Complex Engineering Systems最新文献

Enhancing unmanned aerial vehicle communication through distributed ledger and multi-agent deep reinforcement learning for fairness and scalability 通过分布式账本和多代理深度强化学习增强无人机通信的公平性和可扩展性

Complex Engineering Systems Pub Date : 2024-07-25 DOI: 10.20517/ces.2024.10

Farman Ali, Muhammad Ahtasham, Zahra Anfaal

{"title":"Enhancing unmanned aerial vehicle communication through distributed ledger and multi-agent deep reinforcement learning for fairness and scalability","authors":"Farman Ali, Muhammad Ahtasham, Zahra Anfaal","doi":"10.20517/ces.2024.10","DOIUrl":"https://doi.org/10.20517/ces.2024.10","url":null,"abstract":"Unmanned Aerial Vehicles (UAVs) are pivotal in enhancing connectivity in diverse applications such as search and rescue, remote communications, and battlefield networking, especially in environments lacking ground-based infrastructure. This paper introduces a novel approach that harnesses Multi-Agent Deep Reinforcement Learning to optimize UAV communication systems. The methodology, centered on the Independent Proximal Policy Optimization technique, significantly improves fairness, throughput, and energy efficiency by enabling UAVs to autonomously adapt their operational strategies based on real-time environmental data and individual performance metrics. Moreover, the integration of Distributed Ledger Technologies with Multi-Agent Deep Reinforcement Learning enhances the security and scalability of UAV communications, ensuring robustness against disruptions and adversarial attacks. Extensive simulations demonstrate that this approach surpasses existing benchmarks in critical performance metrics, highlighting its potential implications for future UAV-assisted communication networks. By focusing on these technological advancements, the groundwork is laid for more efficient, fair, and resilient UAV systems.","PeriodicalId":504274,"journal":{"name":"Complex Engineering Systems","volume":"22 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141802962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Extended state observer-based fault-tolerant control for an unmanned surface vehicle under asynchronous injection and deception attacks 异步注入和欺骗攻击下基于扩展状态观测器的无人水面飞行器容错控制

Complex Engineering Systems Pub Date : 2024-07-24 DOI: 10.20517/ces.2024.19

Haiwen Wang, Chun Liu, Xiao Huang, Yuxuan Zhong, Dong Qu, Ron J. Patton

{"title":"Extended state observer-based fault-tolerant control for an unmanned surface vehicle under asynchronous injection and deception attacks","authors":"Haiwen Wang, Chun Liu, Xiao Huang, Yuxuan Zhong, Dong Qu, Ron J. Patton","doi":"10.20517/ces.2024.19","DOIUrl":"https://doi.org/10.20517/ces.2024.19","url":null,"abstract":"This paper explores the problem of fault-tolerant control concerning an underactuated unmanned surface vehicle affected by actuator faults and disturbances in the physical layer and multiple cyber threats (time-varying delays, injection attacks, and deception attacks) in the networked layer. Firstly, an extended state observer is designed to estimate the relative state and fault information by constructing the estimation error term based on the output information affected by injection attack and delay. Secondly, a novel fault-tolerant controller is designed to deal with random Bernoulli deception attacks and compensate for time-varying delay and actuator faults by using the estimated information and considering the probability dynamics of deception attacks. Assuming that dual-channel asynchronous independent injection and deception attacks occur on the sensor-to-observer and observer-to-controller channels. A sufficient condition for asymptotic stability of the unmanned surface vehicle is derived by using Lyapunov-Krasovskii functional within the co-design framework of fault estimation and fault-tolerant control, and ensured by eliminating the equality constraint. Finally, the efficacy of the proposed algorithm is assessed through simulations of the unmanned surface vehicle under two distinct scenarios: low forward speed and high forward speed.","PeriodicalId":504274,"journal":{"name":"Complex Engineering Systems","volume":"16 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141806707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Layered random fault injection method for the air braking system based on multiple Markov chains 基于多马尔可夫链的空气制动系统分层随机故障注入法

