Application Of Intelligent Control Research Articles

Safe dynamic optimization of automatic generation control via imitation-based reinforcement learning

IntroductionThe increasing penetration of distributed generation (e.g., solar power and wind power) in the energy market has caused unpredictable disturbances in power systems and accelerated the application of intelligent control, such as reinforcement learning (RL), in automatic generation control (AGC). However, traditional RL cannot ensure constraint safety during training and frequently violates the constraints (e.g., frequency limitations), further threatening the safety and stability of grid operation.MethodsTo address the safety issue, we propose a novel safe RL framework that combines expert experiences with the RL controller to achieve imitation-based RL. This method allows an initialized safe policy by imitating expert experiences to prevent random explorations at the beginning. Specifically, we first formulate the AGC problem mathematically as a Markov decision process. Then, the imitation mechanism is developed atop a soft actor–critic RL algorithm.Results and discussionFinally, numerical studies are conducted with an IEEE 39-bus network, which show that the proposed method satisfies the frequency control performance standard better and improves the RL training efficiency.

Application of intelligent controller to speed up vibration attenuation of a sandwich smart structure subjected to external excitation

Intelligent controllers represent a class of advanced control systems designed to adapt, learn, and make decisions based on changing environments and dynamic conditions. These controllers leverage artificial intelligence and computational techniques to enhance traditional control methods, providing a level of adaptability and sophistication that is particularly valuable in complex and uncertain systems. In this work for the first time, the Proportional–integral–derivative (PID) controller the intelligent controller is used to speed up vibration attenuation of a sandwich smart structure subjected to external excitation. The sandwich smart structure is made of a composite core and sensor and actuator patches. Using shear deformation theory, Hamilton’s principle, and compatibility equations, the governing equations of the sandwich smart structure under external excitations are obtained. After that using numerical solver and time-dependent solution procedure, the equations are solved. After obtaining the datasets via mathematical modeling, this kind of dataset is used as the input and output data to train, validate, and test the machine learning method to use it with low computational cost. After that, using this kind of machine learning method, future researchers can use it to estimate a control vibration of the sandwich structure after mechanical excitation. The results suggest some applicable recommendations to speed up the vibration attenuation of a sandwich smart structure subjected to external excitation.

Application of Intelligent Remote Control Combined with Machine Vision in Coal Mine Electromechanical Equipment

Application of Intelligent Remote Control Combined with Machine Vision in Coal Mine Electromechanical Equipment

Research on intelligent control and sensor application strategy of motor based on RFID technology

Research on intelligent control and sensor application strategy of motor based on RFID technology

Application of Intelligent Control in Chromatography Separation Process

Chromatographic separation plays a pivotal role in the manufacturing of chemical products and biopharmaceuticals. This technique exploits differences in distribution between stationary and mobile phases to separate mixtures, impacting final product quality. Simulated moving bed (SMB) technology, recognized for its continuous feed, enhances efficiency and increases production capacity while reducing solvent and water consumption. Despite its complexity in controlling variables like flow rates and valve switching times, traditional control theories fall short. This study introduces an intelligent fuzzy controller resembling an approximate neural network (NN) for SMB control. Simulation results demonstrate the controller’s effectiveness in achieving desirable outcomes for the SMB system.

Transportation Internet: A Sustainable Solution for Intelligent Transportation Systems

New challenges such as automation, connection, electrification, and sharing (ACES) have brought disruptive changes to vehicles, transportation, and mobility services, which urgently requires an ideal solution for sustainable transportation. This paper introduces the Internet as a paradigm and, for the first time, proposes the Transportation Internet (TI), inspired by the similarity between the Internet and transportation. Referring to the construction ideas of the Internet, this paper establishes the framework of TI, proposes the transportation router based on the transportation switching and routing models, and preliminarily forms a large-scale automatic transportation solution. Following the latest technologies of the Internet, this paper further presents the software-defined transportation (SDT) by separating the control plane and transport plane of the transportation router, which can enhance transportation routing and provide Internet-like capabilities such as centralized intelligent control, terminals plug-and-play, and open application ecology. The evaluation of the prototype system shows promising results. The software-defined signals (SDS) can save 36% energy compared to signal machines, and the software-defined vehicles (SDV) automatic driving can save 24% energy compared to manual driving. Overall, TI brings innovations to sustainable transportation, and provides a framework for a new generation of Intelligent Transportation Systems (ITS).

