Intelligent Control System Research Articles

A hybrid approach for PV based grid tied intelligent controlled water pump system

SummaryThis article proposes a hybrid POA‐RBFNN approach for PV (photovoltaic) based grid tied intelligent controlled water pump system. The proposed method is the hybrid wrapper of Pelican Optimization Algorithm (POA) and Radial Basis Function Neural Network (RBFNN) and later it is termed as POA‐RBFNN method. On the basis of bidirectional power grids, this research presents bidirectional power flow control for PV systems. The water pump is driven through a three‐phase synchronous reluctance motor (RSM). A novel Cuk integrated Landsman converter can operate at low duty cycles to provide larger DC voltages with negligible switching losses is used. In addition, to enhance the pumping system reliability, solar panels and electric pumps can be used to their maximum potential. Despite fluctuating weather conditions, the system supplies nominal displacement all over the day and surplus energy to the grid. Furthermore, if one of the sources fails, the pump performance is unaffected. Switching losses are minimized since the Voltage Source Inverter (VSI) operates at a fundamental frequency. In all operational modes, this control delivers unique grid‐side power quality and unity power factor functioning. Here, the performance of the proposed hybrid system is implemented in the MATLAB and is compared with several existing methods.

Research on the Construction of Intelligent Contract Mortgage System for Enterprise Financial Risk Control based on Blockchain Technology and Information Disclosure Theory

Research on the Construction of Intelligent Contract Mortgage System for Enterprise Financial Risk Control based on Blockchain Technology and Information Disclosure Theory

Guest Editorial of the Special section on Customers Centric Communication and Networked Control for Intelligent Cyber-Physical Transportation Systems

Guest Editorial of the Special section on Customers Centric Communication and Networked Control for Intelligent Cyber-Physical Transportation Systems

Research and Design of an Intelligent Street Lamp Control System Based on NB-IoT

Research and Design of an Intelligent Street Lamp Control System Based on NB-IoT

Development of intelligent protection and automation control systems using fuzzy logic elements

In this article, the causes of technological disturbances in electrical systems are considered, and several characteristic disadvantages of the protection and automation of elements of electrical systems are highlighted. The tendency to decrease the reliability of relay protection associated with the transition from analog to digital types of protection is substantiated. Based on the studied examples, the use of fuzzy logic in protections, the expediency of using fuzzy logic elements in protection devices, and the automation of electrical systems to identify types of short circuits are justified. This article analyzes the most common damages and presents the results of modeling an electrical system with transformer coupling, where all types of asymmetric short circuits were initiated. The dynamics of changes in the symmetrical components of short-circuit currents of the forward, reverse, and zero sequences are determined. Rules have been created for the identification of asymmetric types of short circuits. An algorithm of protection and automation operation using fuzzy logic elements has been developed. The proposed algorithm of protection and automation will reduce the time to determine the type of damage and trigger protections.

Intelligent residual film recovery machine monitoring and control system

Domestic residue recovery machines in China still follow the traditional towed mechanical model, lacking automation and intelligent features. Under the key R&D project of Xinjiang Autonomous Region, our team has developed integrated intelligent agricultural machinery for cotton straw crushing and residue recovery. This research covers the monitoring and control system, including PLC control and communication, working parameter monitoring and adjustment, algorithmic control of electro-hydraulic actuators, and human-machine interface display. Compared to traditional models, this machine replaces the conventional chain-driven mechanical structure and diesel tractor traction power with a fully hydraulic integrated suspension drive and power system. Sensors, PLC, and a touch screen are utilized for real-time monitoring and display of parameters. Hardware components like electromagnetic switch valves, electro-hydraulic proportional valves, hydraulic cylinders, and software elements such as PLC programs and intelligent control algorithms are employed for electro-hydraulic switching and proportional control of certain working parameters and mechanisms. All parameter information is consolidated in the PLC host and expansion modules and displayed and controlled on the human-machine interface. The system was installed on the first-generation machine for indoor testing, revealing that the monitoring and control system used on the first-generation machine achieves parameter detection and adjustment of the actuating mechanisms. It incorporates more advanced functions compared to traditional residue recovery machines. However, there is still ample room for improvement in the overall intelligence of the machine. The control system’s structure requires adjustment, and certain functionalities of the overall mechanical structure and working components need optimization or upgrading. Detailed enhancements to the entire system will be made in the next-generation machine.

