Intelligent Control System Research Articles

Application and Practice of Intelligent Control System in Microcontroller Experiment Courses

Application and Practice of Intelligent Control System in Microcontroller Experiment Courses

Analysis of the Structure and Working Principle of Intelligent Bionic Hand

Intelligent bionic prosthetic hand controlled by intelligent neural networks with perceptual feedback function is a hot topic of research for scientists around the world. This article analyzes the development status of electromyographic prosthetic hands both domestically and internationally over the years, and provides a detailed introduction to the structure of intelligent bionic hands. The intelligent bionic hand consists of three important components: 3D printed bionic hand bone structure, sensors, and intelligent neural network algorithm control system. Among them, the intelligent neural network algorithm control system is its core. The focus is on the working principle of an intelligent bionic hand, where sensors collect electromyography and neural electrical signals generated by human muscle movements. Through the intelligent neural network algorithm control system, the data information obtained is continuously optimized and adjusted to achieve motion control of the prosthetic hand. This article analyzes some problems of intelligent bionic hands and provides development ideas and suggestions. It is pointed out that the reconstruction of the sensing function of the intelligent bionic hand should focus on multimodal feedback, using diverse stimuli and more sensitive sensors for human-machine interaction, so that the intelligent bionic hand can move flexibly and freely, benefiting most disabled patients.

Shallow and Deep Learning Models for Vessel Motions Forecasting during Adverse Weather Conditions

Accurately forecasting vessel motions is a critical step towards achieving fast and accurate intelligent vessel control systems. Intelligent vessel control relies on accurate predictions of vessel motion to make informed decisions regarding control, maneuvering, and positioning, particularly during times of exogenous loading caused by adverse weather conditions. Hence, by accurately forecasting vessel motion accurately, the control system can anticipate potential issues (i.e., excessive trim or roll) and prescribe corrective actions before they become problematic. In this study, the authors propose two approaches to address the problem of vessel motion forecasting. The first approach relies on classical shallow learning models, whereas the second approach involves the use of state-of-the-art deep learning models for improved accuracy at further forecast horizons. Unlike shallow models, deep models can learn the required features directly from the data and therefore do not require a priori knowledge or additional features engineering. By leveraging deep learning models, the authors show that vessel motions can be forecasted further into the future without a significant loss in accuracy, thereby improving the overall effectiveness of the intelligent vessel control system. To support their statements, the authors use real operational data and compare the performance of the shallow and deep learning models. The results show that deep learning outperforms shallow learning models in terms of accuracy without a significant increase in the computational demand. Additionally, the authors demonstrate that their models remain accurate even under adverse weather conditions, indicating that they have practical applicability for vessel motions forecasting and can potentially improve the overall effectiveness of intelligent vessel control systems.

DEVELOPMENT AND RESEARCH OF THE FUNCTIONAL POSSIBILITIES OF THE AUTOMATED FUZZY INDOOR AIR QUALITY MANAGEMENT SYSTEM OF PRODUCTION PREMISES

Purpose. Development of a fuzzy indoor air quality control system and research of the efficiency of an automated complex for increasing the concentration of air ions, namely a small-sized ultrasonic generator of air ions and a pump unit in the conditions of a complex ventilated air environment of work premises. Design/methodology/approach. The comprehensive research carried out is important in the context of the development of measures and means and the research of their effectiveness in maintaining the aeroionic regime of industrial premises at the regulatory level. Conclusions. The structure of an intelligent indoor air quality control system is proposed, which implements a combined change in the rotation speed of the fan and pump and the power of the ultrasonic aeroion generator. For the first time, a formalized criterion for the optimality of the indoor air quality management system is proposed, which depends on individual preferences of people regarding the comfort of the production environment and standardized parameters of temperature, relative humidity, and concentration of air ions in the working space. The task of the intelligent control system is to minimize the proposed criterion. Limitations/consequences of research. The proposed recommendations are universal and can be used in various areas of business to create safe and harmless working conditions in production. Practical consequences. The use of the developed small-sized ultrasonic aeroion generator not only significantly increases the concentration of aeroions in the working area, but also due to the baloelectric effect minimizes the negative impact inherent in most existing ionizers, namely the generation of ozone and nitrogen oxide, which significantly improves indoor air quality and thereby increases the level of industrial safety and labor protection. Originality/meaning. The existing control systems assume that standard ionizers (corona discharges) are inserted in the room, so there was a need to develop and research a modern intelligent system for controlling the quality of the indoor air in the working area of industrial premises using an aeroion generator and automated displacement ventilation.