A Review on Intelligent Control Theory and Applications in Process Optimization and Smart Manufacturing

In the evolving landscape of manufacturing, the integration of intelligent control theory stands as a pivotal advancement, driving both process optimization and the paradigm of smart manufacturing. This review delves into the multifaceted applications of intelligent control theory, emphasizing its role in equipment, operations, and controls optimization. With a focus on three primary methodologies—fuzzy logic, neural networks, and genetic algorithms—the paper elucidates their biological parallels and their significance in simulation, modeling, and optimization. The transformative potential of smart manufacturing, synonymous with Industry 4.0, is also explored, highlighting its foundation in data, automation, and artificial intelligence. Drawing from a comprehensive analysis of recent literature, the review underscores the growing interest in this domain, as evidenced by the surge in publications and citations over the past decade. The overarching aim is to provide contemporary discourse on the applications and implications of intelligent control theory in the realms of process optimization and smart manufacturing.

Analysis of the application of intelligent CNC technology in machinery manufacturing

The application of intelligent computer numerical control(CNC) technology enjoys a key strategic position in various countries, and countries that attach importance to the development of CNC machine tools and their fields of innovation branches generally have a solid foundation of intelligent manufacturing and strong production capacity. Combined with big data and artificial intelligence, CNC technology has become an important cornerstone of smart manufacturing. In order to achieve the goal of sustainable development, the machinery manufacturing industry should not only improve product accuracy and enrich product types, but also shorten the production cycle as much as possible and avoid the impact of human errors on product quality. Based on this, this paper mainly analyzes in detail the actual development of machinery manufacturing based on intelligent robot numerical control technology in order to provide some help for the long-term development of the industry. This paper is dedicated to the visualization of relevant data and finally provides a reliable basis for the corresponding decision-making based on the current actual development of intelligent CNC technology. After this paper analyzes the current situation of the industry, we find that intelligent numerical control technology has a vital role in the intelligent transformation of China’s industry, high-tech innovation, and rationalization of AI applications, and we hope that this paper can provide a reference for China’s industrial production industry.

Application of Intelligent controller For Load Frequency Control for Multi-Area Multi-Source Power System

Application of Intelligent controller For Load Frequency Control for Multi-Area Multi-Source Power System

Design of VO2-based spacecraft smart radiator with low solar absorptance

Thermochromic film with the ability to self-adaptively switch emittance is important for spacecraft missions experiencing rapidly increasing demand. Current research on thermochromic films primarily focuses on optimizing emittance tunability, while the solar absorptance of these films remains high, limiting their practical application in spacecraft thermal control. In this work, a VO2-based spacecraft smart radiator with low solar absorptance and high emittance tunability is proposed. The smart radiator is a multilayer structured film composed of a switchable resonator (i.e., a superposed Fabry-Perot cavity) and a solar reflector (i.e., a Distributed Bragg Reflector). The performance of the smart radiator is optimized by combining the Rigorous Coupled Wave Analysis method and Genetic Algorithm, achieving a normal solar absorptance of 0.121 and emittance tunability of 0.825. The solar absorptance and emittance tunability of the smart radiator remain excellent even at large incident angles. The underlying mechanisms involved in the smart radiator are attributed to the combination of superposed Fabry-Perot resonance and multiple solar reflectance. In addition, we provide a numerical example using a geosynchronous orbit satellite to demonstrate that the regulation range of the net heat dissipation flux density can increase from 146.5–463.9 W/m2 by using Optical Solar Reflector to 0–494.6 W/m2 when using the proposed smart radiator within the permissible temperature range (263.15–318.15 K) of spacecraft. This VO2-based spacecraft smart radiator with outstanding properties which outperform the existing thermochromic films reveals the promising potential of thermochromic films for spacecraft intelligent thermal control applications.

