Artificial Intelligence Control Research Articles

Performance evaluation of BLDC motor drive mounted in aerial vehicle (drone) using adaptive neuro-fuzzy

The development of autonomous drones equipped with cameras and various sensors has paved the way for their application in agriculture and perimeter security. These aerial drones require specific power, acceleration, high torque, and efficiency to meet the demands of agricultural tasks, utilizing built-in brushless DC (BLDC) motors. However, a common challenge drone’s face is maintaining the desired speed for extended periods. Enhancing the performance of BLDC motors through advanced controllers is crucial to address this issue. This research paper proposes optimizing the size and speed of brushless DC motors for aerial vehicles using an adaptive fuzzy inference system and supervised learning techniques. When these drones carry loads, the BLDC motors must dynamically adjust the drone's speed. During this phase, the motors must control their speed and torque using artificial intelligence controllers like adaptive neuro-fuzzy inference systems (ANFIS) to enhance the drone's functionality, resilience, and safety. This research has conducted analyses focused on improving the performance of BLDC motors explicitly personalized for unmanned aerial vehicle (UAVs). The proposed method will be implemented using MATLAB/Simulink, expecting to significantly enhance the BLDC motor's performance compared to conventional controllers. Comparative analyses will be conducted between traditional and ANFIS controllers to validate the effectiveness of the proposed approach.

Maximizing solar power generation through conventional and digital MPPT techniques: a comparative analysis

A substantial level of significance has been placed on renewable energy systems, especially photovoltaic (PV) systems, given the urgent global apprehensions regarding climate change and the need to cut carbon emissions. One of the main concerns in the field of PV is the ability to track power effectively over a range of factors. In the context of solar power extraction, this research paper performs a thorough comparative examination of ten controllers, including both conventional maximum power point tracking (MPPT) controllers and artificial intelligence (AI) controllers. Various factors, such as voltage, current, power, weather dependence, cost, complexity, response time, periodic tuning, stability, partial shading, and accuracy, are all intended to be evaluated by the study. It is aimed to provide insight into how well each controller performs in various circumstances by carefully examining these broad parameters. The main goal is to identify and recommend the best controller based on their performance. It is notified that, conventional techniques like INC, P&O, INC-PSO, P&O-PSO, achieved accuracies of 94.3, 97.6, 98.4, 99.6 respectively while AI based techniques Fuzzy-PSO, ANN, ANFIS, ANN-PSO, PSO, and FLC achieved accuracies of 98.6, 98, 98.6, 98.8, 98.2, 98 respectively. The results of this study add significantly to our knowledge of the applicability and effectiveness of both AI and traditional MPPT controllers, which will help the solar industry make well-informed choices when implementing solar energy systems.

Overview of Solar Photovoltaic MPPT Methods: A State of the Art on Conventional and Artificial Intelligence Control Techniques

Overview of Solar Photovoltaic MPPT Methods: A State of the Art on Conventional and Artificial Intelligence Control Techniques

A New Kind of Atomic Force Microscopy Scan Control Enabled by Artificial Intelligence: Concept for Achieving Tip and Sample Safety Through Asymmetric Control

This paper introduces a paradigm shift in atomic force microscope (AFM) scan control, leveraging an artificial intelligence (AI)-based controller. In contrast to conventional control methods, which either show a limited performance, such as proportional integral differential (PID) control, or which purely focus on mathematical optimality as classical optimal control approaches, our proposed AI approach redefines the objective of control for achieving practical optimality. This presented AI controller minimizes the root-mean-square control deviations in routine scans by a factor of about 4 compared to PID control in the presented setup and also showcases a distinctive asymmetric response in complex situations, prioritizing the safety of the AFM tip and sample instead of the lowest possible control deviations. The development and testing of the AI control concept are performed on simulated AFM scans, demonstrating its huge potential.