Intelligent access control system using decentralized identity with security level

Intelligent access control system using decentralized identity with security level

Development and Evaluation of an Intelligent Control System for Sustainable and Efficient Energy Management

This paper presents a comprehensive study on the integration of Intelligent Control Systems in the global industrial sector, focusing on enhancing energy management through the synergy of Supervisory Control and Data Acquisition (SCADA), Machine Learning (ML), and Digital Twin technologies. We elaborate on a novel ICS architecture designed to optimize energy consumption, reduce operational costs, and minimize environmental impacts. Our system leverages SCADA for real-time monitoring and control, ML algorithms for predictive analytics and optimization, and Digital Twin technology for advanced simulation and operational efficiency. The implementation of the system in a mid-scale industrial facility demonstrated significant improvements: a 15% reduction in energy consumption, an 18% decrease in peak energy demand, a 30% reduction in CO2 emissions, and a 15% reduction in operational downtime, with predictive accuracy standing at 90%. These results underline the potential of integrating advanced digital technologies in industrial energy management, offering a scalable model for sustainable and efficient industrial practices. Future work will explore broader applications and the incorporation of emerging technologies to further enhance the system's capabilities and applicability in diverse industrial settings.

IoT Enabled Boat for Cleaning Water Surface

This project introduces an IoT-enabled boat designed for the purpose of cleaning water surfaces. The system utilizes two gear motors to drive a conveyor belt for efficient collection of floating debris. An Arduino Uno, in conjunction with two IR sensors, provides precise control over the conveyor system, ensuring effective garbage collection. Additionally, the boat's overall movement is managed through a controller employing two gear motors and an Arduino IoT Cloud platform. The conveyor system, powered by the gear motors, facilitates the seamless collection of debris present on the water surface. The Arduino Uno, acting as the central processing unit, interprets signals from two IR sensors strategically placed to optimize the efficiency of the garbage collection process. This intelligent control system allows for the autonomous navigation of the boat and precise maneuvering around obstacles. Key Words: IoT (Internet of Things), Water Surface Cleaning, Gear Motors, Conveyor System, Arduino Uno, IR Sensors, Boat Controller, Arduino IoT Cloud, Garbage Collection, Autonomous Navigation, Remote Monitoring, Environmental Cleanup, Sustainability, Pollution Control, Scalable Solution, Real-time Feedback, Smart Technologies, Automation, Water Pollution, Adaptive Systems.

Analysis of the effect of grouting parameters on pile-soil interaction of grouted piles

Reasonable control of grouting parameters was the key to improving the pile-soil interaction. However, quantitative analysis on the effect of grouting parameters on the pile-soil interaction was still lacking. This article developed an intelligent post-grouting system for precise control and quantitative analysis of grouting parameters. The conventional preparation method for shear tests were improved in this article. By changing the normal stress of the pile-soil interface during grouting, the test sample could simulate the grout distribution of the pile-soil interface at different depths. Through the improved experimental methods, a more accurate analysis of the mechanism for the pile-soil interaction was achieved. And the effectiveness of the intelligent post-grouting system was further verified by the case application. The results indicated that increasing the grout volume could improve the interface shear strength when the normal stress and grouting pressure were constant. This improvement effect caused by increasing grout volume decreased when the normal stress was high. Increasing the grouting pressure increased the interface shear strength when the normal stress and the grout volume remained constant. This improvement effect caused by increasing grouting pressure decreased when the grout volume was small. By appropriately controlling the two grouting parameters, the cohesion and the shear strength of the pile-soil interface were increased by 5.8–18.8 times and 1.16–2.91 times, respectively. The results indicated that the intelligent post-grouting system could accurately control the grouting parameters and effectively improve the pile-soil interaction of bored piles. The intelligent post-grouting system effectively reduced geotechnical engineering risks and improved the bearing capacity of pile.