Preface

With the rapid development of science and technology, the development and innovation in all domains become faster and faster. The field of materials science and intelligent manufacturing have also produced more research directions and new discoveries. Focusing on the latest research results in various aspects of that and other related fields, 2023 International Conference on Materials Science and Intelligent Manufacturing (MSIM 2023) was held via virtual form in Xi’an, China from January 13th to 15th, 2023.Our aim is to establish a forum to exchange and share successful experiences of innovation in the above fields and establish an effective cooperation platform for institutions and industries. Focusing on such topics as Electronic and Magnetic Materials, Ceramic and Glass Materials, Crystal Defect Engineering, Intelligent Mechatronics, Advanced Manufacturing Technology, Integrated Manufacturing System, etc., the Conference invited scientists, engineers, educators and students from all over the world to exchange their current technical application experience and research results, so as to promote the development of related fields.The entire Conference was in an online mode. First is an about 3 hour’s keynote speech and next 2.5 hour’s oral presentation. Discussion and Question and Answer happened went through out the meeting. The professors and delegates presented papers and researches from their own research domains and experiences. We had about 50 participants in the online platform, and had continuous stable internet connection, conducting the virtual conference smoothly.In the Conference, four renowned professors acting as keynote speakers shared their researches with us. Among them, Prof. Xiaofang Yuan from Hunan University, China, whose research domains cover Intelligent Control Theory and Application, Electric Vehicle Control, Robot Drive Control, first made a speech on 3D Map-based Path Planning for Autonomous Vehicles. In the traditional vehicle area, energy-based motion control technology and battery technology are usually employed to solve the energy-saving problem for vehicles. His research proposed a new solution from the perspective of path planning. For vehicles traveling on the complex 3D terrains, the energy consumption of up-slope is far greater than that of the flat road and down-slope. To realize this, a path with a good trade-off between the energy consumption and distance would be the expected route for electric vehicle and mining transportation. A novel multi-objective path planning method was investigated to solve this problem for EV and mining truck, generating an optimal path while saving much energy.We would like to express our gratitude to all the participants for their fruitful work and excellent contribution to MSIM 2023. Especially, we appreciate the enormous work of the editorial board and reviewers in the preparation of this volume. Also, our special thanks and appreciation go to the Journal of Physics: Conference Series. Hopefully, all participants and other interested readers can benefit scientifically from the proceedings and find it rewarding in the process.The Committee of MSIM 2023List of Committee Member is available in the pdf.

Artificial Intelligence/Machine Learning in Energy Management Systems, Control, and Optimization of Hydrogen Fuel Cell Vehicles

Environmental emissions, global warming, and energy-related concerns have accelerated the advancements in conventional vehicles that primarily use internal combustion engines. Among the existing technologies, hydrogen fuel cell electric vehicles and fuel cell hybrid electric vehicles may have minimal contributions to greenhouse gas emissions and thus are the prime choices for environmental concerns. However, energy management in fuel cell electric vehicles and fuel cell hybrid electric vehicles is a major challenge. Appropriate control strategies should be used for effective energy management in these vehicles. On the other hand, there has been significant progress in artificial intelligence, machine learning, and designing data-driven intelligent controllers. These techniques have found much attention within the community, and state-of-the-art energy management technologies have been developed based on them. This manuscript reviews the application of machine learning and intelligent controllers for prediction, control, energy management, and vehicle to everything (V2X) in hydrogen fuel cell vehicles. The effectiveness of data-driven control and optimization systems are investigated to evolve, classify, and compare, and future trends and directions for sustainability are discussed.

Application of Intelligent Control in Mechatronics System

Application of Intelligent Control in Mechatronics System

Effectiveness Study of Artificial Intelligent Facility System in Maintaining Building Fire Safety (Case Study: Typical Public Building Cases of Fire-Fighting Facilities Management in China)

Artificial intelligence plays a decisive role in the healthy and sustainable development of society. The rapid development of the economy and society has put forward higher standards and requirements for fire supervision. The development of science and technology has advanced the intelligence on fire protection. Through the application of intelligent fire control, the fire department can better supervise the fire control of various units and further improve the pertinence and effectiveness of fire control supervision. Artificial intelligence technology is widely used in all stages of construction projects, which brings many conveniences to the construction and use of projects. Based on the analysis of the current situation and existing problems of the basic maintenance of fire-fighting facilities, this project analyzes the effect of the application of artificial intelligence technology in the management of fire-fighting facilities. Therefore, the purpose of this project is to study the role and effectiveness of artificial intelligence technology in the management of fire-fighting facilities and further verify this conclusion with questionnaires and interviews.