Integrated application of synergetic approach for enhancing intelligent steam generator control systems

This article focuses on the integrated application of the synergetic approach to enhance the quality of intelligent steam generator control systems.By combining various techniques such as model-based control, adaptive control, and artificial intelligence, an efficient and flexible control system can be developed. Model-based control utilizes mathematical models of steam generators to formulate control algorithms and predict system behavior. Adaptive control enables the system to adapt to changing conditions by adjusting control parameters based on real-time measurements. Artificial intelligence techniques, including neural networks and genetic algorithms, facilitate learning, optimization, and data-driven decision-making processes. The objectives of this research are to investigate the benefits of the synergetic approach in steam generator control, including improved steam generation efficiency, optimized energy consumption, enhanced system stability and reliability, and adaptability to varying operating conditions and disturbances. The findings and conclusions of this study are expected to provide valuable insights for engineers, researchers, and professionals involved in the design and implementation of intelligent steam generator control systems. By integrating the synergetic approach, substantial enhancements in control quality can be achieved, leading to optimal operation and maximum efficiency of power plants.

Recent advances in the precision control strategy of artificial pancreas.

The scientific diagnosis and treatment of patients with diabetes require frequent blood glucose testing and insulin delivery to normoglycemia. Therefore, an artificial pancreas with a continuous blood glucose (BG) monitoring function is an urgent research target in the medical industry. The problem of closed-loop algorithmic control of the BG with a time delay is a key and difficult issue that needs to be overcome in the development of an artificial pancreas. Firstly, the composition, structure, and control characteristics of the artificial pancreas are introduced. Subsequently, the research progress of artificial pancreas control algorithms is reviewed, and the characteristics, advantages, and disadvantages of proportional-integral-differential control, model predictive control, and artificial intelligence control are compared and analyzed to determine whether they are suitable for the practical application of the artificial pancreas. Additionally, key advancements in areas such as blood glucose data monitoring, adaptive models, wearable devices, and fully automated artificial pancreas systems are also reviewed. Finally, this review highlights that meal prediction, control safety, integration, streamlining the optimization of control algorithms, constant temperature preservation of insulin, and dual-hormone artificial pancreas are issues that require further attention in the future.

Avoiding fusion plasma tearing instability with deep reinforcement learning

For stable and efficient fusion energy production using a tokamak reactor, it is essential to maintain a high-pressure hydrogenic plasma without plasma disruption. Therefore, it is necessary to actively control the tokamak based on the observed plasma state, to manoeuvre high-pressure plasma while avoiding tearing instability, the leading cause of disruptions. This presents an obstacle-avoidance problem for which artificial intelligence based on reinforcement learning has recently shown remarkable performance1–4. However, the obstacle here, the tearing instability, is difficult to forecast and is highly prone to terminating plasma operations, especially in the ITER baseline scenario. Previously, we developed a multimodal dynamic model that estimates the likelihood of future tearing instability based on signals from multiple diagnostics and actuators5. Here we harness this dynamic model as a training environment for reinforcement-learning artificial intelligence, facilitating automated instability prevention. We demonstrate artificial intelligence control to lower the possibility of disruptive tearing instabilities in DIII-D6, the largest magnetic fusion facility in the United States. The controller maintained the tearing likelihood under a given threshold, even under relatively unfavourable conditions of low safety factor and low torque. In particular, it allowed the plasma to actively track the stable path within the time-varying operational space while maintaining H-mode performance, which was challenging with traditional preprogrammed control. This controller paves the path to developing stable high-performance operational scenarios for future use in ITER.

Application of artificial intelligence control in the control system of cooling and heating energy stations

In order to understand the application of artificial intelligence in the control system of cold and hot energy station, the author proposes a research based on artificial intelligence control in the control system of cooling and heating energy station. The author first introduces artificial intelligence technology on the basis of heating and precision heating, relies on the ?Internet plus? and IoT platform, applies indoor temperature measurement technology, intelligent control and information docking technology, and establishes a heating system based on artificial intelligence technology. This system has improved the level of enterprise management, promoted the further development of heating and intelligent energy conservation, reduced energy consumption, reduced pollution, and achieved the need for precise heating of the pipe-line network. Secondly, combined with the application case of a district, this district is selected as the application pilot to describe the role and effect of this system in the Hydronics. The experimental results indicate that the heat consumption of the residential area is 27.07 W/m2, while the heat consumption of the comparison residential area is 28 W/m2. Through indoor temperature measurement, it was found that the average indoor temperature of the comparison residential area is 22?C. Therefore, the application of artificial intelligence heating systems has improved the thermal comfort of residents, bringing users a high quality heating experience while relatively reducing energy consumption. The intelligent application of the Hydronics can more accurately grasp the heating situation, timely and efficiently adjust the heating scheme, achieve heating balance, and improve the utilization efficiency of heat sources. At the same time, it can also effectively improve the troubleshooting ability of the hydronics, prevent safety risks, improve management efficiency, and will certainly provide more powerful support for the energy-saving society and environment-friendly society.