Biomechanical Analysis and Mobility Improvement of Auxiliary Mobile Equipment

As an advanced auxiliary mobile device, exoskeleton booster has attracted wide attention in the field of rehabilitation and life assistance. Through biomechanical analysis, this paper discusses the effect of exoskeleton booster on user mobility. Using biomechanical tools such as kinematics, dynamics and muscle mechanics, this paper deeply studies the influence of exoskeleton booster in different motion States, and verifies its biomechanical effect through simulation and experiment. Firstly, this paper analyzes the influence of exoskeleton booster on gait from the perspective of kinematics. The research shows that the exoskeleton booster can improve the walking efficiency of users by optimizing the step size and step frequency. The precise adjustment of joint motion by intelligent control system enables exoskeleton to better adapt to the gait characteristics of different users. Secondly, through dynamic analysis, this paper deeply studies the influence of exoskeleton on joint torque. The exoskeleton is designed to apply appropriate torque at key joints to help users complete the movement. The synergistic effect of this moment not only reduces the muscle burden of users, but also improves the stability of joints and reduces the risks in sports. In terms of improving mobility, exoskeleton technology can significantly improve the gait of patients in rehabilitation training, which is helpful to improve the balance ability of patients. The improvement effect of exoskeleton technology directly affects the quality of daily life of patients. This biomechanical effect provides strong support for the extensive application of exoskeleton technology in rehabilitation, sports assistance and daily life.

Event-driven intelligent cooperative control of unmanned autonomous systems in complex environment

This paper addresses the event-driven cooperative tracking control problem of unmanned autonomous systems with unknown dead zones, unknown disturbances, and output constraints, which are induced by complex environment. The nonlinear functions of system model are estimated by using the neural networks. Furthermore, a state transformation technique is presented to solve the problem of the constrained control. Unlike most existing methods that only consider unilateral factor, the intelligent cooperative control method designed in this paper can deal with multiple factors in complex environment at the same time, and make the unmanned autonomous systems save more energy and improve endurance through event-driven mechanism. Detailed analysis is given based on Lyapunov stability theory which guarantees that the consensus tracking errors are uniformly ultimately bounded. Finally, results of a numerical example and a practical example of marine surface vehicles are shown to verify the effectiveness of the proposed intelligent control protocol.

Hybrid Convolutional Neural Network Approaches for Recognizing Collaborative Actions in Human–Robot Assembly Tasks

In the context of sustainable manufacturing, efficient collaboration between humans and machines is crucial for improving assembly quality and efficiency. However, traditional methods for action recognition and human–robot collaborative assembly often face challenges such as low efficiency, low accuracy, and poor robustness. To solve such problems, this paper proposes an assembly action-recognition method based on a hybrid convolutional neural network. Firstly, an assembly action-recognition model is proposed using skeletal sequences and a hybrid convolutional neural network model combining Spatial Temporal Graph Convolutional Networks (ST-GCNs) and One-Dimensional Convolutional Neural Networks (1DCNNs) to sense and recognize human behavior actions during the assembly process. This model combines the joint spatial relationship and temporal information extraction ability of the ST-GCN model with the temporal feature extraction ability of the 1DCNN model. By incorporating Batch Normalization (BN) layers and Dropout layers, the generalization performance of the model is enhanced. Secondly, the model is validated on a self-constructed dataset of assembly actions, and the results show that the recognition accuracy of the model can reach 91.7%, demonstrating its superiority. Finally, a digital workshop application system based on digital twins is developed. To test the effectiveness of the proposed method, three sets of control experiments were designed to evaluate both objective and subjective aspects and verify the feasibility of the method presented in this paper. Compared with traditional assembly systems, the proposed method optimizes the recognition of human–robot collaborative assembly actions and applies them to intelligent control systems using digital-twin technology. This intelligent assembly method improves assembly efficiency and saves assembly time. It enables efficient and sustainable collaboration between humans and robots in assembly, leading to a positive and sustainable impact on the manufacturing industry.

