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Fuzzy Model Predictive Formation Maneuver control of Multi-AAVs With Interval Output-Constrained

ieee TRANSACTIONS ON FUZZY systems 2025年第4期33卷 1106-1119页

作者： Du, Zhixu Zhang, Hao Wang, Zhuping Yan, Huaicheng Tongji Univ Shanghai Res Inst Intelligent Autonomous Syst Shanghai 201210 Peoples R China Tongji Univ Natl Key Lab Autonomous Intelligent Unmanned Syst Shanghai 200092 Peoples R China Tongji Univ Frontiers Sci Ctr Intelligent Autonomous Syst Minist Educ Shanghai 200092 Peoples R China Tongji Univ Dept Control Sci & Engn Shanghai 200092 Peoples R China East China Univ Sci & Technol Sch Informat Sci & Engn Key Lab Adv Control & Optimizat Chem Proc Minist Educ Shanghai 200237 Peoples R China

This article considers the formation control of multiple autonomous aerial vehicles (AAVs), where the AAVs operate in dynamic environments with multiple obstacles and narrow (constrained) areas. A new fuzzy model predictive interval output-constrained maneuver formation control method for AAVs is proposed. The interval output constraint algorithm utilizes the distance information to implement output constraints and formation changes, which are more practical but more challenging than the output constraint problem based on user-assigned settling time, especially in narrow areas. One of the distinctive advantages of the proposed output-constrained model predictive controller is that it can activate formation changes and output constraints for AAVs when passing through narrow space, and it can automatically restore the formation of AAVs and deactivate the output constraints when the AAVs are far away from the narrow space. Unlike most nonlinear model predictive control strategy, where predictive control relies on the accurate underlying dynamical system, the article introduces adaptive fuzzy updating law to receding horizon optimization algorithm to estimate and compensate unknown dynamics and external disturbances. Two potential field functions are designed to safely track in 3-D environments with obstacles. Finally, several examples are provided to illustrate the effectiveness of the proposed controller.

关键词： Autonomous aerial vehicles Predictive models Vehicle dynamics Collision avoidance Aerospace electronics Nonlinear dynamical systems Formation control Fuzzy logic systems (FLSs) model predictive formation control output-constrained obstacle avoidance autonomous aerial vehicles (AAVs)

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intelligent control algorithms for industrial automation systems

Intelligent control algorithms for industrial automation sys...

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2025 ieee international Students' conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： Sarathkumar, D. Anil, A.R. Sivadasan, J. Jayakumar, T. Manivel, Murugesan Anandhakumar, C. Engineering Kongu Engineering College Department of Electrical and Electronics Tamil Nadu 638 060 India Sree Buddha College of Engineering Pattoor Department of Computer Science &engineering Kerala Alappuzha India Psr Engineering College Department of Electronics and Communication Engineering Tamil Nadu Sivakasi626140 India Nandha Engineering College Department of Electrical and Electronics Engineering Tamil Nadu 638052 India Karpagam Institute of Technology Department of Electrical and Electronics Engineering Tamilnadu Coimbatore641021 India Sri Ramakrishna Institute of Technology Department of Electrical and Electronic Engineering Tamil Nadu Coimbatore641010 India

ISBN: (纸本)9798331529833

Industrial automation has become a cornerstone of modern manufacturing, enhancing efficiency, reliability, and scalability. The integration of intelligent control algorithms, such as fuzzy logic, neural networks, genetic algorithms, and model predictive control, has revolutionized traditional automation systems, enabling more adaptive, efficient, and robust operations. This paper explores the application and impact of these intelligent control techniques in industrial settings. It highlights how machine learning algorithms can predict equipment failures, optimize processes, and enhance decision-making in real-time. Case studies are presented to illustrate the benefits and challenges of deploying these algorithms across various automated systems, such as robotic assembly lines, process control in chemical plants, and smart warehousing. Furthermore, the paper discusses the integration of these algorithms with industrial IoT (IIoT) platforms, facilitating seamless communication and data exchange between machines and control systems. The research also addresses the challenges of implementing intelligent control, such as computational complexity, security concerns, and the need for real-time processing. Finally, this study evaluates future trends, emphasizing the potential of combining artificial intelligence and deep learning techniques with industrial automation for a more adaptive and autonomous manufacturing ecosystem. © 2025 ieee.