An Artificial Intelligent Hybrid Controller for Solar and Battery Fed Five-Level UPQC for Power Quality Improvement

An Artificial Intelligent Hybrid Controller for Solar and Battery Fed Five-Level UPQC for Power Quality Improvement

Using Artificial Intelligence and Numerical Control in the Design and Production of Ceramics

Using Artificial Intelligence and Numerical Control in the Design and Production of Ceramics

Active control for a low-drag Ahmed vehicle model based on steady blowing

An experimental investigation is conducted on the active drag reduction (DR) of an Ahmed body with a rear slant angle of 35°, corresponding to the low-drag regime, using single and combined actuations at the Reynolds number Re = 1.7 × 105. Five different actuations, produced by steady blowing jets, are deployed independently around the edges of the rear slant surface and vertical base, achieving the maximum DR of 1%–9%. An artificial intelligence control system based on ant colony algorithm is used for finding near-optimal control laws of the combined jets. With both DR and control power input considered in the cost function, the maximum DR obtained reaches 18%, though the corresponding control efficiency η (≡ES/EI, where ES and EI are the saved power due to DR and the total input power of the actuations, respectively) is only 0.13. However, η may go up greatly, climbing to 5.8, given a 3% sacrifice of DR. Extensive flow measurements are conducted, with and without control, to understand the flow physics and mechanisms under the control of individual and combined actuations. A linear regression model is established to describe the correlation between the control efficiency and parameters under the combined actuations.

Big Data in Oil and Gas Industry. A New Project Base Learning Technique for Students

The oil-gas and energy industry (extraction, processing, and supply of fossil and renewable resources), requires the processing of thousands of process data in short times (pressure, volume, flow, position, speed, concentration). In the context of the need to increase energy security, safety in operation and especially the obligation to ensure safe control (not affected by cyber wars), the understanding and use of artificial intelligence by students (in the management of technological processes) requires the modification of learning concepts and professional training. Starting from the increased digital skills of the students, the possibility of using PBL (Project Base Learning) in understanding chemical processes and industrial processing data was created within the technological higher education of the University of Oil and Gas. The method consists of the creation together with student teams of software dedicated to artificial intelligence, for the students to understand their role in the management of technological processes and especially in the design of security in the operation of installations. The teams were formed by the leader, critic, project manager and technologist engineer and each team described a technological installation and the problems that may arise. Thus, at the end of the semester, each team presented a security plan and economic operation of the facility, as well as the software created for this purpose. This paper presents a PBL technique in Oil and Gas University and the role of this project activity in learning engineering studies.

Artificial intelligence control for trust‐based detection of attackers in 5G social networks

AbstractThis study introduces a comprehensive framework designed for detecting and mitigating fake and potentially threatening user communities within 5G social networks. Leveraging geo‐location data, community trust dynamics, and AI‐driven community detection algorithms, this framework aims to pinpoint users posing potential harm. Including an artificial control model facilitates the selection of suitable community detection algorithms, coupled with a trust‐based strategy to effectively identify and filter potential attackers. A distinctive feature of this framework lies in its ability to consider attributes that prove challenging for malicious users to emulate, such as the established trust within the community, geographical location, and adaptability to diverse attack scenarios. To validate its efficacy, we illustrate the framework using synthetic social network data, demonstrating its ability to distinguish potential malicious users from trustworthy ones.

