Automatic Control Research Articles

СИНТЕЗ ЭЛЕКТРОПРИВОДА КОМПРЕССОРНОЙ УСТАНОВКИ ЭЛЕКТРОВОЗА

The study objective is to improve the reliability and energy efficiency of the electric locomotive compressor drive. To achieve this, the required power of the electric motor was calculated the performance of the compressor unit, the volume of the main tanks and the selected electric motor were checked to start and adjust the capacity at low supply voltage. The task to which the paper is devoted is to design an electric drive for a compressor motor. A vector model of an asynchronous motor was made. The process of direct start-up and starting an asynchronous electric motor with the use of a scalar control system and IR compensation were modeled. Research methods: systematic approach; provisions of the theory of automatic control and electrical engineering; numerical PC simulation using Matlab software package. The novelty of the work is in the use of a frequency-controlled electric drive to start the asynchronous electric motor of the compressor unit instead of the normally installed starting capacitors. This design solution will significantly reduce the amplitude of the starting currents, which in turn will reduce the wear of the winding insulation. The study results are presented, based on the results of the constructed models, as graphs of the dependencies of speed and current during direct start of the electric motor and with the control system. Conclusions: the scalar control system, thanks to the introduction of an intensity setter, as well as IR compensation diagram, allow extending the transition time and controlling the value of the inrush current in acceptable values. Such an approach to make an electric drive compressor motor is able to significantly increase its reliability and service life, as well as to increase the overall energy efficiency of the system.

IOT Based Light Automation System

Electronic &Telecommunication (E&TC) Department of JSPM’s Bhivrabai Sawant Institute of Technology & Research (BSIOTR) Wagholi, Pune. Light System involves controlling departmental lighting using the Internet of Things (IoT), allowing for a user-friendly automation system within the department. Instead of switching light manually ON or OFF, this system uses technology for doing it automatically. It enables automatic control of electronic appliances such as fans and lights. In this paper, we have implemented the operation of lights through the Blynk Application to control departmental light automation, allowing users to turn lights ON and OFF as needed. The system primarily utilizes the Raspberry Pi module, a crucial component of our smart light system implementation. It comprises both hardware and software components. The hardware includes Raspberry Pi 3b+ and a Relay Module, while the software utilizes the Python Language to issue commands for turning the lights ON and OFF. Raspberry Pi 3b+ module keeps all electrical Department appliances connected through a single interface for complete smart home system automation. With this setup, there is no need to use the conventional switches, and hence human effort required for the system is comparatively very less. Keywords: Department Light System , Internet of Things (IoT) , User-friendly automation, Raspberry Pi module, Relay Module, Blynk Application. .

Highly stable Brillouin laser with controllable tuning based on fiber ring resonator

Abstract The design and fabrication processes of stimulated Brillouin laser (SBL) is complex, and it is affected by many factors such as temperature and resonance shift. In this study, we have fabricated a Brillouin laser using fiber ring resonator (FRR), with Q factor=7.1×10^8, resonance depth (h)=96%, and the free spectral range (FSR) automatic feedback control technology is proposed to realize the accurate matching of the resonant mode and the Stokes mode. The influence of temperature on the SBL frequency shift is suppressed. The fluctuation range of SBL’s frequency decreases by 5 times. The maximum steady state output of SBL at the best matching position is realized, and output power fluctuation range decreases by 15 times. The power stability of SBL reaches 4.85×10^(-6), which is improved by two orders of magnitude. This simple scheme provides convenience for the application of SBL, such as sensing and other applications.

Enhancing Autonomous Underwater Vehicle Decision Making through Intelligent Task Planning and Behavior Tree Optimization

