this approach is feasible in intelligent control systems operating in uncertain environments.</p> <p> </p>

Development of an IoT-Based Egg Incubator with PID Control System and Web Application

The rapid development of technology significantly impacts various aspects of life, including the field of livestock farming. The advancement of technology is expected to enhance the rate and effectiveness of production, particularly in the hatching of chicken eggs or chick breeding. The existing technology relies on manual on/off systems and manual monitoring, hindering successful egg-hatching rates and percentages. Therefore, this research aims to explain the development of an automated egg incubator using a Proportional Integral Derivative (PID) control system with hypertuning parameters, as well as temperature and humidity monitoring, along with a protection system based on voltage sensors, all integrated with the Internet of Things (IoT). The PID control is employed to regulate the temperature of the egg incubator, ensuring stability according to the predetermined set point temperature. The IoT system in this study comprises an ESP32 node as a microcontroller connected to a sensor, using Firebase and User app for monitoring the egg incubator. The study employed PID control with parameter values Kp=10, Ki=3, and Kd=8. The research yielded time-efficient egg incubation and prevention of turning delays. The DHT21 sensor achieved a 90% success rate in detecting room temperature (38°C) and humidity (77%-84%) within the incubator, while PID control effectively maintained the target temperature. The ACS712 sensor accurately detected current in the heater, power supply, and motor. The Kodular application can display sensor readings. The future implication is developing a more adaptive PID method toward changes and nonlinear dynamics.

Investigation of Different Communication Topologies for Cooperative Adaptive Cruise Control Systems

After Adaptive Cruise Control (ACC) system applications, Cooperative Adaptive Cruise Control (CACC) systems are becoming an important part of automotive technology and industry in autonomous vehicles convoy applications. Together with this developing technology, CACC systems use vehicle to vehicle (V2V) communication to automatically transmit the movement information of vehicles. In this context, ACC systems use Radar or LIDAR measurements while CACC systems also consider the acceleration of the preceding vehicle. In this paper, the forms of information transmission between vehicles in autonomous vehicle convoys using CACC systems have been examined. From these forms of information transmission, the leader following, the predecessor follow-ing and the leader – predecessor following topologies have been considered. For each topology, an autonomous vehicle convoy consisting of eight vehicles was modeled in the MATLAB/Simulink environment. The feedforward and the feedback control system structure were given for CACC and ACC systems. For different communication topologies, the position-time, the speed-time, the acceleration-time and the headway time-time results were obtained. The maximum intervehicle distance error plots for each vehicle in different topology convoys were given to analyze the dynamic behavior of the convoys. The results have been analyzed in terms of the maximum intervehicle distance, the maximum speed, the minimum and the maximum acceleration, and the maximum headway time deviation from the desired headway time.

Thermal modeling of an office environment with variable volume air condition system using zonal method for control system applications

Thermal modeling of an office environment with variable volume air condition system using zonal method for control system applications

Verification and validation optimization method for signal quality bits in digital control system application software of nuclear power plant

Abstract The digitalization enhances nuclear power plant (NPP) instrumentation and control (I&C) system performance, while simultaneously introducing safety and reliability challenges. To enhance the stability of distributed control system (DCS), and minimize the dangerous failure rate and safe failure rate of safety system, signal quality bits (SQBs) are widely used in NPP DCS to identify the validity of important signals. However, in the past decade or so, numerous unscheduled turbine shutdowns and/or reactor trips have resulted from incorrect SQB design or settings. Therefore, it is necessary to conduct optimization research on the design methods and setting principles of SQBs, establish an optimization method for DCS application software SQB verification and validation (V&V), then minimize DCS faults attributed to SQB. With the aim, this work conducts an in-depth investigation and analysis of the definition, design, setting and transmission of SQB in NPP. A specific optimization method for software V&V to conduct SQB on NPP safety DCS application software is proposed. Twelve concerns in the SQB V&V work are detailed. The reactor trip system ΔT protection logic is utilized as a case to illustrate the detail implementation process of this optimization method. This work provides an engineering technical reference and operational guidance for SQB software V&V, conducive to ensuring the safe and economical operation of NPPs.

Innovations and Future Trends in Storage Pest Management

Current innovations and future trends in storage pest management, with a specific focus on the Indian context. The critical role of effective pest management in agriculture and food security is underscored, considering its impact on economic stability and public health. It begins by detailing the challenges faced in storage pest management, including the variety of pests like insects and rodents, and the damage they inflict on stored agricultural products. It critiques the limitations of traditional pest management methods, particularly chemical control, and highlights emerging issues such as climate change effects and pesticide resistance. The core examines the latest innovations in storage pest management. Advanced chemical approaches like novel pesticides and controlled release formulations are discussed, alongside the rise of nanotechnology applications in pest control, including nano-pesticides and smart delivery systems. The emergence of biological control innovations, particularly new biocontrol agents, and genetic control strategies like the sterile insect technique, marks a significant shift towards more sustainable pest management methods. Additionally, It explores developments in physical and mechanical control methods, emphasizing improved storage facilities and environmental control techniques. Integrated Pest Management (IPM) approaches are identified as a key future trend, promoting holistic and sustainable strategies, with case studies underscoring their success and practical application. It also addresses the vital role of policy and regulatory developments, considering the impact of global regulations and the importance of international cooperation and standards in shaping pest management practices in India. Ethical and environmental considerations form a crucial part of the discourse, focusing on the ecological impact of pest management and the balance between control measures and conservation efforts. It concludes with an examination of practical applications and case studies, providing insights into real-world challenges and the strategies employed to overcome them. Overall, It offers a thorough analysis of the state-of-the-art in storage pest management, highlighting the intersection of innovation, sustainability, and practicality. It aims to provide valuable guidance for researchers, policymakers, and practitioners in the field, contributing to the advancement of more effective, environmentally responsible, and sustainable pest management strategies in India.