Artificial Intelligent Maximum Power Point Controller based Hybrid Photovoltaic/Battery System

Photovoltaic (PV) cells have non-linear properties influenced by environmental factors, including irradiation and temperature. As a result, a method known as maximum power point tracking (MPPT) was implemented to boost the PV cells' efficiency and make the most of the energy they could provide. The traditional perturb and observe (P&O) approach for determining the maximum power point tracking (MPPT) has various drawbacks, including poor steady-state performance, increased oscillation around the MPP point, and delayed reaction. As a result, this work aims to present a hybrid fuzzy logic (FL) and P&O MPPT approach to improve the PV system's performance coupled to the lithium battery storage system. Matlab/Simulink is used to bring the suggested technique to life, after which its efficacy is evaluated in the context of rapid changes in the irradiance level. According to the findings of the simulations, the suggested strategy has the potential to enhance the steady-state performance of PV systems in terms of oscillation and time response. Finally, the proposed results are compared with that obtained by the conventional P&O technique, and the stress of PV power is limited to ∆P=1kW and the overshoot power is limited to 5%.

An Analytic Framework for Assessing Artificial Intelligence and Assistive Automation Enabled Command and Control Decision Aids for Mission Effectiveness

The U.S. Army has significant interest in operationalizing Artificial Intelligence and Assistive Automation (AI/AA) technologies on the battlefield to help collate, classify, and clarify multiple streams of situational and sensor data to provide a Commander with a clear, accurate operating picture to enable rapid and appropriate decision-making. This paper offers a methodology integrated with combat simulation output data into an analytic assessment framework. This framework helps assess AI/AA enabled Decision Aids for command and control with respect to mission effectiveness. Our methodology is demonstrated via a real-world operational vignette of an AI/AA-augmented Battalion assigned to clearing a sector of the battlefield. Results indicate that the simulated scenario with an AI/AA advantage modeled led to a higher expected mission effectiveness score.

Artificial intelligent control of energy management PV system

Renewable energy systems, such as photovoltaic (PV) systems, have become increasingly significant in response to the pressing concerns of climate change and the imperative to mitigate carbon emissions. When static converters are used in solar power systems, they change the current, which uses reactive energy. A proportional-integral controller regulates active and reactive powers, whereas energy storage batteries enhance energy quality by storing current and voltage as they directly affect steady-state error. The utilization of artificial intelligence (AI) is crucial for improving the energy generation of PV systems under various climatic circumstances, as conventional controllers do not effectively optimize the energy output of solar systems. Nevertheless, the performance of PV systems can be influenced by fluctuations in meteorological conditions. This study presents a novel approach for integrating solar PV systems with high input performance through adaptive neuro-fuzzy inference systems (ANFIS). A fuzzy neural inference-based controller regarding energy generation and consumption aspects was designed and examined. This study examines the importance of artificial intelligence in facilitating continuous power supply to clients using a battery system, hence emphasizing its significance in energy management. Moreover, the findings demonstrated promising outcomes in energy regulation and management.

Automated Sprinkle Irrigation Advantages and Disadvantages

In light of continuous progress in agricultural technology, the rise of autonomous systems has led to the development of advanced solutions like the Sprinkler Robot. This comprehensive paper reviews the design, functionality, and applications of Sprinkler Robots in modern agriculture. It emphasizes their vital role in optimizing water distribution and crop management to tackle challenges related to water scarcity and labour-intensive irrigation practices. By examining the integration of Sprinkler Robots into current agricultural frameworks, the study delves into underlying technologies such as artificial intelligence, sensing mechanisms, and precise control. Moreover, it explores the potential environmental and economic benefits linked with the widespread adoption of Sprinkler Robots, underlining their contribution to sustainable and efficient agricultural practices. The paper also evaluates existing limitations and future research prospects, providing a comprehensive perspective on the impact of Sprinkler Robots on the future of precision agriculture.