关键词： Genetic algorithms

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Assessment and Management of Security Risks in Accounting Information systems from a Lifecycle Perspective

Assessment and Management of Security Risks in Accounting In...

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2024 international conference on Mechatronics and intelligent control, ICMIC 2024

作者： Gong, Lijiahui Business School Wuhan Huaxia Institute of Technology Wuhan430223 China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

As the carrier of information assets, the importance of accounting information systems is increasing with the development of informatization. Meanwhile, information systems are facing more security threats. How to effectively ensure the security of information systems and prevent information assets from being stolen or damaged has become an inevitable challenge. Therefore, conducting research on the security issues of information systems is of great significance. In response to the data security risks in accounting information systems, this article first establishes a risk indicator system and proposes an information system risk assessment model based on adaptive fuzzy neural networks, which scores the risk indicator attributes during the assessment process. In addition, we propose a fine-grained weight adaptive adjustment algorithm in the model, which provides a quantitative method for the risk indicators of accounting information systems. The feasibility and applicability of the model have been verified through simulation testing. © 2025 SPIE.

关键词： Information security Risk assessment Network security Computer security Computing systems Fuzzy logic Situational awareness sensors Neural networks systems modeling Prototyping

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Research on Optimization of intelligent control systems Based on Deep Reinforcement Learning

Research on Optimization of Intelligent Control Systems Base...

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2024 international conference Optoelectronic Information and Optical Engineering, OIOE 2024

作者： Huang, Tao College of Modern Information Industry Guangzhou College of Commerce Guangdong Guangzhou510000 China College of Electronic and Information Engineering Guangdong Ocean University Guangdong Zhanjiang524000 China

ISBN: (数字)9781510688209

ISBN: (纸本)9781510688193

This study constructs an intelligent control system based on Deep Q-Network (DQN) to improve control efficiency in complex dynamic environments. By employing methods such as input state preprocessing, control action design, and reward function optimization, the system achieves rapid convergence and high-precision control. Through multiple simulation experiments, the results show that the proposed DQN model outperforms the traditional Q-learning algorithm in terms of average cumulative rewards, control accuracy, and energy consumption, demonstrating significant performance advantages. The study indicates that the system possesses good adaptability and efficiency in intelligent control applications, providing important groundwork for future research. © 2025 SPIE.

关键词： Deep reinforcement learning

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Adaptive Neural Network Finite-Time Event Triggered intelligent control for Stochastic Nonlinear systems With Time-Varying Constraints

IEEE Transactions on Artificial Intelligence

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ieee Transactions on Artificial Intelligence 2025年第3期6卷 773-779页

作者： Liu, Jia Liu, Jiapeng Wang, Qing-Guo Yu, Jinpeng Qingdao University School of Automation Qingdao266071 China Beijing Normal University Institute of AI and Future Networks Zhuhai519087 China BNU-HKBU United International College Guangdong Key Laboratory of AI and MM Data Processing Guangdong Provincial Key Laboratory IRADS IAS DST Zhuhai519087 China

Finite-time command-filter event-trigger control based on adaptive neural network is presented in this article for a class of output-feedback stochastic nonlinear system (SNS) with output time-varying constraints and unmeasured states. The adaptive neural network combined with backstepping is utilized to approximate the unknown nonlinear functions of the system. The finite-time command-filter is employed to reduce the difficulty of complex calculation caused by backstepping technique. An adaptive observer is developed to estimate unmeasured states, and a controller is designed to be triggered only when the event-triggered condition is met. The time-varying barrier Lyapunov function is utilized to ensure the output time-varying constraint. The control method proposed in this article not only guarantees the finite-time stability of the system but also meets the output constraint. The effectiveness of the method is demonstrated on the ship maneuvering system with three degrees of freedom. © 2024 ieee.