The expansion of underwater scenarios and missions highlights the crucial need for autonomous underwater vehicles (AUVs) to make informed decisions. Therefore, developing an efficient decision-making framework is vital to enhance productivity in executing complex tasks within tight time constraints. This paper delves into task planning and reconstruction within the AUV control decision system to enable intelligent completion of intricate underwater tasks. Behavior trees (BTs) offer a structured approach to organizing the switching structure of a hybrid dynamical system (HDS), originally introduced in the computer game programming community. In this research, an intelligent search algorithm, MCTS-QPSO (Monte Carlo tree search and quantum particle swarm optimization), is proposed to bolster the AUV’s capacity in planning complex task decision control systems. This algorithm tackles the issue of the time-consuming manual design of control systems by effectively integrating BTs. By assessing a predefined set of subtasks and actions in tandem with the complex task scenario, a reward function is formulated for MCTS to pinpoint the optimal subtree set. The QPSO algorithm is then leveraged for subtree integration, treating it as an optimal path search problem from the root node to the leaf node. This process optimizes the search subtree, thereby enhancing the robustness and security of the control architecture. To expedite search speed and algorithm convergence, this paper recommends reducing the search space by pre-grouping conditions and states within the behavior tree. The efficacy and superiority of the proposed algorithm are validated through security and timeliness evaluations of the BT, along with comparisons with other algorithms for automatic AUV decision control behavior tree design. Ultimately, the effectiveness and superiority of the proposed algorithm are corroborated through simulations on a multi-AUV complex task platform, showcasing its practical applicability and efficiency in real-world underwater scenarios.

Minimizing Temperature Deviations in Rubber Mixing Process using Artificial Neural Networks

ABSTRACT Rubber mixing is a complex manufacturing process that poses challenges for process control due to the high number of control variables including mixing parameter settings, rheological behaviour, compound viscosity and batch-dependent material variations. Already small deviations from the control variables can influence the compound properties, leading to increased scrap rates. To address these challenges, this paper introduces an Artificial Intelligence (AI)-based approach to enhance process control in rubber mixing by predicting mixing temperatures from input variables. The proposed method utilizes Feedforward Neural Networks (FFN) to enable early identification of batch-specific temperature deviations, enabling systematic improvements with each new application. The FFN was trained on a diverse dataset encompassing various rubber recipes and batches. Post-training, the FFN demonstrated remarkable accuracy, achieving a Mean Absolute Percentage Error (MAPE) of 1.00% on the training dataset and 1.44% on the validation dataset, thereby showcasing its efficacy in predicting temperature fluctuations within the mixing process. Consequently, the FFN can determine the relevant input variables necessary to achieve specific mixing temperatures, providing a foundation for an automated control system in rubber mixing process. This paper outlines the system architecture of the FFN tailored for rubber mixing and provides a comprehensive overview of the experimental results.

Enhancing electromagnetic compatibility and energy efficiency of electric vehicle charging stations

Problem. The article proposes a single-link structure for an electric vehicle charging station utilizing an active four-square rectifier with power factor correction. A Matlab model of the proposed charging station is developed, taking into account parameters such as the power network, the switches of the active rectifier, its automatic control system, and an equivalent model of the battery compartment. Additionally, a mathematical model for calculating static and dynamic losses is created based on polynomial approximation of the energy dependencies of IGBT modules. The analysis investigates power quality parameters, components of energy losses, and efficiency of the charging station across various charge currents and PWM frequencies during a full battery charge interval. Goal. The aim of this study is to propose a single-link structure for an electric vehicle charging station using an active four-square rectifier with power factor correction. It includes an analysis of power quality parameters, components of energy losses, and efficiency of the charging station at different charge currents and PWM frequencies during a full battery charge interval. Methodology. To achieve the goal, several key steps are considered. These include theoretical substantiation of the scheme of the electric microgrid charging station for electric vehicles with one-stage energy conversion, analysis of the battery connection scheme in the Tesla Model S electric car, research and calculation of efficiency, modeling of the charging station, development of a Matlab model of a microgrid system for the charging station, SAC analysis of battery charge voltage and current of a three-phase AV with PWM, modeling of losses in IGBT modules by polynomial approximation of dependencies, distribution of losses in the charging station system, and analysis of energy efficiency parameters. Results. The study presents the energy efficiency parameters of an external DC EV charging station using an active rectifier. It reveals that maximum efficiency of the system is achieved at minimum charge current. However, decreasing the charge current prolongs the charge process and slightly affects power quality parameters. Originality. A mathematical model for calculating static and dynamic losses was developed based on polynomial approximation of the energy dependencies of IGBT modules. The analysis encompasses power quality parameters, components of energy losses, and efficiency of the charging station across various charge currents and PWM frequencies during a full battery charge interval. Practical value. This study contributes to the further development of electric vehicles by improving the energy indicators of electric vehicle batteries and converters of electric vehicle charging stations, enabling fast charging modes. Active development is observed in each of these directions.