Complex Engineering Systems Pub Date : 2024-04-01 DOI: 10.20517/ces.2024.02

Zhiwen Chen, Jingke Fan, Lijuan Peng, Hao Luo, Chao Cheng, Zhiyong Chen

{"title":"Layered random fault injection method for the air braking system based on multiple Markov chains","authors":"Zhiwen Chen, Jingke Fan, Lijuan Peng, Hao Luo, Chao Cheng, Zhiyong Chen","doi":"10.20517/ces.2024.02","DOIUrl":"https://doi.org/10.20517/ces.2024.02","url":null,"abstract":"The air braking system is crucial for the safe operation of high-speed trains but is susceptible to faults from harsh environments and prolonged use. However, faulty data in practice are still rare because of the \"safety oriented principle\". For this purpose, fault injection is regularly employed. Due to the stochastic nature of faults in the system, random fault injection can more realistically simulate faulty scenarios compared to the deterministic fault injection. The traditional method entails analyzing a significant amount of raw data to extract the fault distribution function, followed by random sampling. However, the obstacles lie in the scarcity of raw fault data and the labor-intensive nature of constructing the fault distribution function. This paper proposes a layered random fault injection method based on multiple Markov chains. First, a multi-layer structured fault model base is established for the system, followed by the implementation of layered fault injection. Then, the random fault types and degrees are realized using Markov chains, in which the fault probability function is determined by the state transition matrix. Subsequently, a low-complexity Alias sampling algorithm is proposed for discrete random sampling. The nominal model is transformed into a corresponding fault model based on the sampling outcomes, facilitating the acquisition of fault data. Finally, a graphical user interface is developed to present and visualize the validation results.","PeriodicalId":504274,"journal":{"name":"Complex Engineering Systems","volume":"54 18","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140788386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Active manipulation of a tethered drone using explainable AI 利用可解释人工智能主动操纵系留无人机

Complex Engineering Systems Pub Date : 2024-03-28 DOI: 10.20517/ces.2023.33

Shraddha Barawkar, Manish Kumar

{"title":"Active manipulation of a tethered drone using explainable AI","authors":"Shraddha Barawkar, Manish Kumar","doi":"10.20517/ces.2023.33","DOIUrl":"https://doi.org/10.20517/ces.2023.33","url":null,"abstract":"Tethered drones are currently finding a wide range of applications such as for aerial surveillance, traffic monitoring, and setting up ad-hoc communication networks. However, many technological gaps are required to be addressed for such systems. Most commercially available tethered drones hover at a certain position; however, the control task becomes challenging when the ground robot or station needs to move. In such a scenario, the drone is required to coordinate its motion with the moving ground vehicle without which the tether can destabilize the drone. Another challenging aspect is when the system is required to operate in GPS denied environments, such as in planetary exploration. In this paper, to address these issues, we take advantage of passive or force-based control in which the tension in the tether is sensed and used to drive the drone. Fuzzy logic is used to implement the force-based controller as a tool for explainable Artificial Intelligence. The proposed fuzzy logic controller takes tether force and its rate of change as the inputs and provides desired attitudes as the outputs. Via simulations and experiments, we show that the proposed controller allows effective coordination between the drone and the moving ground rover. The rule-based feature of fuzzy logic provides linguistic explainability for its decisions. Simulation and experimental results are provided to validate the novel controller. This paper additionally develops an adaptive controller for estimating unknown constant winds on these tethered drone systems using a proportional controller. The simulation results demonstrate the effectiveness of the proposed adaptive control scheme in addressing the effect of wind on a tethered drone.","PeriodicalId":504274,"journal":{"name":"Complex Engineering Systems","volume":"92 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140371023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Command filter-based adaptive neural tracking control of nonlinear systems with multiple actuator constraints and disturbances 基于指令滤波器的多执行器约束和干扰非线性系统自适应神经跟踪控制