关键词： Stochastic systems

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Research on Optimization control Model and Algorithm for Sewage Treatment Process

Research on Optimization Control Model and Algorithm for Sew...

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2024 international conference on Mechatronics and intelligent control, ICMIC 2024

作者： Huang, Lanlin Wang, Wenfu College of Resources and Environment Yunnan Land and Resources Vocational College Kunming650217 China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

The present research addresses the challenge of optimizing control in the wastewater treatment process, presenting a refined control model rooted in the particle swarm optimization (PSO) algorithm. Through a comprehensive examination of the nonlinear and interdependent multivariable dynamics inherent in wastewater treatment systems, a tailored control model suitable for real-world operational conditions is developed. In the algorithm design phase, the PSO algorithm is employed to fine-tune control parameters, enhancing both the system's stability and overall treatment performance. The creation of the simulation framework integrates several crucial elements of the wastewater treatment process, thereby ensuring the model's robustness and practical applicability in diverse operational scenarios. The results from the simulation highlight that the proposed optimization control model offers considerable benefits in minimizing energy usage while boosting treatment effectiveness, demonstrating strong adaptability and global convergence characteristics. Subsequent data analysis corroborates the model's precision and practicality, delivering valuable technical insights for the intelligent optimization of wastewater treatment processes. © 2025 SPIE.

关键词： Particle swarm optimization (PSO)

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Optimal Stochastic Injection Attack Strategy Against Discrete Time-Varying System via Moment Matrices

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ieee TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 11026-11037页

作者： Gao, Sheng Gramlich, Dennis Zhang, Hao Yan, Huaicheng Ebenbauer, Christian Tongji Univ Coll Elect & Informat Engn Dept Control Sci & Engn Shanghai 200092 Peoples R China Rhein Westfal TH Aachen Chair Intelligent Control Syst D-52074 Aachen Germany Tongji Univ Shanghai Inst Intelligent Sci & Technol Shanghai 200092 Peoples R China East China Univ Sci & Technol Key Lab Adv Control & Optimizat Chem Proc Minist Educ Shanghai 200237 Peoples R China East China Univ Sci & Technol Sch Informat Sci & Engn Shanghai 200237 Peoples R China

This paper investigates injection attacks incorporating stochastic noise against linear discrete time-varying system, which is more general but also more challenging to defend than deterministic injection attacks. Based on the optimization theory and novel key defining matrices, an optimal stochastic injection attack strategy is proposed. Unlike existing strategies, this strategy is stochastic, making it harder for defenders to predict. Therefore, the newly designed attack is expected to be widely used to disrupt system performance. By leveraging estimated data from the observer and the attack input, a virtual residual system is established, which more accurately reflects changes in the system error before and after the attack than the traditional error system. Using the state and output residuals along with the stochastic attack input, two performances are defined to ensure the stealthiness and effectiveness of the attack, respectively. Subsequently, an optimal attack problem with non-convex objective function and constraint is formulated. The key to acquiring the designed optimal stochastic injection attack strategy is to apply a semi-definite relaxation involving moment matrices for transforming this non-convex optimization problem into a convex optimization problem and solving it. Finally, the effectiveness of the proposed attack strategy is validated through numerical simulation of a networked mass-spring-damper system and a V-formation experiment involving three quadrotors. Note to Practitioners-The primary objective of this paper is to focus on the cyber security of discrete time-varying systems from the perspective of the attacker, which provides insight into the way of generating attack strategies under the stochastic case. The majority of existing injection attack strategies against intelligent systems are deterministic, and this can make the attacks less effective as the attacked system reconstructs and compensates for the attack signals, and the attack s

关键词： Time-varying systems Optimization Observers Linear programming Programming Estimation error Numerical simulation Convex functions control systems Automation Stochastic injection attack non-convex optimization problem moment matrices semi-definite programming

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RFID Door Lock Access control systems: Trends, Technologies and Applications 3

RFID Door Lock Access Control Systems: Trends, Technologies ...