A multi-stimulus-responsive bionic fish microrobot for remote intelligent control applications.

In nature, all creatures have their unique characteristics that allow them to adapt to the complex and changeable living environments. In recent years, bionic fish has received increased attention from the research community, and many fish-like microrobots driven by the Marangoni effect have been developed. They are generally characterized by easy operation and rapid driving. However, traditional fish-like microrobots can only be driven by a single stimulus and move on two-dimensional (2D) gas-liquid interfaces, which greatly limits their ability in obstacle avoidance and transportation. In this article, we propose a multi-stimulus-responsive bionic fish microrobot, which is made of temperature-responsive hydrogel poly(N-isopropylacrylamide) (pNIPAM). This microrobot is impregnated with carbon nanotubes (CNTs) and Fe3O4 and therefore has magnetic and photothermal conversion properties. Under the action of optical, magnetic or ethanol molecules, the microrobot can perform complex programmable translational motion on 2D surfaces and controllable rising and sinking, while realizing motion simulation and obstacle avoidance. The microrobot is expected to be used for a wide range of applications in intelligent control systems.

Methods of Intelligent Control in Mechatronics and Robotic Engineering: A Survey

Artificial intelligence is becoming an increasingly popular tool in more and more areas of technology. New challenges in control systems design and application are related to increased productivity, control flexibility, and processing of big data. Some kinds of systems require autonomy in real-time decision-making, while the other ones may serve as an essential factor in human-robot interaction and human influences on system performance. Naturally, the complex tasks of controlling technical systems require new modern solutions, but there remains an inextricable link between control theory and artificial intelligence. The first part of the present survey is devoted to the main intelligent control methods in technical systems. Among them, modern methods of adaptive and optimal control, fuzzy logic, and machine learning are considered. In its second part, the crucial achievements in intelligent control applications in robotic and mechatronic systems over the past decade are considered. The references are structured according to the type of such common control problems as stabilization, controller tuning, identification, parametric optimization, iterative learning, and prediction. In the conclusion, the main problems and tendencies toward intelligent control methods improvement are outlined.

A review on design and implementation of type‐2 fuzzy controllers

Nowadays, advances in different fields of technology have increased demands for reliable controllers. Uncertainty and disturbances, which are inevitable in most real‐world systems, as well as increasing complexity in the dynamics of many systems, are requiring the design and application of intelligent controllers. Fuzzy controllers, and specifically type‐2 fuzzy control techniques, could play a beneficial role in a variety of control purposes, since they are robust against uncertainties. Therefore, an immense body of research is devoted to these fuzzy control techniques. This paper introduces a comprehensive review about the most recent advances in the design and implementation of type‐2 fuzzy control schemes both for integer‐ and fractional‐order systems. Thus, in addition to the past and present achievements in this context, future trends are also delineated. Moreover, most important works dealing with type‐2 fuzzy controller are discussed, and their advantages and drawbacks are outlined. Consequently, we firmly believe the present paper will pave the way for future research in this area.