SMART REFRIGERATOR USING IoT

With the rapid advancement of technology, smart devices like refrigerators have become integral parts of modern living. In this context, our proposed model aims to enhance refrigerator functionality by incorporating features for food quantity detection, temperature monitoring, and spoilage reminders. This system utilizes a mobile application interface to provide users with real-time information regarding the quantity of food items in the refrigerator and the current temperature. By leveraging this data, users can efficiently plan their meals, reduce food wastage, and optimize energy consumption through automatic control mechanisms. Additionally, the system incorporates a spoilage reminder feature, alerting users to food items in air-tight containers that may spoil without emitting detectable odours. This comprehensive approach to refrigerator management not only enhances convenience but also promotes sustainability by minimizing food waste and energy usage. Keywords— - Embedded system, Internet of things, DHT11, IR detector, Arduino Uno

One-Dimensional Crystal-Structure Te-Se Alloy for Flexible Shortwave Infrared Photodetector and Imaging.

Flexible shortwave infrared detectors play a crucial role in wearable devices, bioimaging, automatic control, etc. Commercial shortwave infrared detectors face challenges in achieving flexibility due to the high fabrication temperature and rigid material properties. Herein, we develop a high-performance flexible Te0.7Se0.3 photodetector, resulting from the unique 1D crystal structure and small elastic modulus of Te-Se alloying. The flexible photodetector exhibits a broad-spectrum response ranging from 365 to 1650 nm, a fast response time of 6 μs, a broad linear dynamic range of 76 dB, and a specific detectivity of 4.8 × 1010 Jones at room temperature. The responsivity of the flexible detector remains at 93% of its initial value after bending with a small curvature of 3 mm. Based on the optimized flexible detector, we demonstrate its application in shortwave infrared imaging. These results showcase the great potential of Te0.7Se0.3 photodetectors for flexible electronics.

Wirelessly Empowered Agriculture a Study on Arduino, NodeMCU and LoRaWAN Enabled Greenhouse Automation

Greenhouse automation system is a way to benefit rural farmers through automatic monitoring and control of the greenhouse environment. It changes care directly to people. To ensure the safety of crops, temperature, light intensity, soil moisture and humidity must be monitored regularly and continuously to keep them under control. It prevents climate change and extends the growing season. Although greenhouses are beneficial for farmers as they increase crop yield and production, they are inconvenient for farmers as they have to visit and check the greenhouses regularly and if the weather conditions are not perfect, it damages crops and productivity. But the emergence of sensors and the Internet of Things has changed the situation in the greenhouse. Sensors are bringing automation to greenhouse management and monitoring through the use of the Internet of Things and embedded technologies.

Intelligent IoT-based Control System of the UAV for Meteorological Measurements

This study focuses on the creation and examination of an intelligent automated control system for UAVs utilized in meteorological measurements based on the Internet of Things (IoT) and mobile technologies. The proposed system enables the achievement of commendable flight control standards for UAVs during meteorological data gathering, thereby markedly enhancing the overall effectiveness of meteorological stations. Notably, this system is constructed on the foundation of three integrated principles: (a) a hierarchical two-level approach for control and data collection based on IoT and mobile technologies, (b) a straightforward and dependable fuzzy logic control characterized by high performance, and (c) the effective optimization of fuzzy control components through the application of bio-inspired multi-agent computing techniques. To assess the performance of the suggested intelligent system, this study involves the creation and bioinspired optimization of the climb speed fuzzy controller. Subsequent simulation experiments are conducted to evaluate the automatic control of UAV’s flight processes under different modes. The analysis of the simulation results indicates that the developed system, utilizing fuzzy control, exhibits superior efficiency and higher quality metrics when compared to existing counterparts, especially in diverse flight scenarios such as uniform climbing, gradual approach to designated altitude levels, and smooth landing during meteorological measurements.