An Integrated Two-Factor Authentication Scheme for Smart Communications and Control Systems

Fast and reliable authentication is a crucial requirement of communications networks and has various research challenges in an Internet of Things (IoT) environment. In IoT-based applications, as fast and user-friendly access and high security are required simultaneously, biometric identification of the user, such as the face, iris, or fingerprint, is broadly employed as an authentication approach. Moreover, a so-called multi-factor authentication that combines user identification with other identification information, including token information and device identity, is usedto enhance the authentication security level. This paper proposes a novel twofactor authentication scheme for intelligent communication and control systems by utilizing the watermarking technique to incorporate the mobile device authentication component into the user’s facial recognition image. Our proposed scheme offers user-friendliness while improving user security and privacy and reducing authentication information exchange procedures to provide a secure and lightweight schema in real applications. The proposed scheme’s security advantages are validated using the widely accepted Burrows–Abadi–Needham (BAN) logic and experimentally assessed using the Automated Validation of Internet Security Protocols and Applications (AVISPA) simulator tool. Finally, our experimental results show that the proposed authentication scheme is an innovative solution for a smarthome control system, such as a smart lock door operation.

"Harnessing the Power of Artificial Intelligence in Flexible Manufacturing Systems: Enhancing Efficiency, Adaptability, and Competitiveness"

A promising paradigm in contemporary manufacturing is the fusion of Artificial Intelligence (AI) technology with Flexible Manufacturing Systems (FMS). FMS, characterized by their ability to adapt to dynamic production demands, have found a perfect ally in AI, which offers advanced capabilities in data analysis, decision-making, and process optimization. This abstract provides an overview of the synergistic relationship between AI and FMS and highlights the potential benefits and challenges associated with their integration. Firstly, this abstract explores the role of AI in FMS, focusing on three key areas: planning and scheduling, intelligent control, and predictive maintenance. FMS is equipped with AI technologies like machine learning and deep learning to quickly analyze massive amounts of data, spot trends, and make precise predictions. These capabilities enhance production planning by optimizing resource allocation, reducing setup time, and minimizing production downtime. Additionally, intelligent control systems powered by AI enable real-time adjustments in response to changing conditions, leading to improved system flexibility, agility, and responsiveness. Due to a number of strong arguments, the combination of Flexible Manufacturing Systems (FMS) with Artificial Intelligence (AI) is of great research significance. The research significance of combining AI with Flexible Manufacturing Systems lies in the potential to significantly enhance operational efficiency, adaptability, and decision-making capabilities in manufacturing. This integration enables manufacturers to optimize resource utilization, mitigate downtime, and proactively manage maintenance, ultimately leading to improved productivity, cost savings, and competitiveness. By addressing the challenges and exploring the opportunities offered by AI in FMS, researchers can contribute to the advancement and transformation of the manufacturing industry. Due to the abundance of possibilities offered on the global market, conflicting situations can develop while choosing a certain motorcycle. There may be many alternatives to the initial choice or there may not always be a fixed amount of possibilities available. The possibility of not having an acceptable option for the criterion exists as well. “Multiple Criteria Decision Making” is a technique designed for the optimization of problems with an “infinite or finite number of choices” and the MCDM technique. “WSM method” is used to optimize the process in this paper. In artificial intelligence with flexible manufacturing system evaluated six criteria and got the values. in that values .FMS 1 has got the first rank, FMS 2 got the second rank,FMS 3 got the third rank and FMS 4 got the last rank.In conclusion, the integration of AI with Flexible Manufacturing Systems offers numerous opportunities for enhanced operational efficiency, productivity, and adaptability.

The simulation design of smart home system based on Internet of Things

With the upgrading of the traditional home industry, the deep integration of home design and Internet of Things technology is the development direction of modern smart home design, and it creates a comfortable, energy-saving and safe working and living environment for people. This paper designs an application scenario of the Internet of Things in smart home design, integrates smart home, Internet, Internet of Things and other technologies, designs the overall architecture, topology and IP address allocation scheme of the system, and realizes the intelligent door control and temperature control system of smart home based on the simulation platform. The whole network is connected to ISP Internet through home gateway and mobile data. After testing, the system runs stably as a whole, thus providing a solution for the application of the Internet of Things in smart home.