Preface

Due to recent pandemic, 2021 3rd International Conference on Robotics, Intelligent Control and Artificial Intelligence (ICRICA 2021) was held virtually online on December 03 - 05, 2021. The decision to hold the virtual conference was made in compliance with many restrictions and regulations that were imposed by countries around the globe. Such restrictions were made to minimize the risk of people contracting or spreading the COVID-19 through physical contact. There were 150 individuals who attended this on-line conference, represented many countries.ICRICA 2021 brings together industry professionals and academics from research institute, governmental agencies, and universities around the world to exchange information on advances in Robotics, Intelligent Control and Artificial Intelligence. The objective of the ICRICA 2021 is to facilitate the exchange between researchers and practitioners in the area of Robotics, Intelligent Control and Artificial Intelligence and related technologies, and provides attendees with a unique opportunity to share, discuss, and witness research results, thereby creating a common platform for exchanging ideas and fostering future developments in the field.During the conference, the conference model was divided into three sessions, including oral presentations, keynote speeches, and online Q&A discussion. In the first part, some scholars, whose submissions were selected as the excellent papers, were given about 5-10 minutes to perform their oral presentations one by one. Then in the second part, keynote speakers were each allocated 30-45 minutes to hold their speeches. In the second part, we invited three professors as our keynote speakers. Prof. Rüdiger Dillmann, Karlsruhe Institute of Technology, German (IEEE Fellow, IROS Fellow). His research interest is in the areas of humanoid robotics and neurorobotics with special emphasis on intelligent, autonomous, and interactive robot behavior generated with the help of machine learning methods and programming by demonstration (PbD). The second keynote speakers, Prof. Xiaofang Yuan, Hunan University, China. His research area includes intelligent control theory and application, electric vehicle control, robot drive control. Lastly, we were glad to invite Prof. Hui Zhang, Hunan University, China as our finale keynote speakers. He is mainly engaged in the research of intelligent robot vision detection, deep learning image recognition and other fields. Their insightful speeches had triggered heated discussion in the third session of the conference. Every participant praised this conference for disseminating useful and insightful knowledge.The proceedings are a compilation of the accepted papers and represent an interesting outcome of the conference. Topics include but are not limited to the following areas: Robot structure design and control, Sensor/actuator networks, Robot kinematics and dynamics, Robot operating system and other related topics. All the papers have been through rigorous review and process to meet the requirements of international publication standard.List of Committee member are available in this pdf.

Safe Reinforcement Learning Algorithm and Its Application in Intelligent Control for CPS

PDF HTML XML Export Cite reminder Safe Reinforcement Learning Algorithm and Its Application in Intelligent Control for CPS DOI: 10.21655/ijsi.1673-7288.00284 Author: Affiliation: Clc Number: Fund Project: Article | Figures | Metrics | Reference | Related | Cited by | Materials | Comments Abstract:The design of a safe controller for a Cyber-Physical System (CPS) is a hot research topic. The existing safety controller design based on formal methods has problems such as excessive reliance on models and poor scalability. Intelligent control based on Deep Reinforcement Learning (DRL) can handle high-dimensional nonlinear complex systems and uncertain systems and is becoming a very promising CPS control technology, but it lacks safety guarantees. This study addresses the safety issues of Reinforcement Learning (RL) control by analyzing a typical case of an industrial oil pump control system and carries out research on a Safe Reinforcement Learning (SRL) algorithm and intelligent control application. First, the SRL issue of the industrial oil pump control system is formalized, and a simulation environment of the oil pump is built. Then, by designing the structure and activation function of the output layer, an oil pump controller in the form of a neural network is constructed to satisfy the linear inequality constraints of the on-off operations of the oil pump. Finally, in order to better balance the safety and optimality control objectives, a new SRL algorithm is designed based on the Augmented Lagrange Multiplier (ALM) method. A comparative experiment on the industrial oil pump shows that the controller synthesized by the proposed algorithm surpasses existing similar algorithms both in safety and optimality. During the evaluation, the neural network controllers synthesized in this study pass rigorous formal verification with a probability of 90%. Meanwhile, compared with the theoretically optimal controller, neural network controllers achieve an optimal objective value loss of 2%. The proposed method is expected to be applied in more scenarios, and the case study scheme may provide a reference for other researchers in the field of safe intelligent control and formal verification. Reference Related Cited by

Numerical and Experimental Research on the Brushing Aluminium Alloy Mechanism Using an Abrasive Filament Brush.

Abrasive filament brushes have been widely used in surface processes for a wide range of applications, including blending, edge-radiusing, and polishing. However, the associated brush mechanics of material removal is still not clear. In order to analyze the brush grinding of aluminium alloy, this paper constructed a kinematic model of a single filament, simulated the scratch process of a single abrasive grain, and investigated the brush force and material removal based on the finite element approach. The simulated result shows that the brush grinding can be changed from elastic–plastic deformation to chip formation when increasing the brush speed to 1000 r/min. The normal and tangential forces increase linearly and quadratically with the increase in the rotation speed (500–5000 r/min), respectively, and increase linearly with the increase in the penetration depth (0.1–1 mm), which is consistent with the experiment results. In addition, the amount of material removal initially increases with the increase in penetration depth, and then decreases. This paper provides a new approach to understanding the process of material removal and is helpful for the selection of reasonable brush parameters in the intelligent grinding control application.