CAAC: An effective reinforcement learning algorithm for sparse reward in automatic control systems

CAAC: An effective reinforcement learning algorithm for sparse reward in automatic control systems

Controlling neural activity: LPV modelling of optogenetically actuated Wilson–Cowan model * *This work was supported by ANPCyT, Ministry of Science & Technology, Argentina Grant PICT2017-2417. Demián García-Violini is supported by the Agencia I+D+i, Ministry of Science & Technology, Argentina, under PICT-2021-I-INVI-00190.

Objective. This paper aims to bridge the gap between neurophysiology and automatic control methodologies by redefining the Wilson–Cowan (WC) model as a control-oriented linear parameter-varying (LPV) system. A novel approach is presented that allows for the application of a control strategy to modulate and track neural activity. Approach. The WC model is redefined as a control-oriented LPV system in this study. The LPV modelling framework is leveraged to design an LPV controller, which is used to regulate and manipulate neural dynamics. Main results. Promising outcomes, in understanding and controlling neural processes through the synergistic combination of control-oriented modelling and estimation, are obtained in this study. An LPV controller demonstrates to be effective in regulating neural activity. Significance. The presented methodology effectively induces neural patterns, taking into account optogenetic actuation. The combination of control strategies with neurophysiology provides valuable insights into neural dynamics. The proposed approach opens up new possibilities for using control techniques to study and influence brain functions, which can have key implications in neuroscience and medicine. By means of a model-based controller which accounts for non-linearities, noise and uncertainty, neural signals can be induced on brain structures.

Frequency Control of Interconnected Power System via SBO assisted Simultaneous Tuning Strategy in Presence of DFIG

To achieve the objectives of the automatic generation control (AGC) of wind integrated interconnected power system (WIIPS), better tuning and control strategies are required. This paper suggests simultaneous tuning strategy for the AGC and wind system controllers to improve the stability of the WIIPS. The bulk amount of wind is integrated into the grid using the doubly fed induction generator (DFIG) and its simplified model is used to simulate the WIIPS. Initially, classical controllers are opted in AGC mechanism to reduce the frequency disturbances caused by the load and wind generation uncertainties. Further, classical controllers of WIIPS are replaced with cascade controllers to enhance the system frequency profile during the disturbances. The optimal set of solutions of the classical and cascade controllers are examined by the satin bowerbird optimizer (SBO) with the help of integral square error as fitness function of the optimization problem. Comparisons are provided to show the merits of the WIIPS over normal interconnected power system, cascade controller over classical controller and simultaneous tuning strategy over regular tuning mechanism.

HDR vision sensor with neuro-memristive skin detection for edge computing

Human skin classification is an essential task for several machine vision applications such as human-machine interfaces, people/object tracking, and classification. In this paper, we describe a hybrid CMOS/memristor vision sensor architecture embedding skin detection over a wide dynamic range. In-sensor RGB to rg-chromaticity color-space conversion is executed on-the-fly through a pixel-level automatic exposure time control. Each pixel of the array delivers two pre-filtered analog signals, the r and g values, suitable for being efficiently classified as skin or non-skin through an analog memristive neural network (NN), without the need for any further signal processing. Moreover, we study the NN performance and theorize how it should be added in the hardware. The skin classifier is organized in an array of column-level memristor-based NN to exploit the nano-scale device characteristics and non-volatile analog memory capabilities, making the proposed sensor architecture highly flexible, customizable for various use-case scenarios, and low-power. The output is a skin bitmap that is robust against variations of the illuminant color and intensity.

Joint non-fragile automatic generation control and multi-event driven mechanism co-design for wind integrated power system under denial of services attacks

Denial of services (DoS) attacks exist in wind integrated power system. DoS attacks can cause network-induced delay and packages loss in information transmission. Meanwhile, considering the parameter perturbation of controller and system model uncertainty in wind integrated power system, these may cause the system dynamic performances degradation or even instability. Based on the above considerations, the joint non-fragile automatic generation robust control of wind integrated power system under DoS attacks is studied in this paper. In order to ensure the expected system performance and more effectively utilize the limited network communication resources under DoS attacks, a novel dynamic multi-event driven mechanism based joint non-fragile H∞ automatic generation control method is proposed. By constructing a suitable Lyapunov-Krasovskii functional and utilizing the Shur complement lemma to handle nonlinear matrix inequality, the sufficient conditions are derived to guarantee the asymptotic stability of wind integrated power system under DoS attacks. Furthermore, the performance of the proposed non-fragile regulator is demonstrated through a four-area wind integrated power system to show the feasibility and applicability. The analysis result indicates that the proposed scheme provides stronger robustness, higher wind energy utilization efficiency and more efficient communication mechanism.