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3rd international conference on intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Nair, Adwaith A. Adithyan, R. Unni, Akshara Nalinakshan, Shankar Department of Electrical and Electronics Engineering Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9798331527549

Radio Frequency Identification (RFID) door lock systems are at the forefront of modern access control technology, combining security, convenience, and scalability. This paper presents a comprehensive review of RFID-based access control systems, highlighting their fundamental components, operational mechanisms, and advantages over traditional methods. Key innovations in RFID technology, such as integration with IoT platforms and mobile devices, are discussed in the context of enhancing system functionality and user experience. Moreover, this paper examines the challenges associated with RFID implementations, including data security vulnerabilities, signal interference, and high installation costs, while suggesting potential solutions. Through this analysis, the paper highlights the growing relevance of RFID access systems in residential, commercial, and industrial environments, paving the way for further advancements in secure and intelligent access control *** research identifies key challenges in traditional access control systems, such as susceptibility to breaches, lack of scalability, and operational inefficiency. RFID door lock systems emerge as an advanced solution, integrating wireless communication, IoT platforms, and mobile devices to address these limitations. This study presents an in-depth analysis of RFID systems, highlighting their components, operational mechanisms, and comparative advantages. Considering issues such as vulnerability to data security, interference signals, and a high price tag for the installation, this paper suggests possibilities with solutions of viable, secure, scalable, and intelligent access control systems. Hence, the transformative RFID technology into a residential, commercial, or industrial domain holds an immense study area. © 2025 ieee.

关键词： Radio frequency identification (RFID)

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Research on intelligent Voice Broadcast System for Smart Water Conservancy 19

Research on Intelligent Voice Broadcast System for Smart Wat...

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19th international conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Huang, Zhenqian Chen, Lanxin College of Computer Science and Software Engineering Hohai University Nanjing China University of Birmingham Birmingham United Kingdom

ISBN: (纸本)9798331507817

Smart water management imposes higher demands on the real-time capabilities and intelligence of watershed information monitoring systems. Therefore, we propose an intelligent voice broadcast system for monitoring water information, aimed at achieving efficient and accurate dissemination of warning information. This system can be deployed and integrated into existing water information monitoring systems to enable real-time voice broadcasting of water-related information. To ensure the system’s timeliness and effectiveness, we propose a Performance Optimization Mechanism for Data Processing, Priority control for voice broadcasting, a Content Optimization Mechanism, and a Timeliness control Mechanism. Comparative experiments demonstrate that the proposed intelligent voice broadcast system significantly enhance the efficiency and accuracy of water information dissemination, providing an innovative solution for real-time monitoring and early warning in water management. © 2025 ieee.

关键词： Information management

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Research on Spares Optimization Method of the Complex Electronic Information System Based on Monte Carlo Simulation

Research on Spares Optimization Method of the Complex Electr...

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2024 international conference on Mechatronics and intelligent control, ICMIC 2024

作者： Lu, Yanchun Yang, Hongqi Lei, Ting Pan, Yong CEPREI Guangzhou510610 China Key Laboratory of Industrial Equipment Quality Big Data MIIT Guangzhou510610 China Guangdong Provincial Engineering Technology Research Center for Quality Big Data of High-end Equipment Guangzhou510610 China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

In order to solve the problems such as high maintenance cost and difficult spare parts optimization of the complex electronic information system, considering advantages of the generalized stochastic Petri Net (GSPN) in characterization and analysis of dynamic change process of complex system, the process and method of spare parts optimization based on Monte Carlo simulation are studied to achieve the best balance between the rate of mission success and the cost of maintenance. © 2025 SPIE.

关键词： Stochastic systems

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