Numerical investigation on cooling performance and control effect of a novel evaporation-driven cooling unit for Mars extravehicular space suit application

The special environment of Mars brings new challenges to the thermal control of spacecraft on Mars missions. The outstanding advantages of water evaporation cooling technology make it promising for use in the thermal control system of the Mars spacecraft. This paper proposes a novel concept of evaporation cooling hardware that integrates heat collection, heat transfer and heat rejection, namely evaporation-driven cooling unit. The evaporation-driven cooling unit has a simple structure, high evaporation efficiency and the potential to simplify the system topology. Furthermore, an expert hierarchical coordination control strategy applied to the Mars extravehicular space suit cooling loop with an evaporation-driven cooling unit as its heat sink is proposed. The dynamics model of this cooling loop is also constructed using the lumped parameter method. The numerical results have shown that the expert hierarchical coordination control strategy can achieve precise temperature control and reduce the water consumption of the system. The average water consumption rate during extravehicular activity is 0.6975 kg/h when the mean heat load is 572 W (including member metabolic heat and electronic waste heat). Compared to the open-loop case with the same input heat load, a typical extravehicular activity lasting 8 h will save about 11.5 g water if the expert hierarchical coordination control is applied. The proposed evaporation-driven cooling unit will provide more choices for heat rejection of extravehicular space suits. It is expected that the evaporation-driven cooling unit and expert hierarchical coordination control have potential applications in thermal control systems for other space vehicles in near-Earth orbit, on the surface of the Moon and Mars.

Graph-based meta-learning for context-aware sensor management in nonlinear safety-critical environments

This study introduces a novel framework for optimizing energy efficiency and computational load in safety-critical robotic systems operating in nonlinear domains. Leveraging Graph Attention Networks for state awareness and decision-making, the framework employs adaptive sensor and filter toggling strategies to dynamically manage system resources through real-time inferential processes. Our framework maintains continuous robot operation in the presence of sensor noise and environmental disturbances by activating additional sensors, thus preventing system shutdowns or stalls. Few-shot meta-learning techniques further augment the model's adaptability, allowing it to generalize and make real-time decisions across varying operational conditions. An extensive evaluation reveals a reduced average energy consumption, compared to ‘always-on’ configurations, by 13.71% and CPU utilization by 29.07%, without compromising system performance and safety. We also introduce Matching Networks and Siamese Networks with different loss functions to assess the system's capability to adapt to different levels of criticality. Our experiments demonstrate that the system prioritizes performance and safety in high-critical scenarios while maximizing energy efficiency in less critical situations. The framework's real-time decision-making capability is particularly crucial in human–robot environments and holds significant implications for future applications in nonlinear control systems and resilient robotic systems.

An improved seeds scheme in K‐means clustering algorithm for the UAVs control system application

AbstractClustering algorithm is the primary technology used in target clustering and group status analysis which are key features of the Unmanned Aerial Vehicles (UAVs) control system. Due to variable application environment, the stability of the algorithm in the UAVs control system needs to be considered. K‐means clustering is a widely used method in intelligent systems. However, K‐means algorithm is susceptible to the local optimum due to the influence of the initial centroid. For this problem, the predecessors have proposed various effective solutions. These algorithms perform better on real and large‐scale datasets, but they are unable to achieve optimum results with unbalanced datasets. Herein, a simpler and more effective algorithm for seed initialization is proposed, it has a better accuracy rate than the alternative algorithms.Moreover, after running tests multiple times with each algorithm independently, it has the highest stability and the lowest overall volatility. With unbalanced datasets, the proposed algorithm performs significantly better than several other algorithms and therefore can solve the problems that other algorithms have with unbalanced datasets.

Synthesis Methodology for Discrete MIMO PID Controller with Loop Shaping on LTV Plant Model via Iterated LMI Restrictions

This paper presents a methodology for synthesizing discrete MIMO PID controllers through iterative solutions of LMIs. It justifies the necessity of direct synthesis of discrete controllers in digital control systems. The proposed methodology allows for synthesizing an LTI controller for an LTV plant model, ensuring the robust stability of the synthesized system. Robustness is further ensured by the small-gain theorem and a novel approach to loop shaping, enabling the use of arbitrary shape functions. As a result, this methodology provides a broad statement of the problem with performance criteria necessary for practical application in control systems. Numerical examples are used to illustrate the methodology, which is implemented using the MATLAB toolbox, freely available for use.