Intelligent process control system for predicting operating conditions of slag grinding machines: a data mining approach for improved efficiency and energy savings

This study develops an intelligent data-driven approach for optimising slag grinding systems. Slag grinding exhibits complex nonlinear dynamics that challenge control. The proposed system monitors key operating parameters to assess machine health and automate control adjustments. Operating data are collected, and features linked to health status are identified using data mining techniques. Cluster analysis categorises historical data into healthy/unhealthy modes to build a condition library. Real-time data are then evaluated against this library. A predictive model forecasts future trend. The system was implemented in an industrial slag mill. Results demonstrated reduced vibration and energy use versus manual control. Validation confirmed improved accuracy in predicting mill responses. Significant energy savings were achieved annually through optimised control. The system enhances safety by automating adjustments while minimising costs and environmental impacts. Data-driven strategies overcome the limitations of traditional methods, representing an advance for intelligent management of industrial processes. Benefits were confirmed through rigorous factory implementation and performance monitoring.

Retracted: Automatic Intelligent Control System Based on Intelligent Control Algorithm

Retracted: Automatic Intelligent Control System Based on Intelligent Control Algorithm

Study on multi-objective adaptive cruise control of intelligent vehicle based on multi-mode switching

Aiming at the problems of slow response, poor comfort and high fuel consumption of the existing adaptive cruise control system under complex operating conditions, a multi-objective adaptive cruise control method of intelligent vehicles based on multi-mode switching is proposed. Firstly, the overall scheme of the adaptive cruise control system is designed by using the hierarchical control structure, and the multi-mode switching strategy is designed by using the fuzzy control theory to realize the division and switching of the working modes during vehicle cruise. Secondly, based on the variable spacing strategy and longitudinal kinematics model, the multi-objective of safety, following performance, fuel economy and comfort is analyzed and carried out, and a quadratic multi-objective optimization function based on multi constraints is obtained. Then, the model predictive control algorithm based on particle swarm optimization (PSO) is used to transform multi-objective function into a standard form with predictive control increment as the optimization variable, and the optimal control rate is solved. Finally, the simulation experiment is carried out by setting three complex working conditions: the preceding vehicle uniform speed change, the preceding vehicle rapid speed change and the adjacent vehicle cut in. The results show that the proposed method can meet the requirements of safety, comfort and fuel economy, and can improve the adaptability and friendliness of intelligent vehicle cruise control system.

Optimal data-driven control of manufacturing processes using reinforcement learning: an application to wire arc additive manufacturing

AbstractNowadays, artificial intelligence (AI) has become a crucial Key Enabling Technology with extensive application in diverse industrial sectors. Recently, considerable focus has been directed towards utilizing AI for the development of optimal control in industrial processes. In particular, reinforcement learning (RL) techniques have made significant advancements, enabling their application to data-driven problem-solving for the control of complex systems. Since industrial manufacturing processes can be treated as MIMO non-linear systems, RL can be used to develop complex data-driven intelligent decision-making or control systems. In this work, the workflow for developing a RL application for industrial manufacturing processes, including reward function setup, development of reduced order models and control policy construction, is addressed, and a new process-based reward function is proposed. To showcase the proposed approach, a case study is developed with reference to a wire arc additive manufacturing (WAAM) process. Based on experimental tests, a Reduced Order Model of the system is obtained and a Deep Deterministic Policy Gradient Controller is trained with aim to produce a simple geometry. Particular attention is given to the sim-to-real process by developing a WAAM simulator which allows to simulate the process in a realistic environment and to generate the code to be deployed on the motion platform controller.

A platoon ordering algorithm on reservation based intersection management systems

Traffic intersections act as pivotal junctions where various streams of traffic intersect and intersecting movements occur. When traffic volumes are high, intersections that are efficiently designed and equipped with intelligent traffic control systems can optimize the movement of vehicles. Accordingly, the time spent by vehicles at intersections increases, and these vehicles cause the vehicles behind them to slow down. This study aims to enhance the efficacy of reservation-based intersection control for platoons comprising vehicles arriving at intersections and turning in various directions. To assess the impact of different platooning algorithms on both the average speed of vehicles within the platoons and the total time spent at intersections, simulations were conducted using the SUMO environment. These simulations observed variations in the average speed values of vehicles and the duration spent by vehicles at intersections. The simulation results showed that the proposed reservation-based platoon ordering algorithm significantly contributed to increasing the average speed and reducing waiting time.