Artificial Neural Network-Powered, Driverless Vehicle Concept Development

Autonomous cars are now possible due to significant advances in robotics and intelligent control systems. Before these vehicles can safely operate in traffic and other hostile environments, there are many navigation, vision, and control issues. We want techniques that are both cost-effective and efficient, so that the field of research and academia may fully embrace self-driving cars. Within this scenario, we need something that can convert people to autonomous automobiles and include existing vehicles so that academics and explorers can access them. This study proposes a flexible mechanical layout that can be assembled in a short time and installed in most modern automobiles; it can also be used as a stepping stone in the development of autonomous vehicles. Using various actuators, conventional automobiles can be converted into autonomous vehicles. In the context of motor vehicle automation, motors are often used as actuators. In addition to motors, a pneumatic system was developed to automate the predetermined steps. An autonomous vehicle's mechanical arrangement is crucial, and it must be regularly updated and built to be robust in the face of dynamic conditions. We re-implemented two additional convolutional neural networks in an effort to conduct an objective test of their proposed network and compare our system's structure, technical complexity, and performance test during autonomous driving with theirs. This predicted network is around 250 times larger than the Alex Net network and four times larger than Pilot Net after training. Although the complexity and measurement of the publication's system are lower than other models that contribute lower latency and greater speed throughout inference, the operation was claimed by our system, which achieved autonomous driving with an equivalent efficacy as that achieved with two other models. The projected deep neural system reduces the need to infer ultra-fast computational hardware. This is important for cost efficiency, scale, and cost.

Intelligent control of district heating system based on RDPG

Given the continuous expansion of heating areas in recent years, the design of a precise and dependable district heating system (DHS) has become increasingly crucial. Traditional control decisions are made based on real-time environmental temperature feedback, often leading to uneven heating on the user side and affecting residents' comfort. This paper proposes an intelligent control strategy based on the deep reinforcement learning recurrent deterministic policy gradient (RDPG) algorithm for DHSs. To explore the control performance of the RDPG algorithm on DHS, we have meticulously modeled the pivotal components of the DHSs, namely plate heat exchangers, secondary heating pipe networks, and heat users. Moreover, taking into account the periodic factors in heating regulation, the traditional recurrent neural network (RNN) in the recurrent deterministic policy gradient (RDPG) algorithm has been replaced with the long short-term memory (LSTM) network. The proposed algorithm was trained using actual data from a heat exchange station in Tianjin and compared with reinforcement learning algorithms such as TD3, DPPO, DDPG, and A3C in terms of training rewards, effectiveness, and training stability. The results of the models are evaluated and visualized. Experimental results show that the proposed control method based on the RDPG algorithm, compared to other control schemes, can achieve the highest training reward and the most stable control performance, with an indoor temperature fluctuation range of only 0.1 °C.

Theoretical substantiation of the requirements for an intelligent video monitoring system for analyzing the physiological state of cows and predicting its changes

At the present stage of development of dairy farming, the main task facing the industry is the effective management of all processes that ensure the operation of a farm with a large livestock. This task will not be achievable without the use of digital intelligent production control and management systems, tracking the health of animals and their productivity. There are many systems that integrate into production and allow you to monitor the health of cows on a dairy farm. These systems are based on the identification, tracking and collection of information about the physiological state of animals through various sensors. However, all these systems are expensive, demanding to maintain, often fail due to aggressive operating conditions. The article considers the possibility of using an intelligent video monitoring system to analyze the physiological state of cows and predict its changes. To substantiate the requirements for an intelligent video monitoring system of the physiological state of cows, a mathematical model of the life of productive cows in the form of a graph of states is proposed. This method graphically displays the possible states of the system and their possible transitions from state to state. Having constructed a graph of states, relying on the central probability theorem for event flows, it is possible to determine the probability and intensity of state transitions using differential equations. For the functioning of the video monitoring system for analyzing the physiological state of cows and predicting its changes, the proposed mathematical model can be the basis of the system and be based on the collection and analysis of video information about animal behavior. When covering the entire territory of the animal's living space with video cameras, the intelligent video monitoring system will be able to provide early diagnosis of changes in the physiological state of the observed animals and issue a warning, which will allow for timely therapeutic and preventive measures.