Development of Microcontroller Based Water Quality Monitoring and Water Level Control Device

Access to clean and safe water is not only a fundamental human right but also a vital requirement for electronics industries, chemicals industries, pharmaceuticals, food and beverage production, fisheries and aquatic farming. This project addresses the pressing issue of water quality monitoring and efficient water usage within the context of these industries. The study's identification of problem areas for water quality can lead to more efficient interventions and solutions to safeguard the environment, and prevent waterborne diseases. This paper presents the design and implementation of microcontroller-based system for monitoring water quality and water level in the thank, the water in the thank is refilled when it goes beyond a set threshold with the aid of an automatic switch control. The system is a hybrid of 3 subsystems: a Water Level Checker, a Water Quality Monitoring System and an Automatic Switch control system. The Water level checker consists of Ultrasonic sensors. The Water Quality Monitoring system consists of pH sensors, TDS (Total Dissolved Solids) sensors and Turbidity sensors. The continuous (Analog) data from the water quality sensors used would be outputted via an LCD (Liquid Crystal Display). The Automatic Switch control system consists of 5v Relay modules and a 12v Pump that would automatically switch ON/OFF depending on the level of water in the tank. The values of EC (Electrical Conductivity) and TDS spike drastically to the introduction of salt from as low as 138 -906 for TDS and 0.34 – 2.04 for EC, signifying increase in water capacity of electrical conductivity. Temperature rise in the afternoon also increases TDS values from 142 for clean water to 920 for the salty water while it ranges from 138 for clean water to 906 for salt water at both morning and evening time. Pumping aspects of the system are triggered when water level in the thank falls below 10% and trips off when water level goes above 95% as programmed in the control unit. The developed system marks a significant achievement in water management and automation technology. integration of quality monitoring sensors has enabled real-time evaluation of water parameters that holds potential for applications in environmental monitoring, ensuring safe drinking water, and safeguarding aquatic ecosystems. The automated switch control mechanism responds dynamically to water levels, enabling the system to trigger actions such as pump activation or filling processes based on predefined thresholds.

Accurate identification and confidence evaluation of automatic generation control command execution effect based on deep learning fusion model

With the increasing complexity of the power grid, the precision of the thermal power units' execution of automatic generation control (AGC) commands is gradually increasing the impact on the online regulation of the power grid. The deviation between the actual output of thermal power units and the AGC command of the grid will not only affect the consumption of new energy output, but also endanger the safe operation of the grid. This paper introduces “deep learning” technology to solve the problem. Firstly, an AGC command execution effect identification and confidence evaluation algorithm (ACEEI-CEA) is proposed. The algorithm builds a neural network model to accurately predict the unit output and the confidence evaluation of the prediction results. Next, a high-dimensional input preprocessing strategy based on variational autoencoder (VAE) is proposed to reduce the dimensionality of the model input attributes, improving the convergence and accuracy of the model. Finally, an AGC optimal command fast inversion solution method (AOCFISM) is designed. This method transforms the unit output deviation problem into an objective optimisation problem. And improve the efficiency of solving the optimal AGC command value by constraining the unit command value. The calculation results show that the error of the prediction results of the model proposed in this paper is 5% lower than that of the traditional neural network. The difference between the output value of the optimal AGC command and the expected output value obtained is less than 0.5 MV, which can support AGC online decision-making.© 2017 Elsevier Inc. All rights reserved.