Simulation of Automatic Speed Control in Motor Vehicles (Cruise Control) Using Simulink PID Controller

An adaptive control system is an advanced method for controlling the speed of a moving motorized vehicle. Using this intelligent control system, the driver can easily control the speed of the car according to his wishes or the prevailing situation. The adaptive control system consists of a sensor attached to a moving vehicle which then registers the speed of the car and provides input to the processing unit. The controller is designed according to the force exerted by the car to drive a certain distance in a certain time. This time, the control uses a PID controller. This method is followed for various tunings of Kp, Ki, and Kd values for the P, PI, PID, and IPD structures for a cruise control system using MATLAB. The PID used in this experiment is intended to control the speed to make it more stable and optimal.

Development of automated metabolite control using mid-infrared probe for bioprocesses and vaccine manufacturing.

Intelligent digital control systems using continuous in-line metabolite data enabled end-to-end automation of fed-batch processes in stirred-tank reactors.

Construction of power load control and management terminal operation system based on machine learning technology

With the increasing Power Load (PL), the operation of the power system is facing increasingly severe challenges. PL control is an important means to ensure the stability of power system operation and power supply quality. However, traditional PL control methods have limitations and cannot meet the requirements of load control in the new era of power systems. This is because with the development of modern industry and commerce, the demand for electricity is gradually increasing. This article constructed a PL control and management terminal operating system based on machine learning technology to achieve intelligent management of PL, so as to improve the operational efficiency and power supply quality of the power system. This article identified the design concept of a PL control management terminal operating system based on machine learning technology by reviewing the current research status of PL control technology. Based on the operational characteristics and data characteristics of the power system, this article selected suitable machine learning algorithms to process and analyze load data, and established a prototype of a PL control and management terminal operating system based on machine learning technology, so as to realize intelligent processing and analysis of load data and conduct experimental verification. The experimental results show that through the comparative study of 6 sets of data in the tertiary level, the difference between the system and the real tertiary level is 0.079 kw, 0.005 kw and 0.189 kw respectively. Therefore, therefore, the average difference between the predicted value and the measured value of the PL system is about 0.091 kw. This indicated that the system had high accuracy and real-time performance in predicting PL, which could effectively improve the load control efficiency and power supply quality of the power system. The PL control management terminal operating system based on machine learning technology constructed in this article provided new ideas and methods for the development of PL control technology. In the future, system algorithms can be further optimized and a more intelligent PL control and management terminal operating system can be constructed to cope with the growing PL and increasingly complex power system operating environment.

Multifunctional Robotic Device with Intelligent Positioning System

At the present stage of agricultural production development, Smart farming is widely used as a systematic transition from managing a separate technological operation to managing processes that ensure the achievement of the required level of overall profitability of production through the use of new decision-making tools and automated management technologies. This approach involves expanding the scope of machines, equipment and software, including the widespread use of robotic tools in horticultural production and processing technologies, in order to increase production efficiency, eliminate the “human factor” in the production of products, replace human participation in production processes with a large proportion of heavy manual labor and minimize harmful effects chemical protection products for humans and the environment. Another reason for the intensification of the development and implementation of robotic tools with intelligent control systems in agriculture is the shortage of technologists and engineers in farms, due to the unattractiveness of labor in the agro-industrial complex. The article discusses the issues of increasing the efficiency of industrial gardening, through the development and implementation of robotic systems and electric drive transformer modules in various technological processes. The features of the designs and practical application of robots with intelligent motion control systems on garden plantations are analyzed. The application of the block-modular principle of the layout of robotic machines is justified to increase the efficiency and productivity of their work in industrial gardening in various technological operations: plant care (spraying, milling of aisles) and harvesting.

Intelligent Control System for Wood Drying: Scalable Architecture, Predictive Analytics, and Future Enhancements