Economical Design of Drip Irrigation Control System Management Based on the Chaos Beetle Search Algorithm

In the realm of existing intelligent drip irrigation control systems, traditional PID control encounters challenges in delivering satisfactory control outcomes, primarily owing to issues related to non-linearity, time-varying behavior, and hysteresis. In order to solve the problem of the unstable operation of the drip irrigation system in an intelligent irrigation system, this paper proposes chaotic beetle swarm optimization (CBSO) based on the BAS (beetle antennae search) longicorn search algorithm, with inertial weights, variable learning factors, and logistic chaos initialization improving global search capabilities. This was accomplished by formulating the optimization objective, which involved integrating the control input’s time integral term, the square term, and the absolute value of the error. Subsequently, PID parameter tuning was performed. In order to verify the actual effect of the CBSO algorithm on the PID drip irrigation control system, MATLAB was used to simulate and compare PID control optimized by the GA algorithm, PSO algorithm, and BSO (beetle search optimization) algorithm. The results show that PID control based on CBSO optimization has a short response time, small overshoot, and no oscillation in the steady state process. The performance of the controller is improved, which provides a basis for PID parameter setting for a drip irrigation control system.

Efficient design of energy microgrid management system: A promoted Remora optimization algorithm-based approach

Microgrids are a promising solution for decentralized energy generation and distribution, offering reliability, efficiency, and resilience. These small-scale power systems can operate independently or connect to the main grid, providing greater reliability and resilience. However, integrating renewable energy into microgrids presents challenges due to their unpredictable nature and fluctuating load of electricity. Energy management strategies play a crucial role in optimizing the operation of microgrids, aiming to balance electricity supply and demand, maximize renewable energy utilization, and minimize operational costs. Various approaches have been proposed for energy management in microgrids, including optimization algorithms, machine learning techniques, and intelligent control systems. This study proposes an optimized and efficient strategy for microgrids operating in both independent and grid-connected modes, focusing on microgrids that utilize a combination of solar and green energy sources. The proposed approach, based on the Promoted Remora Optimization (PRO) algorithm, aims to meet load power requirements at the lowest possible cost while ensuring constant DC bus voltage and safeguarding batteries against overcharging and depletion. The CRO method effectively optimized the charging process, maintaining a consistent level of charge and achieving a final SoC of 33.37 %–33.60 %. It also demonstrated high system efficiency, with an average of 87.99 %, and a range of 87.80 %–88.03 %. The optimizer efficiency ranged from 83.12 % to 86.52 %, with an average of 86.46 %. The CRO method also achieved reasonable operating costs, with a cost per power of $0.1687/kW to $0.1699/kW and a daily cost of $1,379,595 to $1,479,998. Overall, the CRO method showed promise in optimizing the charging process in terms of efficiency and cost-effectiveness. Comparative analysis with existing literature is conducted to evaluate the effectiveness of the proposed approach, demonstrating its superior results compared to other energy management strategies for microgrids. This study contributes to the field of microgrid energy management by providing a novel approach based on the PRO algorithm and demonstrating its effectiveness through comparative analysis.

Optimization and intelligent power management control for an autonomous hybrid wind turbine photovoltaic diesel generator with batteries

In this paper, a critical issue related to power management control in autonomous hybrid systems is presented. Specifically, challenges in optimizing the performance of energy sources and backup systems are proposed, especially under conditions of heavy loads or low renewable energy output. The problem lies in the need for an efficient control mechanism that can enhance power availability while protecting and extending the lifespan of the various power sources in the system. Furthermore, it is necessary to adapt the system's operations to variations in climatic conditions for sustained effectiveness. To address the identified problem. It is proposed the use of an intelligent power management control (IPMC) system employing fuzzy logic control (FLC). The IPMC is designed to optimize the performance of energy sources and backup systems. It aims to predict and adjust the system's operating processes based on variations in climatic conditions, providing a dynamic and adaptive control strategy. The integration of FLC is specifically emphasized for its effectiveness in balancing multiple power sources and ensuring a steady and secure operation of the system. The proposed IPMC with FLC offers several advantages over existing strategies. Firstly, it showcases enhanced power availability, particularly under challenging conditions such as heavy loads or low renewable energy output. Secondly, the system protects and extends the lifespan of the power sources, contributing to long-term sustainability. The dynamic adaptation to climatic variations adds a layer of resilience to the system, making it well-suited for diverse geographical and climatic conditions. The use of realistic data and simulations in MATLAB/Simulink, along with real-time findings from the RT-LAB simulator, indicates the reliability and practical applicability of the proposed IPMC strategy. Efficient load supply and preserved batteries further underscore the benefits of the fuzzy logic-based control strategy in achieving a well-balanced and secure system operation.