Complex Engineering Systems Pub Date : 2024-02-21 DOI: 10.20517/ces.2023.38

Yinguang Li, Jianhua Zhang, Yang Li

{"title":"Command filter-based adaptive neural tracking control of nonlinear systems with multiple actuator constraints and disturbances","authors":"Yinguang Li, Jianhua Zhang, Yang Li","doi":"10.20517/ces.2023.38","DOIUrl":"https://doi.org/10.20517/ces.2023.38","url":null,"abstract":"In this paper, the adaptive practical finite-time tracking control problem for a class of strictly feedback nonlinear systems with multiple actuator constraints is investigated using backstepping techniques and practical finite-time stability theory. The effects of deadband and saturated nonlinear constraints on the controller design of nonlinear systems are addressed by the equivalent transformation method. The problem of complexity explosion due to the derivatives of virtual control signals is solved by using the virtual control signals as inputs to the command filters and using the outputs of the command filters to perform the corresponding control tasks. An adaptive neural network tracking backstepping control strategy based on the command filter technique and the backstepping design algorithm is proposed by approximating an unknown nonlinear function using a neural network. The control strategy ensures the boundedness of all variables in the closed-loop system, and the output tracking error fluctuates in a small region near the origin. Finally, simulations verify the effectiveness of the control strategy designed in this paper.","PeriodicalId":504274,"journal":{"name":"Complex Engineering Systems","volume":"25 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140443964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Continual online learning-based optimal tracking control of nonlinear strict-feedback systems: application to unmanned aerial vehicles 基于持续在线学习的非线性严格反馈系统优化跟踪控制：应用于无人驾驶飞行器

Complex Engineering Systems Pub Date : 2024-02-20 DOI: 10.20517/ces.2023.35

I. Ganie, S. Jagannathan

引用次数: 0

Fuzzy inference system-assisted human-aware navigation framework based on enhanced potential field 基于增强势场的模糊推理系统辅助人类感知导航框架

Complex Engineering Systems Pub Date : 2024-01-13 DOI: 10.20517/ces.2023.34

Shurendher Kumar Sampathkumar, Daegyun Choi, Donghoon Kim

引用次数: 0

Integrated control of anti-lock and regenerative braking for in-wheel-motor-driven electric vehicles 轮内电机驱动电动汽车的防抱死和再生制动集成控制

Complex Engineering Systems Pub Date : 2024-01-12 DOI: 10.20517/ces.2023.36

Jiawang Yong, Yiyao Dong, Zhilin Zhang, Nenglian Feng, Wanting Li

{"title":"Integrated control of anti-lock and regenerative braking for in-wheel-motor-driven electric vehicles","authors":"Jiawang Yong, Yiyao Dong, Zhilin Zhang, Nenglian Feng, Wanting Li","doi":"10.20517/ces.2023.36","DOIUrl":"https://doi.org/10.20517/ces.2023.36","url":null,"abstract":"The in-wheel motor is increasingly used in electric vehicles due to the significantly improved controllability, response capability, and energy recovery efficiency based on this technology. However, the independent control of in-wheel motors will lead to braking torque distribution problems, especially in a situation where anti-lock braking systems (ABS) are triggered, which may cause the braking energy to be unrecoverable without the coordinated control between anti-lock and RB for two in-wheel motor-driven electric vehicles based on the RB efficiency map. Control-oriented wheel dynamics and slip ratio models of the system are generated. A sliding mode intervention of regenerative braking (RB) control. This paper presents an integrated algorithm to realize the control-based ABS controller is designed to prevent the wheels from locking and to maintain the slip ratio within a desired level, and the stability and robustness of the controller to uncertainties and disturbances are discussed. Moreover, the braking strength of the driver is calculated and divided into different modes to derive a dynamic braking torque distribution to improve the energy recovery efficiency. The hardware-in-the-loop simulation results show that the recovered energy of the proposed strategy under ABS-triggered maneuver is increased by 52.9% than that of the Proportional, Integral, and Derivative controller and can effectively improve the braking performance and stability.","PeriodicalId":504274,"journal":{"name":"Complex Engineering Systems","volume":"48 19","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139533203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adaptive neural control for delayed discrete-time switched systems under deception attacks 欺骗攻击下延迟离散时间开关系统的自适应神经控制

Complex Engineering Systems Pub Date : 2024-01-01 DOI: 10.20517/ces.2023.29

Dongke Zhao, Yongjie Tian, Ning Zhao

引用次数: 0

Non-fragile state estimation for reaction-diffusion genetic regulatory networks with mode-dependent timevarying delays 具有模式依赖时变延迟的反应扩散遗传调控网络的非脆弱状态估计

Complex Engineering Systems Pub Date : 2023-12-21 DOI: 10.20517/ces.2023.32

Jiarui Liu, Shuai Song, Yulong Song, Xiaona Song

引用次数: 0