This article explores the research and development undertaken as part of a Master’s degree in Computer Engineering, with a primary focus on enhancing control mechanisms for natural wood drying. While this method is known for its cost-effectiveness in terms of labor and energy, it suffers from slower and unstable drying cycles. The project’s objective is to implement an intelligent control system that significantly improves monitoring and recording of humidity levels in each wooden stack. Additionally, the system incorporates the capability to predict humidity based on data sourced from a weather forecasting API. The proposed solution entails a three-layer system: data collection, relay, and analysis. In the data collection layer, low-computing devices, utilizing a Raspberry Pi, measure humidity levels in individual wood stacks. These devices then transmit the data via Low Power Bluetooth to the subsequent layer. The data relay layer incorporates an Android application designed to aggregate, normalize, and transmit collected data. Furthermore, it provides users with visualization tools for comprehensive data understanding. The data storage and analysis layer, developed with Django, serves as the back-end, offering management functionalities for stacks, sensors, overall data, and analysis capabilities. This layer can generate humidity forecasts based on real-time weather information. The implementation of this intelligent control system enables accurate insights into humidity levels, triggering alerts for any anomalies during the drying process. This reduces the necessity for constant on-site supervision, optimizes work efficiency, lowers costs, and eliminates repetitive tasks.

An Intelligent Control Method for Industrial Equipment Based on Distributed Electrical and Electronic Architecture

Abstract The article establishes the objective function of distributed electronic and electrical architecture from three dimensions of economy, quantization and loadability, and constructs a mathematical model for multi-objective optimization based on the satisfaction of constraints. The model was applied and tested in the intelligent control system of automotive industrial equipment. In practical applications, the intelligent control system can automatically reduce the speed to 16km/h to maintain a safe distance and prevent collision when the target vehicle is monitored to enter the lane it is in and the distance between the two cars is less than 30 meters. Under 1500 pcu/h traffic flow conditions, the average delays of vehicles at the intersection through intelligent control were only 8.06 seconds and 8.02 seconds, compared to 20.58 seconds and 23.29 seconds for vehicles under conventional control. Vehicle intelligent control systems based on distributed electronic and electrical architectures have great potential for development and significant safety performance improvement in automotive industrial equipment manufacturing. The research in this paper provides a new perspective and adequate technical support for the intelligent control of future industrial equipment.

An intelligent greenhouse monitoring and control system employing Internet of Things

IoT-based intelligent greenhouse monitoring and control system has been designed to optimize agricultural processes. The proposed system employs a network of sensors to collect real-time data on environmental parameters such as temperature, humidity and soil moisture within the greenhouse. Leveraging Internet of Things (IoT) technology, the collected data is transmitted to a centralized platform accessible by farmers. Through this platform, users can remotely monitor and control the greenhouse conditions, ensuring optimal growth conditions for plants. Automated actuators enable precise adjustments, such as irrigation and ventilation, contributing to resource efficiency and sustainable farming practices. This innovative solution enhances crop yield, reduces resource wastage, and empowers farmers with actionable insights for informed decision-making, marking a significant advancement in modern agriculture.

Research on the Root Cause Analysis and Governance Measures of Frequent Power Outages in Electric Power Enterprises

Abstract Accompanied by the rapid development of the economy and the great strides of reform, the whole society puts forward higher and higher requirements for the safety and stability of the power grid, and the frequent power outage events will bring great losses to the power enterprises. In this paper, we employ the linear encoding function in the MAGNN method to linearly process the risk factors associated with frequent power outages in electric power enterprises. We then assign weights to these linearly processed risk factor vectors using a graph attention mechanism. Finally, we apply the correction matrix of outage risk factors to identify the risk of power outages in electric power enterprises and design an intelligent power outage control system. The study discovered that 10 kV system failure is the primary cause of frequent outages in Power Enterprise-A. The number of failures occurred 129 times due to 10kV feeder system failures in rural distribution networks. The study also reveals that the implementation of an intelligent outage control system resulted in a 73.60% year-over-year decrease in the reliability loss of fault outages in power enterprise A, thereby effectively addressing the issue of frequent outages and reducing the economic losses incurred by power enterprises. This paper provides an effective method for resolving frequent power outages in electric power enterprises and provides a guiding direction for improving power supply reliability in electric power enterprises.

ПРИМЕНЕНИЕ ИНТЕЛЛЕКТУАЛЬНЫХ СИСТЕМ УПРАВЛЕНИЯ ОСВЕЩЕНИЕМ ДЛЯ ПОВЫШЕНИЯ ЭНЕРГОЭФФЕКТИВНОСТИ

The article discusses the use of intelligent lighting control systems to improve energy efficiency. It describes the main components of such systems, modern technologies and methods of lighting control, as well as examples of their application in various industries. It analyzes methods for assessing energy efficiency and economic benefits from the implementation of intelligent systems. Particular attention is paid to technical and technological challenges, ethical and social aspects of using such systems. A forecast is made for the development of intelligent lighting control systems and possible innovations in this area are considered.