Preface

The 2023 2nd International Conference on Electronics Technology and Artificial Intelligence (ETAI 2023) took place during August 18th to 20th, 2023 in Changsha, China (hybrid form). ETAI exists as an international annual conference which tries to meet the needs for various kinds of electronics technology and artificial intelligence and has been established as a high-quality international conference.The Conference took “bringing together global wisdom in scientific innovation to promote high-quality development” as the theme. It encouraged the exchange of information on research frontiers in different fields, connected the most advanced academic resources at home and abroad, and turned research results into industrial solutions. It also promoted the development of the fields related to electronics technology and artificial intelligence, expanded the international scientific and academic exchange channels, enhanced scientific innovation on a global scale, and contributed to academic cooperation between China and the international community.The Conference was attended by about 50 participants and hosted 3 keynote speeches, several oral presentations as well as poster presentations. The 2nd ETAI covered various research fields where electronics technology and artificial intelligence are used, such as Power Electronics Technology, Integrated Circuit Design and Integrated Systems, Advanced Electromagnetics, Artificial Intelligence Modeling and Simulation, Intelligent and Artificial Intelligence Control, etc.According to all attendees, the scientific program was excellent with high level of presentations and the scientific environment was fruitful, thus all attendees had a great and creative time. Among them, Prof. Jahangir Hossain (University of Technology Sydney, Australia) first performed a keynote speech. He mainly focuses his study on areas of Power Systems, Wind Generator Integration and Stabilization, Voltage Stability, Micro Grids, Robust Control, etc. Besides, he has published more than 300 articles in international refereed journals and conferences which attracted more than 7100 citations (Google Scholar) with H-index 44. Prof. Bing Shi (Wuhan University of Technology, China), whose research fields cover artificial intelligence and multi-agent systems, also made a keynote report. With rounds of dramatic researches sharing and ideas exchanging, the Conference was made filled with strong academic atmosphere.We would like to thank our host and sponsor for providing their excellent organization and support for the needs of the Conference. We would also like to thank the members of the Organizing Committee as well as the members of the Technical Program Committee. Our gratitude also goes to Journal of Physics: Conference Series for its support in publishing this volume.The Committee of ETAI 2023List of Committee Member are available in the pdf.

Critical infrastructure defense: perspectives from the EU and USA cyber experts

Purpose. To examine the components of cyber autonomy according to the insights of seasoned professionals from the European Union (EU) and the United States of America (USA). The value of each element will be calculated by obtaining data from structured in-depth interviews. Methodology. Through an investigation of different aspects of the research, we used the Delphi technique and research interviews include the option of the Interviewee Transcript Review (ITR). The Delphi method is processed in several rounds, usually three, with two rounds being considered as a minimum and in that respect the Delphi method helps our study explore, predict and identify the nature and fundamental elements of Cyber Autonomy. Findings. The study findings demonstrate that elements such as “Policies”, “Reputation management”, and “Infrastructure and Architecture” hold substantial importance within Cyber Autonomy. These elements are considered critical for future perspectives. The research highlights the role of Cyber Autonomy in streamlining cybersecurity approaches, mitigating the impact of cyber-attacks, and safeguarding possible reputation damage. The study also highlights the importance of well-defined implementation methods and the organizational structure in successfully deploying Cyber Autonomy. Originality. The research demonstrates the interdisciplinary nature of cybersecurity and applies a comprehensive approach covering information security, information security policy, technical and economic aspects, noting the important role of governance in the company share value recovery process. Cyber Autonomy could offer a concept of defense reputation that helps to identify potential cyber threats that are further intensified in connection with the development of various platforms for remote control of artificial intelligence, distance learning, and opportunities for autonomous operation of enterprise systems, the influence of multinational companies on financial markets, and automated decision-making systems. Practical value. Experts’ insights are analyzed that can help to provide practical solutions for the Cyber Autonomy and risk management methods for implementing cyber resilience strategy for critical infrastructure. The research provides adjustments to existing cybersecurity frameworks and directives which consider new cyber elements of information security realities. The current study can be used as a guide to confidence-building measures for possible reputation and financial loss, reinforces protection actions against disinformation or negative cyber impact.

Research on Artificial Intelligence Controller of DC Motor

This paper combines PID control and fuzzy control to control the DC motor. First of the DC motor PID control simulation, given the large range of parameters, the setting process numerous lock, so adopt the genetic algorithm of PID parameters, through the simulation, setting process greatly simplified, and then adopt fuzzy self-setting method to control DC motor, finally, various control scheme for simulation, the results of comparative analysis, shows that the fuzzy self-setting controller performance not only has good dynamic and static quality, and has strong anti-interference ability and parameter time-varying adaptability.adjust