Review of Recent Patents on Smart Bearing

Background: Smart bearing is based on the traditional bearing, which is integrated by sensing devices and control devices, formating a unique bearing structure unit. It uses information processing, automatic control, and other technologies to achieve real-time online monitoring of bearing operating conditions, fault detection, and state control. Compared with ordinary bearings, smart bearings have the characteristics of self-awareness, self-decision, and self-regulation. By analyzing the composition of various types of smart bearings and their working principles, it is beneficial to prospect the future development direction of bearings. Objective: We analyze and organize the patents on smart bearings in recent years, summarize the problems encountered in the development of smart bearings, and propose the development direction of smart bearings in the future to provide a reference for future scientific research. Methods: This paper reviews the patents related to smart bearings and analyzes the composition and structure of smart bearings. The main smart bearings include rolling bearing, sliding bearing, magnetic bearing, and rubber bearing. The principle and structure of these bearings are analyzed. Their advantages and shortcomings are discussed. Results: Through the analysis of relevant patents and papers on smart bearings, the current development status and problems of smart bearings are summarized, the future design of smart bearings is proposed, and the research ideas and directions are proposed. Conclusion: The development of smart bearings is conducive to the timely detection of bearing conditions in the operation process. Except for detection, smart bearings have the functions of judgment, feedback, processing, etc. There are many factors that interfere with the accuracy of the smart bearing judgment and information transfer, so it is necessary to improve the structure and working principle of smart bearings through a variety of measures. In the future, more smart bearings will be invented.

Dual-Threshold Control Architecture for Improved Thermal and Power Management in Open-Cathode Direct Methanol Fuel Cell

A new dual-threshold cascade control scheme is proposed for water management in direct methanol fuel cells. The control architecture is constructed with the goal of adjusting the liquid-level of a methanol solution in an internal storage tank, where this control structure is based on a set point constrained by upper and lower thresholds. The logic of the algorithm is driven by a noninverting and an inverting Schmitt triggers comprising a switching function that sets the master controller on manual mode for periods of time during which the operating temperature of the electricity-generating fuel cell’s stack can be fixed at an optimal value. Automatic control is implemented when either threshold is reached, triggering a level recovery action where the temperature is manipulated to promote or inhibit cathodic evaporative water losses, depending on the existing perturbations. Manual mode is reinstated when a chosen mid liquid-level target is reached. The strategy seeks to avoid water flooding and dry-out states in the device, while ensuring constant power and current production during periods of manual operation. Simulation studies show that the new control method is immune to severe perturbations, which stands in contrast to a standard cascade control scheme that causes significant degradation and unstable electrical performance in the cell under analogous conditions. Unlike the standard cascade control where the operating temperature is seldom close to the optimal operating point, the improved dual-threshold logic is superior such that the desired temperature of operation can be realized during the entire manual mode, enhancing the performance of the variables that critically depend on temperature. The new method realizes a successful power transition from 30 to 40W, demonstrating its ability to meet changes in load demands, where this transfer is implemented by altering the operational temperature from 45°C to 60°C at a constant voltage. The new strategy can be incorporated into a higher-level optimization control layer, where different temperature operating points can be chosen based on user-defined performance criteria. The study focuses on a case of two different points of the polarization curve corresponding to a power of 40W. While the operating point at a current of 2.51A and temperature of 57.5°C has a higher voltage efficiency of 10%, the second point operating at 2.79A and 50°C shows a boost by 7.6% in fuel efficiency over the first point. Hence, a user can choose the optimal operating condition based on specific performance metrics while simultaneously meeting the load demands. Few recommendations are presented to advance this work in the future.

An industrial IoT-based deformation resistance prediction and thickness control method of cold-rolled strip in steel production systems

Different from traditional analytical and numerical simulation methods, data-driven methods can more effectively describe the effects of nonlinear and coupling factors. Although the fluctuations of deformation resistance and thickness for hot-rolled strips significantly influence the thickness accuracy in strip cold rolling, the traditional deformation resistance prediction model and thickness control mode don't involve that. Thus, based on the Industrial Internet of Things (IIOT) platform and data-driven technology, this work proposed a new method for deformation resistance prediction and a new mode of feed-forward control for strip thickness. IIOT platform can collect production data from the various levels to provide a complete data set. The deformation resistance prediction model is established by adopting the differential evolutionary algorithm to optimize the bi-directional long and short-term memory network. In addition, the feed-forward control (DFFC) strategy for strip thickness is proposed based on deformation resistance prediction. An application results of a 1420 mm production line of CR indicated that the established prediction model could provide a high accuracy prediction for deformation resistance, and compared to traditional thickness gauges-based automatic gauge control methods, the DDFC can capture the effect of the fluctuations of deformation resistance and thickness for the hot-rolled strip to improve thickness accuracy effectively.